# WEKA Articles In-depth articles on data infrastructure, AI/ML storage, and high-performance computing. ## [OpenAI Cut Inference Costs in Half. Here's What That Actually Means.](/article/openai-cut-inference-costs-in-half-here-s-what-that-actually-means) **Published:** August 12, 2026 **Author:** Val Bercovici Explore how OpenAI’s cost breakthrough highlights KV caching and memory architecture as the real levers for scalable, efficient AI inference. Explain why KV cache and memory, not GPUs, drive inference economics. Show how persistent cache infrastructure prevents costly recomputation at scale. Position WEKA Augmented Memory Grid as a path to higher token throughput. ## [Investors Stopped Counting GPUs. Here's What Counts Right Now.](/article/investors-stopped-counting-gpus-here-s-what-counts-right-now) **Published:** August 4, 2026 **Author:** WEKA Discover how investor Dave Easton judges real AI value using simple, ROI-driven metrics in this episode of Deep Geeks. Focus ROI on customer happiness and team efficiency, not GPU counts. Measure intelligence per kilowatt hour to track compute effectiveness. Align culture and mission to withstand hard times and inspire teams. ## [Your AI Stack Is Hitting a Wall and Most Teams Aren’t Ready](/article/your-ai-stack-is-hitting-a-wall-and-most-teams-arent-ready) **Published:** July 27, 2026 **Author:** WEKA Confront AI’s memory wall and redesign your stack for efficient, scalable inference performance. Expose how storage and the IO blender silently throttle GPU utilization. Contrast training and inference to define distinct infrastructure and latency tiers. Emphasize memory efficiency as the only lever amid hardware constraints. ## [The Inference Economy Is Here. Your Infrastructure Wasn't Built for It.](/article/inference-economy-ai-factory-infrastructure) **Published:** July 21, 2026 **Author:** Liran Zvibel Explore why inference at scale breaks infrastructure built for the training era. WEKA is building a new category of data and memory infrastructure purpose-built for the inference era. Together, WEKApod and NeuralMesh 6 unlock efficient, scalable production AI. Maximize rack, power, and GPU utilization for inference workloads. Consolidate file and object storage to reduce data copies and AI costs. Gain predictable, controlled hardware supply and multi-tenant operations at scale. ## [When Everything Is Scarce, Density Is the Only Lever You Have](/article/when-everything-is-scarce-density-is-the-only-lever-you-have) **Published:** July 21, 2026 **Author:** Phil Curran Discover how purpose-built WEKApod hardware and WEKA NeuralMesh software push AI storage density beyond conventional chassis limits. Maximize NVMe density and bandwidth within fixed rack, power, and cooling envelopes. Combine hardware-software co-design for thermal resilience and graceful, non-disruptive degradation. Unlock exabyte-scale capacity and higher GPU utilization from existing datacenter infrastructure. ## [Feature Focus: Replication Reimagined for AI](/article/reimagining-replication-smarter-faster-data-movement-built-for-ai) **Published:** July 21, 2026 **Author:** Betsy Chernoff NeuralMesh Replication gives AI teams a flexible way to make data available across sites, clouds, and GPU environments without copying everything everywhere. Make remote data useful sooner with namespace-first visibility. Start workloads before full copies complete. Apply on-demand, selective, or full hydration by filesystem. Simplify operations with direct cluster-to-cluster replication. ## [Feature Focus: Data Reduction Without the Performance Tax ](/article/data-reduction-capacity-and-performance) **Published:** July 21, 2026 **Author:** Mike Beaumont Optimize AI storage by boosting effective capacity without sacrificing write performance or GPU utilization. Capture similarity-based savings traditional deduplication and compression engines miss. Maintain near-native write throughput with background-first, async data reduction. Cut NVMe capacity, power, and rack costs with a guaranteed DRR for both capacity and performance. ## [Feature Focus: S3 Object Storage That Never Makes GPUs Wait](/article/neuralmesh-fast-s3-object-store) **Published:** July 21, 2026 **Author:** Phil Curran Fix AI storage bottlenecks by replacing legacy S3 architectures with NeuralMesh’s high-concurrency, unified storage system: Hold performance under the concurrency GPU clusters generate, instead of queuing and degrading. Run S3 and POSIX natively on the same data, eliminating copies between stages in an AI pipeline. Get up to 4x greater rack-scale throughput on WEKApod systems, plus S3 engineering built for performance, data, security, and multitenancy. ## [The Capability Stack Behind Exabyte-Scale AI](/article/the-capability-stack-behind-exabyte-scale-ai) **Published:** July 21, 2026 **Author:** Phil Curran Discover how WEKApod and NeuralMesh deliver exabyte-scale AI storage without trading off performance or economics. Unite POSIX and S3 access on a single high-performance NVMe object store. Combine TLC and QLC flash to balance performance with large-scale capacity costs. Apply always-on data reduction and intelligent replication to optimize exabyte-scale AI workloads. ## [Cut the Middle, Keep The Mess](/article/cut-the-middle-keep-the-mess) **Published:** July 16, 2026 **Author:** WEKA Rethink the power of middle management in AI-driven orgs, and see why memory, cost, and oversight matter in this episode of DeepGeeks. See how removing managers makes AI agents costlier and less accurate. Understand why memory scarcity, not models, limits AI agent swarms. Get a new take on why management skills as essential to controlling AI costs. ## [Why Capacity Planning Is an Unsung Hero of Enterprise AI Deployment](/article/why-capacity-planning-is-an-unsung-hero-of-enterprise-ai-deployment) **Published:** July 13, 2026 **Author:** WEKA Explore how strategic AI capacity planning closes the gap between fast-evolving models and slow hardware cycles. Define data-driven metrics to right-size GPU investments and avoid overbuying. Use elastic buffers and dynamic quotas to maximize existing GPU utilization. Align regional capacity with user behavior, regulations, and business priorities. ## [Inference Margins Are a Trap](/article/inference-margins-are-a-trap) **Published:** July 8, 2026 **Author:** Betsy Chernoff Reframe AI inference margins by treating memory as an architectural constraint, not a GPU procurement problem. See how memory shortages and costs crush inference economics. Learn why KV cache optimization unlocks massive effective GPU capacity. Understand how software-defined memory turns constraints into durable margins. ## [Gigawatt AI...Zero Waste?](/article/gigawatt-ai-zero-waste) **Published:** July 6, 2026 **Author:** Val Bercovici On DeepGeeks, Nebius’s Head of Sustainability reveals the full-stack efficiency playbook behind gigawatt-scale AI infrastructure. Custom servers draw 20% less power and run at 45°C, enabling closed-loop liquid cooling with zero water intake Software orchestration eliminates GPU idle waste, cuts buffer requirements by 75%, and extends cluster stability from ~10 hours to 56+ hours Heat recovery from Nebius's Finland campus already heats 2,500 homes and avoided 4,000 tonnes of CO2 emissions in 2025 ## [The Shift from Training Runs to AI Factories Is Already Happening](/article/the-shift-from-training-runs-to-ai-factories-is-already-happening) **Published:** June 30, 2026 **Author:** Nilesh Patel Explore how enterprises are evolving from single training clusters to full AI factories that unify training, fine-tuning, and inference on shared, data-centric infrastructure. Build AI factories that run training, fine-tuning, and inference concurrently. Treat curated, high-throughput data platforms as core competitive infrastructure. Design for reasoning workloads, low latency, resilience, and hybrid deployment. ## [Your PostgreSQL Is Only As Fast As Its Storage](/article/neuralmesh-postgresql-storage-benchmark) **Published:** June 25, 2026 **Author:** Mike Bookham Benchmark PostgreSQL on EKS by comparing NeuralMesh with gp3 EBS under realistic, storage-bound workloads. See how storage latency caps PostgreSQL throughput at scale. Compare TPS and latency across identical Postgres setups on both backends. Reproduce the full Terraform and pgbench benchmark in under an hour. ## [The Bottleneck in Cancer Research Isn't the Algorithm](/article/cancer-research-bottleneck-isnt-the-algorithm) **Published:** June 24, 2026 **Author:** WEKA Explore why AI infrastructure — not algorithms — determines whether cancer research breakthroughs reach patients in time, in this episode of DeepGeeks. Expose why storage throughput — not compute — is the hidden bottleneck slowing medical AI from the lab to the clinic. Unpack the capacity planning framework Jess Audette uses to run one of the most demanding AI research environments in the world. Reframe infrastructure investment as a life-or-death variable — not a cost center. ## [No Memory, No Margin](/article/why-gpu-memory-scarcity-and-kv-cache-eviction-are-undermining-agentic-ai-economics-in-2026) **Published:** June 18, 2026 **Author:** WEKA See how GPU memory limits and fragile KV caching quietly wreck agentic AI costs and quality. Quantify how cache misses turn trillion-token workloads into unsustainable spend. Contrast logical vs. physical KV cache hit rates in real multi-agent systems. Outline practical steps to instrument, architect, and buy for higher cache efficiency. ## [Manufacturing AI Has a Storage Problem. Here's What It's Costing You.](/article/storage-built-for-manufacturing-ai-feed-every-gpu-catch-every-defect-keep-production-moving) **Published:** June 16, 2026 **Author:** Steven Miller Data storage is impacting your manufacturing yield and revenue. GPUs power defect detection, simulation, robotics, and digital twins, so delays are unacceptable Legacy storage was built for HPC workflows, but faster, concurrent AI workloads can overwhelm them NeuralMesh is built for faster checkpointing, automated tiering, less downtime, less rework, lower costs ## [Watt's Really Holding AI Back](/article/can-ai-survive-its-own-energy-appetite-or) **Published:** June 9, 2026 **Author:** WEKA Confront AI’s soaring energy demands and see why efficiency now defines competitive infrastructure in this episode of DeekGeeks. Expose how next-gen GPUs and agents drive a real power crisis. Unpack why software orchestration and AI-tailored storage unlock massive efficiency gains. Reframe waste heat and tokens-per-watt as core business metrics. ## [Token Demand Went Exponential. Your Infrastructure Didn’t Get the Memo.](/article/token-demand-went-exponential-your-infrastructure-didn-t-get-the-memo) **Published:** June 8, 2026 **Author:** WEKA AI infrastructure has a memory problem, and it’s costing the industry billions. Token demand is exploding while memory supply chains flatline. The companies that close this gap first — by warehousing tokens and routing models intelligently — will win the next phase of enterprise AI. ## [For WEKA and NVIDIA, Securing Agentic AI Starts at the Data Layer](/article/for-weka-and-nvidia-securing-agentic-ai-starts-at-the-data-layer) **Published:** June 1, 2026 **Author:** Betsy Chernoff WEKA and NVIDIA are moving AI security into the data path, where agentic AI actually operates NeuralMesh™ with WEKA Augmented Memory Grid™ makes stateful inference memory persistent, fast, and governable NVIDIA Vera BlueField-4 STX and NVIDIA DOCA make the infrastructure around that memory enforceable in silicon Together, they create a secure foundation for agentic AI: persistent inference memory from WEKA, real-time data-path policy enforcement from NVIDIA, and defense in depth for the AI factory ## [Built With AI Clouds: Multitenancy That Scales and Economics That Finally Work](/article/built-with-ai-clouds-multitenancy-that-scales-and-economics-that-finally-work) **Published:** May 7, 2026 **Author:** Phil Curran Optimize shared AI infrastructure economics with NeuralMesh multitenancy, combining strong tenant isolation with elastic resource sharing. Eliminate idle capacity through dynamic, per-tenant resource sharing. Serve diverse tenants with both physical and logical isolation on one platform. Enforce network-level isolation and QoS to prevent noisy neighbors. ## [KV Cache Is the Bottleneck In AI Inference. Here's What Comes Next.](/article/inference-has-a-memory-problem-what-comes-next) **Published:** April 20, 2026 **Author:** WEKA GPU memory is AI’s scarcest bottleneck — and the path forward isn’t more hardware. Here’s what you need to know: HBM supply is structurally constrained for years, driving up costs and forcing organizations to rethink their AI infrastructure strategies NeuralMesh augments GPU memory with pooled flash storage at latencies the GPU can’t distinguish from HBM — at a fraction of the cost As AI shifts from training to inference at scale, software-defined memory infrastructure is critical ## [Your Kubernetes Workloads Aren’t CPU Bound — They’re Waiting on Storage](/article/your-kubernetes-workloads-aren-t-cpu-bound-they-re-waiting-on-storage) **Published:** April 9, 2026 **Author:** Mike Bookham Discover why Kubernetes performance often stalls on storage, not CPU, and how NeuralMesh restores linear scaling. Identify clear signals your workloads are I/O bound, not compute bound. Understand why traditional SAN, NAS, and object storage architectures bottleneck Kubernetes. See how WEKA NeuralMesh scales storage throughput alongside Kubernetes compute. ## [How KV Cache and the AI Memory Wall Are Reshaping Infrastructure Strategy](/article/how-ai-s-memory-wall-is-reshaping-infrastructure-strategy-beyond-gpus) **Published:** April 7, 2026 **Author:** WEKA Learn why AI’s real bottleneck is memory, not GPUs, and how KV cache optimization transforms costs. Quantify how KV cache limits waste GPU capacity and money. Learn how software-based cache optimization delivering up to 4.2x faster inference. Get strategies for surviving rising HBM costs and memory shortages. ## [AI Workflows in Financial Services: How Storage Makes or Breaks Your Models](/article/ai-workflows-in-financial-services-how-storage-makes-or-breaks-your-models) **Published:** March 30, 2026 **Author:** Steven Miller Optimize AI-driven trading by fixing storage bottlenecks that waste GPUs and erode profitability. Accelerate quantitative research with high-performance, low-latency storage infrastructure. Maximize GPU and CPU utilization by eliminating data access bottlenecks. Increase profitability by identifying trading strategies faster than competitors. ## [Why Your AI Training Infrastructure Won't Handle KV Cache at Inference Scale](/article/why-the-infrastructure-that-built-your-ai-wont-run-it) **Published:** March 24, 2026 **Author:** WEKA - AI inference is overtaking training as the dominant GPU workload — and it demands a completely different infrastructure approach - KV cache management is the key lever for controlling inference costs and latency at scale - Specialized “subscaler” cloud providers are outcompeting hyperscalers for GPU workloads - Self-healing infrastructure — not GPU specs — is becoming the real competitive differentiator ## [What AI Infrastructure Actually Costs (And Why Most Teams Get It Wrong)](/article/what-ai-infrastructure-actually-costs-and-why-most-teams-get-it-wrong) **Published:** March 18, 2026 **Author:** WEKA Hidden costs — engineering time, power and cooling, opportunity cost — are just as significant as GPUs and tokens, and far less visible. No single metric captures AI performance. The winning approach: measure everything, triangulate, and stay skeptical of any one number. Flexibility isn't a nice-to-have. With new chip releases every six months, multi-tier strategies and multi-cloud options are survival basics. ## [NeuralMesh AI Data Platform: Built to Operationalize AI Factories at Enterprise Scale](/article/neuralmesh-ai-data-platform-built-to-operationalize-ai-factories-at-enterprise-scale) **Published:** March 17, 2026 **Author:** Betsy Chernoff Discover how NeuralMesh AIDP turns NVIDIA AI Factory blueprints into reliable, production-scale data infrastructure. Operationalize always-current AI data pipelines across changing enterprise sources. Standardize ingestion, transformation, vectorization, and retrieval on a unified platform. Deploy validated, NVIDIA-aligned hardware and software as an integrated system. ## [NeuralMesh Observe: Visibility and Control for Your WEKA Environment](/article/neuralmesh-observe-visibility-and-control-for-your-weka-environment) **Published:** March 12, 2026 **Author:** Phil Curran Discover how NeuralMesh Observe delivers clear visibility, performance validation, and proactive control across your entire WEKA environment. Gain unified dashboards for multi-cluster health, performance, and capacity. Diagnose issues faster with hover-synced metrics and client-level diagnostics. Reduce downtime using smart, configurable alerts and full inventory views. ## [Demystifying the BlueField-4 & Inference Context Memory Storage Announcement](/article/demystifying-the-bluefield-4-and-inference-context-memory-storage-announcement) **Published:** February 24, 2026 **Author:** Callan Fox Explore how NVIDIA ICMS, BlueField-4, and WEKA’s Augmented Memory Grid reshape context memory for scalable AI inference. Explain why KV cache is now shared, first-class inference infrastructure. Detail how ICMS and BlueField-4 extend context memory speed and capacity. Show how WEKA operationalizes shared context memory on today’s infrastructure. ## [KV Cache Costs Are Breaking AI Unit Economics — Here's the Fix ](/article/fixing-the-unit-economics-of-ai-is-really-a-memory-problem) **Published:** February 20, 2026 **Author:** Val Bercovici Expose why today’s cheap AI is unsustainable and how fixing memory unlocks real pricing. Explain how subsidies and rate limits distort AI token costs. Reveal how the memory wall drives throttling and complex pricing. Present WEKA’s Augmented Memory Grid as a scalable, cost-stabilizing solution. ## [NVIDIA Is Defining the Future of Shared KV Cache—WEKA Provides the Adoption Roadmap](/article/nvidia-is-defining-the-future-of-shared-kv-cache-weka-provides-the-adoption-roadmap) **Published:** February 11, 2026 **Author:** Betsy Chernoff NVIDIA's Inference Context Memory Storage platform (ICMS) defines shared KV cache as foundational inference infrastructure. WEKA provides the pragmatic adoption roadmap—delivering immediate gains on existing systems, scaling to pooled architectures, and enabling a smooth transition to ICMS-native deployments. ## [Breaking the MSA Storage Bottleneck to Accelerate AlphaFold](/article/breaking-the-msa-storage-bottleneck-to-accelerate-alphafold) **Published:** February 5, 2026 **Author:** Carmel Schwartz Accelerate AlphaFold by removing MSA storage bottlenecks so pipelines scale with compute resources. Explain why MSA stages are dominated by storage I/O, not compute. Show how WEKA NeuralMesh sustains high concurrent MSA throughput. Enable predictable, high-throughput AlphaFold for multi-user research environments. ## [NVIDIA Signals an Infrastructure Shift for Inference Systems at Scale](/article/nvidia-signals-an-infrastructure-shift-for-inference-systems-at-scale) **Published:** February 3, 2026 **Author:** Boni Bruno NVIDIA's Inference Context Memory Storage Platform (ICMS) announcement confirms that AI inference is now context-bound: managing KV cache across memory and systems matters more than GPU FLOPS. - NVIDIA's ICMS platform treats context as shared infrastructure, formalizing KV cache as a distinct memory layer in the inference stack - Local NVMe configurations break down under sustained inference load due to endurance, thermal limits, and fault isolation challenges - WEKA enables incremental adoption: extend KV cache beyond GPU HBM today, scale to pooled architectures, and arrive ready for ICMS-native deployments ## [AI Factories and the Enterprise Acceleration They Make Possible](/article/ai-factories-and-the-enterprise-acceleration-they-make-possible) **Published:** January 27, 2026 **Author:** Betsy Chernoff AI infrastructure is shifting toward AI factories—integrated systems built to produce intelligence (tokens) at enterprise scale. AI factories unify data pipelines, training, fine-tuning, and inference into one coordinated platform They enable faster deployment, elastic scaling, and continuous improvement via feedback loops The result is AI that becomes a shared enterprise capability, not a scarce resource limited to a few teams ## [The Memory Shortage Exposes Broken Architecture – Here’s How to Fix It](/article/the-memory-shortage-exposes-broken-architecture-here-s-how-to-fix-it) **Published:** January 22, 2026 **Author:** Phil Curran - The memory shortage exposed a broken AI storage architecture. Most GPU clusters waste 50-70% of capacity because storage can't feed GPUs fast enough and GPU memory runs out during inference. - Two bottlenecks, same root cause. Storage starves training workflows. GPU memory exhaustion forces inference to recompute tokens. Both stem from treating storage and memory as separate tiers. - Fix the architecture and triple output with the same hardware. Deploy software-defined storage co-located with GPU servers. Extend high-bandwidth memory to flash. Leverage intelligent tiering. ## [Reducing AI Project Abandonment Means Rethinking AI Infrastructure](/article/reducing-ai-project-abandonment-means-rethinking-ai-infrastructure) **Published:** January 14, 2026 **Author:** Val Bercovici AI project abandonment isn't an AI problem. It's an infrastructure problem. By breaking the memory wall, organizations can cut inference energy use by an order of magnitude, reallocate scarce GPU and memory resources more efficiently, and run continuous, large-scale agent workloads 24/7 — without adding hardware or increasing energy consumption. ## [The Context Era Has Begun](/article/the-context-era-has-begun) **Published:** January 5, 2026 **Author:** Jim Sherhart AI inference is shifting from a compute problem to a context-first infrastructure challenge, redefining how platforms handle KV cache and memory. Explain why KV cache and context memory now bottleneck agentic, multi-turn AI systems. Contrast traditional enterprise storage with purpose-built inference context memory requirements. Highlight how NVIDIA’s platform and WEKA’s Augmented Memory Grid operationalize fast, reusable context at scale. ## [Why Storage Architecture is the New Bottleneck for HPC and AI Teams ](/article/why-storage-architecture-is-the-new-bottleneck-for-hpc-and-ai-teams) **Published:** December 22, 2025 **Author:** Office of the CTO Scaling GPUs without rethinking storage leaves AI and HPC teams bottlenecked by data access, not compute. Learn how legacy storage starves GPUs and slows AI and HPC workloads. Discover how NeuralMesh™ cuts training time, boosts throughput, and expands research capacity. Dig into real-world success stories in genomics, live entertainment, and semiconductor manufacturing analytics to see how organizations are scaling and maximizing GPU utilization. ## [Democratizing AI Inference: The Future of AI Should Take Minutes, Not Weeks](/article/democratizing-ai-inference-the-future-of-ai-should-take-minutes-not-weeks) **Published:** December 16, 2025 **Author:** Betsy Chernoff Reimagine AI inference by replacing GPU-bound memory bottlenecks with scalable, persistent KV cache infrastructure. Unlock long-context, multi-user workloads without massive bespoke GPU clusters. Boost TTFT and token throughput using NVMe as GPU-addressable memory. Simplify deployment via NeuralMesh, Axon, open source, and OCI integrations. ## [Building AI Factories: Storage Architecture Defines Success](/article/building-ai-factories-storage-architecture-defines-success) **Published:** December 9, 2025 **Author:** Office of the CTO Exploding AI data volumes make storage architecture the critical limiter—or accelerator—of scalable, economical AI factories. Stop choosing between performance and capacity — the TLC vs. QLC trade-off is breaking your AI factory economics before you scale Eliminate GPU idle time with a unified flash architecture that handles massive checkpoint writes and metadata operations without throttling ## [From Prompts to Agent Swarms: Three Considerations for Enterprise AI](/article/from-prompts-to-agent-swarms-three-considerations-for-enterprise-ai) **Published:** December 4, 2025 **Author:** WEKA Explore how agentic AI, regulation, and token economics are reshaping enterprise AI strategies and infrastructure requirements. Learn how orchestrated agent swarms will be used to tackle complex, multi-step enterprise workflows. Get a sense for how regulatory timing might impact AI adoption and innovation across regions. Understand token economics tradeoffs across accuracy, latency, and cost for scalable AI agents. ## [Next Generation WEKApod Shatters AI Storage Economics](/article/next-generation-wekapod-shatters-ai-storage-economics) **Published:** November 18, 2025 **Author:** Phil Curran Transform AI storage economics with new WEKApod appliances that deliver extreme performance, density, and efficiency without complexity. Deliver 65% better price-performance and 4.6x higher capacity density. Cut power use per TB by 68% while sustaining AI performance. Simplify NeuralMesh deployment with turnkey, scalable, non-proprietary appliances. ## [NeuralMesh Delivers 1000x GPU Memory for AI Inference on Oracle Cloud](/article/neuralmesh-delivers-1000x-gpu-memory-for-ai-inference-on-oracle-cloud) **Published:** November 18, 2025 **Author:** Betsy Chernoff NeuralMesh with Augmented Memory Grid on Oracle Cloud Infrastructure extends GPU memory to enable faster, more efficient large-scale AI inference. Deliver up to 1000x more KV cache capacity at memory-like speed. Achieve up to 20x faster time-to-first-token for long-context workloads. Stream KV cache directly between NVMe and GPU HBM, bypassing CPU and DRAM bottlenecks. ## [WEKA Joins Guardant Health to Advance Open Standards in Life Sciences](/article/weka-joins-guardant-health-to-advance-open-standards-in-life-sciences) **Published:** November 13, 2025 **Author:** Product Team WEKA and Guardant Health collaborate on an open data standard to unlock exabyte-scale life sciences research and accelerate cancer-fighting innovation. Learn how the Single Namespace Standard removes vendor lock-in and stranded data silos. Learn about the cross-industry collaboration happening among competing vendors to create an open, interoperable standard. Discover how SNS speeds research by enabling fast, unified access to massive, long-retained datasets. ## [AI Storage is the New Battleground for Inference at Scale](/article/ai-storage-is-the-new-battleground-for-inference-at-scale) **Published:** November 5, 2025 **Author:** Office of the CTO AI inference now dominates AI workloads, making ultra-low-latency, scalable storage a critical competitive advantage for production deployments. This article shares how AI storage can help you: Prioritize ultra-low-latency, high-IOPS storage to prevent inference bottlenecks. Architect scalable, fault-tolerant storage that handles unpredictable, always-on AI demand. Leverage RAG and distributed KV cache to optimize inference performance. ## [The Business Value of Flexibility: Driving AI ROI with Smart Infrastructure](/article/the-business-value-of-flexibility-driving-ai-roi-with-smart-infrastructure) **Published:** November 5, 2025 **Author:** Office of the CTO Explore how flexible AI infrastructure boosts utilization, cuts costs, and turns AI investments into real business value. Maximize GPU utilization and reduce cost per token with adaptive infrastructure. Align technical trade-offs with ROI through modular, software-defined architectures. Accelerate innovation and customer experiences by scaling AI without constant redesigns. ## [Building Gigascale AI Factories with NVIDIA BlueField-4 and WEKA NeuralMesh](/article/building-gigascale-ai-factories-with-nvidia-bluefield-4-and-weka-neuralmesh) **Published:** October 28, 2025 **Author:** Jim Sherhart Discover how WEKA and NVIDIA BlueField-4 reshape AI factories with DPU-accelerated storage, higher efficiency, and secure, scalable data pipelines. Accelerate AI pipelines with DPU-offloaded storage, networking, and security services. Increase GPU utilization and tokens-per-watt by eliminating CPU bottlenecks. Strengthen zero-trust isolation with inline, programmable DOCA microservices. ## [Agents at Scale: Escaping Upside-Down Tokenomics with up to 4.2x and more ](/article/agents-at-scale-escaping-upside-down-tokenomics-with-up-to-4-2x-efficiency) **Published:** October 16, 2025 **Author:** Betsy Chernoff In production tests on CoreWeave, WEKA's Augmented Memory Grid™ running on NeuralMesh™ Axon® successfully demonstrated that inference performance hinges on sustaining KV cache hits and avoiding token recomputation. Once DRAM's cache capacity ceiling was surpassed, Augmented Memory Grid maintained KV cache hit rates, reduced TTFT by up to 6x and delivered up to 4.2x more tokens per GPU—all without additional hardware. This is measured from long-context testing results. Full testing details will be published later this month in a white paper. Once live, we will update this blog with the link to read it. ## [WEKA's Award-Winning Storage for AI Unleashes Performance on Oracle Cloud Infrastructure](/article/wekas-award-winning-storage-for-ai-unleashes-performance-on-oracle-cloud-infrastructure) **Published:** October 13, 2025 **Author:** Phil Curran Discover how WEKA’s NeuralMesh and Axon on OCI remove AI storage bottlenecks and boost GPU performance. Highlight Oracle awards validating NeuralMesh’s impact on AI workloads. Explain how Axon maximizes GPU utilization and scales linearly on OCI. Show benefits for leading AI, robotics, and research organizations. ## [What Is the AI Memory Wall and Why Is It an Existential Threat to Inference Performance?](/article/what-is-the-ai-memory-wall-and-why-is-it-an-existential-threat-to-inference-performance) **Published:** September 30, 2025 **Author:** Val Bercovici Agentic AI deployments are encountering the “memory wall”: GPU memory capacity can’t accommodate enough Key-Value (KV) cache required for extended concurrent parallel agent contexts. Without sufficient KV caching, systems supporting agents must regularly recompute (prefill) input tokens, consuming wasteful energy, adding latency, and even reducing quality for reasoning and output tokens. WEKA Augmented Memory Grid achieves 96-99% KV cache hit rates for agentic workloads through its token warehousing architecture, without adding latency or reducing throughput. ## [Multi-Agent AI: Why Data Movement Matters More Than Just Compute and Memory](/article/multi-agent-ai-why-data-movement-matters-more-than-just-compute-and-memory) **Published:** September 25, 2025 **Author:** Boni Bruno Stop starving your multi-agent AI of data: Learn why fast data movement, not just GPUs and memory, determines whether agentic systems scale in production. Expose why multi-agent workloads explode data movement as agents scale. Explain how data bottlenecks idle GPUs and inflate infrastructure costs. Define the role of a high-performance data mesh in agent orchestration. Highlight how NeuralMesh keeps GPUs utilized by accelerating KV cache and state access. Share NVIDIA-coordinated results validating NeuralMesh performance for wide-agent systems. ## [Midstream Flexibility: Retrofitting AI Infrastructure without Starting Over](/article/midstream-flexibility-retrofitting-ai-infrastructure-without-starting-over) **Published:** September 16, 2025 **Author:** Kevin Tubbs When I talk to customers, I hear the same thing again and again: “Our cluster was fine for training, but everything broke in production.” At scale, inference is even more demanding than training—and rigid infrastructure can’t keep up. The good news is you don’t need to start over to get the performance you need. With the right upgrades and software-defined layers, you can retrofit what you already have. That’s exactly what NeuralMesh™ by WEKA® is built for: helping you pivot from training to inference, scale seamlessly, and stay competitive without ripping and replacing what you’ve already built. ## [From Project to Product: Aligning Technical and Business Goals in AI Deployment](/article/from-project-to-product-aligning-technical-and-business-goals-in-ai-deployment) **Published:** September 9, 2025 **Author:** Kevin Tubbs Early AI wins come fast—but once you scale, everything changes. Problems you didn't know you had start showing up in force: bottlenecks, fragility, operational pain. I've seen this pattern with customers over and over. What was “good enough” early on breaks under the weight of real-world production. This post is about what happens next—how flexibility in your infrastructure gives you room to adapt, optimize, and keep moving when the pressure's on. ## [Eliminate Infrastructure Sprawl and Accelerate AI on Dell PowerEdge](/article/eliminate-infrastructure-sprawl-and-accelerate-ai-on-dell-poweredge) **Published:** September 2, 2025 **Author:** Nilesh Patel Supercharge your production AI stack with a unified Dell PowerEdge and WEKA’s NeuralMesh Axon platform built for Tier 0 performance. Collapse compute and Tier 0 storage into a single high-density AI system. Boost GPU utilization by up to 80% by eliminating data and I/O bottlenecks. Cut hardware, power, and cooling needs with zero-footprint, software-defined storage. Accelerate deployment from rack to production with simplified, scalable infrastructure. Extend effective GPU memory using Augmented Memory Grid for larger, faster inference workloads. ## [Flexibility by Design: Avoiding the Quiet Trap of AI Infrastructure Lock-In](/article/flexibility-by-design-avoiding-the-quiet-trap-of-ai-infrastructure-lock-in) **Published:** August 29, 2025 **Author:** Kevin Tubbs Early AI wins are often built on speed—but scaling them takes a different kind of thinking. Teams feel the friction as soon as it hits, but they don’t always know how to name it. In this post, I break down the patterns I see over and over: infrastructure decisions made for speed that become roadblocks at scale. The fix isn’t starting over—it’s designing for flexibility, so you can move fast early and adapt when business and technical demands shift. ## [Beyond Resilience: How NeuralMesh Defends Your Data from Bitflips, Failures, and the Unexpected](/article/beyond-resilience-how-neuralmesh-defends-your-data-from-bitflips-failures-and-the-unexpected) **Published:** July 30, 2025 **Author:** Colin Gallagher NeuralMesh doesn’t just help you bounce back from failure—it ensures that what you bounce back to is accurate, intact, and trustworthy. It’s a full-stack, AI-native approach to data integrity: Checksums on every block Erasure coding-style distributed protection Write-ahead journaling Automated scrubbing and error correction Hardware-aware safeguards Default-on safety mechanisms Resiliency helps you recover. Integrity ensures what you recover is right. NeuralMesh gives you both—because in AI infrastructure, science, and critical enterprise computing, performance is just table stakes. Trustworthy data is everything. ## [NeuralMesh's Snap-to-Object: More Than Just a Snapshot](/article/neuralmesh-snap-to-object-more-than-just-a-snapshot) **Published:** July 28, 2025 **Author:** Product Marketing Team Go beyond basic snapshots with Snap-to-Object, a powerful option for teams protecting AI-scale data across locations. Export full, consistent filesystem snapshots directly to on-prem or cloud object storage. Reduce ongoing protection costs with incremental updates and low-cost object tiers. Strengthen ransomware and insider threat defense with air-gapped, immutable object copies. Enable cross-site disaster recovery by restoring snapshots to any NeuralMesh cluster. Maintain strong security with integrated encryption and key management for exported data. ## [NeuralMesh Doesn’t Just Save Space, It Maximizes Efficiency](/article/neuralmesh-doesnt-just-save-space-it-maximizes-efficiency) **Published:** July 24, 2025 **Author:** Product Marketing Team Stop wasting expensive NVMe capacity. Learn how NeuralMesh quietly maximizes data efficiency at scale. Cut NVMe storage costs by aggressively reducing redundant and repetitive data. Capture savings from similar, not just identical, files using similarity hashing. Maintain full application performance with background, transparent data reduction. Enable filesystem-specific reduction that targets user data while preserving system structures. Consolidate AI, EDA, database, and code workloads onto a single high-performance system. ## [WEKA Accelerates AI Inference with NVIDIA Dynamo and NVIDIA NIXL](/article/weka-accelerates-ai-inference-with-nvidia-dynamo-and-nvidia-nixl) **Published:** July 22, 2025 **Author:** Betsy Chernoff Supercharge GPU-heavy AI inference with WEKA and NVIDIA Dynamo/NIXL to cut latency and boost throughput. Accelerate KV-cache transfers with WEKA’s Augmented Memory Grid at near-memory speeds. Reduce Time to First Token using NIXL’s optimized data movement across GPU and storage. Eliminate CPU bottlenecks with zero-copy RDMA pipelines via GPUDirect Storage. Scale KV-cache capacity elastically using WEKA NeuralMesh’s software-defined, multi-node NVMe aggregation. Benefit from open-source WEKA plugins integrated into the NVIDIA Dynamo and NIXL ecosystem. ## [How Certified Storage is Reshaping the AI Factory Floor](/article/how-certified-storage-is-reshaping-the-ai-factory-floor) **Published:** July 17, 2025 **Author:** Product Marketing Team Supercharge your AI factory by pairing next-gen NVIDIA GPUs with certified storage that actually keeps up. Understand why storage, not GPUs, often bottlenecks modern AI factories. See how WEKA sustains 1 GB/s per GPU at massive scale. Explore certified architectures for GB200 NVL72 and HGX H100/H200/B200 systems. Discover how composable, multitenant storage supports industrial-scale AI workloads. Evaluate Micron 9550 SSD benefits for high-performance, energy-efficient NVMe storage. ## [How NeuralMesh Gets More Resilient as You Scale](/article/how-neuralmesh-gets-more-resilient-as-you-scale) **Published:** July 14, 2025 **Author:** Product Marketing Team See how NeuralMesh turns large clusters into faster-healing, more resilient infrastructure for demanding data environments. Understand how distributed 4K block stripes reduce impact from domain failures. Discover how cluster-wide parity rebuilds accelerate recovery as nodes increase. See why larger clusters shrink vulnerability windows after multiple simultaneous failures. Compare rebuild times between 50-node and 100-node clusters under identical conditions. Recognize how NeuralMesh maintains performance while restoring protection and avoiding data loss. ## [WEKA Powers World Record Pi Calculation with Linus Tech Tips](/article/weka-powers-world-record-pi-calculation-with-linus-tech-tips) **Published:** July 10, 2025 **Author:** Product Marketing Team See how WEKA’s software-defined storage helped Linus Tech Tips shatter pi-calculation records, and what it means for your most demanding data-intensive workloads. ## [NeuralMesh Axon Reinvents AI Infrastructure Economics for the Largest Workloads](/article/neuralmesh-axon-reinvents-ai-infrastructure-economics-for-the-largest-workloads) **Published:** July 8, 2025 **Author:** Ajay Singh Reimagine AI infrastructure economics by embedding high-performance, software-defined storage directly into GPU servers for massive training and inference gains. Boost GPU utilization to over 90%, cutting infrastructure, power, and cooling costs. Extend effective GPU memory from terabytes to petabytes for larger, faster inference. Simplify operations by eliminating external storage systems across on-prem, hybrid, and cloud. ## [Beyond the Buzzword: An Economic Perspective on “Tokenomics”](/article/beyond-the-buzzword-an-economic-perspective-on-tokenomics) **Published:** July 2, 2025 **Author:** Ford Shaper Reframe AI token costs using an economic lens that connects model, context, and infrastructure decisions to real business value. Clarify how model competence, context relevance, and inference efficiency shape token value. Compare throughput with “good-put” to focus on tokens that drive outcomes. Evaluate tradeoffs when upgrading models, context windows, and serving architectures. ## [Cache is Magic—Until It Isn’t](/article/cache-is-magic-until-it-isnt) **Published:** June 30, 2025 **Author:** Product Marketing Team Stop relying on fragile caching tricks and see how NeuralMesh delivers consistent, high-speed performance for demanding AI and data workloads. Understand why cache only hides underlying performance bottlenecks. Recognize how growing, unpredictable datasets quickly break traditional caching. See why cache misses make performance unreliable at scale. Discover how NeuralMesh provides sub-millisecond latency without cache dependence. Evaluate an architecture built for any workload size and access pattern. ## [Introducing the Next Phase of WARRP: Simplifying Scalable Inference for Enterprise AI](/article/introducing-the-next-phase-of-warrp-simplifying-scalable-inference-for-enterprise-ai) **Published:** June 25, 2025 **Author:** Shimon Ben-David Discover how the latest WARRP release streamlines scalable, production-ready RAG inference for enterprise AI. Deploy a full RAG stack in minutes with one-line Wundler automation. Reduce operational overhead with turnkey, repeatable on-prem and cloud deployments. Accelerate inference with NeuralMesh, NVIDIA ecosystem support, and database-native responses. ## [Model Loading that is Faster than Local Node NVMe with NVIDIA Run:ai](/article/model-loading-that-is-faster-than-local-node-nvme-with-nvidia-runai) **Published:** June 23, 2025 **Author:** Shimon Ben-David If you’re running large-scale LLM inference, you know how important slashing model load times is. Learn how NVIDIA Run:ai and NeuralMesh can help teams: Cut model loading times by up to 6x with model streaming. Achieve up to 40% faster loads than local node NVMe storage. Maximize GPU utilization by eliminating idle time during model loading. Scale inferencing across many GPUs without copying models to local NVMe. Handle high-concurrency, metadata-heavy workloads with AI-optimized storage architecture. ## [WEKA Sets A New Bar With 75X Faster Time to First Token (TTFT)](/article/weka-sets-a-new-bar-with-75x-faster-time-to-first-token-ttft) **Published:** June 20, 2025 **Author:** Betsy Chernoff Supercharge long-context LLM inference with WEKA’s Augmented Memory Grid and open source GDS integrations for AI teams. Unlock massive context windows without hitting GPU memory limits. Cut prefill latency by up to 75X for long prompts. Scale LLM workloads efficiently while reducing infrastructure complexity and cost. Adopt open source integrations with LM Cache and NVIDIA TensorRT-LLM. Enable faster, more responsive AI applications for developers and AI leaders. ## [Why a New Approach to Storage is Needed to Power Today’s AI Workloads](/article/why-a-new-approach-to-storage-is-needed-to-power-todays-ai-workloads) **Published:** June 18, 2025 **Author:** Ajay Singh Rethink your AI infrastructure with storage built for microservices, not monolithic bottlenecks. Understand why legacy, appliance-based storage fails modern AI workloads. See how microservices enable modular, elastic, and fault-tolerant storage architectures. Recognize the importance of container-native, portable storage across on-prem and cloud. Align storage operations with the service-oriented way your engineering teams already work. ## [Why Prefill has Become the Bottleneck in Inference—and How Augmented Memory Grid Helps](/article/why-prefill-has-become-the-bottleneck-in-inference-and-how-augmented-memory-grid-helps) **Published:** June 6, 2025 **Author:** Callan Fox Stop letting prefill costs choke your LLM inference. This post shows infrastructure leaders how WEKA Augmented Memory Grid slashes GPU waste with smarter Key Value (KV) cache storage and reuse. Understand why prefill, not decode, now dominates inference cost. See how higher KV cache hit rates cut latency and GPU consumption. Explore prefix matching to safely reuse KV caches across sessions. Compare KV cache sizes and storage needs across popular transformer and MoE models. Discover how disaggregated, NVMe-based caching extends TTL far beyond DRAM limits. ## [Open Sourcing GDS Integration from Augmented Memory Grid—See Results for Yourself](/article/open-sourcing-gds-integration-from-augmented-memory-grid-see-results-for-yourself) **Published:** May 30, 2025 **Author:** Betsy Chernoff See how WEKA’s open-sourced GDS integration boosts AI inference performance for engineers running large-scale GPU workloads. In this post, explore: How GDS connects inference servers to WEKA’s Augmented Memory Grid. Ways to benchmark token throughput, TTFT, and GPU utilization in your own environment. How to evaluate GDS acceleration with vLLM and LMCache-based architectures. Performance under bursty, data-intensive prefill caching workloads. ## [Redefining AI Infrastructure: Powering Intelligent Agents with NVIDIA and WEKA](/article/redefining-ai-infrastructure-powering-intelligent-agents-with-nvidia-and-weka) **Published:** May 19, 2025 **Author:** Product Marketing Team Discover how NVIDIA and WEKA deliver fast, scalable infrastructure for real-time intelligent agents. Power agentic AI with a full-stack NVIDIA data platform and WEKA. Move massive, unstructured data with microsecond latency using NeuralMesh. Deploy reliable, repeatable agent architectures with WEKA WARP across hybrid environments. ## [Unlocking Scalable Inference with WEKA Augmented Memory Grid](/article/unlocking-scalable-inference-with-weka-augmented-memory-grid) **Published:** May 15, 2025 **Author:** Callan Fox Supercharge large-context LLM inference by cutting time-to-first-token (TTFT), boosting cache efficiency, and simplifying operations with WEKA Augmented Memory Grid. Reduce redundant prefills by persisting KV cache in a high-speed token warehouse. Achieve memory-class Warehouse-to-GPU access for massive context windows and large models. Improve cache hit rates to increase output tokens and lower inference costs. Simplify routing and scaling by decoupling KV cache from individual GPU hosts. Deploy flexibly with dedicated or converged token warehouse models across on-prem and cloud. ## [Coding Agents Go Mainstream](/article/coding-agents-go-mainstream) **Published:** May 8, 2025 **Author:** Val Bercovici Discover how next-generation AI coding agents overcome GPU memory limits to deliver faster, more scalable developer experiences. Understand why KV cache-heavy coding agents overwhelm traditional GPU memory. Learn how to reduce time-to-first-token and keep large prompts responsive at scale. Discover ways to simplify GPU scheduling by decoupling KV cache from specific hosts. Walk away knowing how to increase effective token generation while cutting inference infrastructure costs. ## [We Don’t Speak Milliseconds](/article/we-dont-speak-milliseconds) **Published:** May 6, 2025 **Author:** Product Marketing Team Stop wasting GPU budget on millisecond latency—discover how NeuralMesh delivers true microsecond data performance for demanding AI and HPC teams. Eliminate kernel bottlenecks by bypassing the client operating system for data operations. Cut latency with polling-based I/O using SPDK and DPDK instead of interrupts. Maximize throughput by parallelizing reads and writes across drives, threads, and nodes. Accelerate AI pipelines with direct GPU access via Magnum IO GPU Direct Storage. Boost NVMe efficiency through 4K-aligned I/O and distributed microservices-based metadata. ## [The Real State of AI: Hype vs. Reality](/article/the-real-state-of-ai-hype-vs-reality) **Published:** April 30, 2025 **Author:** Val Bercovici Cut through AI infrastructure hype to see why true KV Cache performance now defines real-world LLM success. Contrast GPU memory hierarchy solutions with commodity storage approaches. Understand why memory-class performance is critical for modern KV Cache. See how bottlenecks in storage cripple LLM scale and responsiveness. Explore WEKA’s Augmented Memory Grid benefits for latency and throughput. Recognize why rethinking infrastructure is essential for AI-first organizations. ## [The Magic of WEKA Happens at Scale](/article/the-magic-of-weka-happens-at-scale) **Published:** April 22, 2025 **Author:** Product Marketing Team Stop fighting fragile infrastructure at scale—this post is for teams running data-intensive, fast-growing environments. Understand why scale amplifies chaos, tail latency, and hidden inefficiencies. Recognize how small files and metadata overhead cripple traditional storage. See how NeuralMesh distributes I/O to avoid hotspots and queue depth issues. Discover design principles that reduce fragility and manual tuning at scale. Explore NeuralMesh’s anti-fragile architecture that improves resilience and performance as you grow. ## [Solving Latency Challenges in AI Data Centers](/article/solving-latency-challenges-in-ai-data-centers) **Published:** April 1, 2025 **Author:** Product Marketing Team Stop wasting GPU power on slow storage—this post is for AI infrastructure leaders fixing latency at scale. Understand how latency cripples AI inference, training, and GPU utilization. Identify key bottlenecks in legacy storage and networking architectures. Compare modern high-speed networks with traditional server bus performance. Apply ten concrete strategies to cut storage and network latency. Build scalable, future-ready AI infrastructure with consistent low-latency performance. ## [WEKA’s Augmented Memory Grid—Pioneering a Token Warehouse for the Future](/article/wekas-augmented-memory-grid-pioneering-a-token-warehouse-for-the-future) **Published:** March 27, 2025 **Author:** Betsy Chernoff Transform how your AI factory handles exploding token volumes with a high-speed, persistent token warehouse built for GPUs and NVMe. Cut inference latency by serving cached tokens at near-memory speed. Reduce GPU recomputation and lower FLOPS, power use, and TCO. Extend GPU memory into a distributed, high-performance data fabric. Persist and reuse trillions of tokens as durable, retrievable assets. Unify training and inference by accessing token data without copying. ## [GTC 2025 Hot Takes: 10 Things You Missed If You Weren’t There](/article/gtc-2025-hot-takes-10-things-you-missed-if-you-were-not-there) **Published:** March 26, 2025 **Author:** Product Marketing Team Get up to speed on GTC 2025’s biggest AI infrastructure shifts and what they mean for enterprise teams. Understand why enterprise AI has moved from experiments to large-scale production. Grasp why tokens, not just FLOPs, now define AI performance and cost. See how disaggregated GPU and memory architectures reshape scalable, profitable inference. Recognize power, cooling, and efficiency as hard limits on AI growth. Explore how data platforms and digital twins become core to modern AI factories. ## [Unleash AI Reasoning with NVIDIA Blackwell and NeuralMesh](/article/unleash-ai-reasoning-with-nvidia-blackwell-and-neuralmesh) **Published:** March 18, 2025 **Author:** Product Marketing Team Discover how WEKA-certified storage unlocks NVIDIA Blackwell GPUs for high-performance AI reasoning at massive scale. Eliminate storage bottlenecks to drive 90%+ GPU utilization. Deliver ultra-low-latency, high-throughput data for real-time AI workloads. Scale AI clouds from petabytes to exabytes with efficient multitenancy. ## [New Augmented Memory Grid Revolutionizes the Economics of AI Inference Infrastructure](/article/new-augmented-memory-grid-revolutionizes-the-economics-of-ai-inference-infrastructure) **Published:** March 18, 2025 **Author:** Nilesh Patel Unlock faster AI inference by extending GPU memory with Augmented Memory Grid, built for teams scaling agentic and long-context workloads. Break through GPU memory wall constraints with petabyte-scale persistent KV cache. Cut time to first token for long contexts, dramatically improving user response times. Reduce GPU overprovisioning and balance speed, accuracy, and cost more effectively. Offload KV cache from GPU memory to boost overall inference throughput. Align AI infrastructure choices with evolving AI tokenomics and profitability goals. ## [Advancing Scientific Discovery in High-Performance Compute: NeuralMesh Now Supports AWS ParallelCluster](/article/advancing-scientific-discovery-in-high-performance-compute-neuralmesh-now-supports-aws) **Published:** February 25, 2025 **Author:** Phil Curran Supercharge your HPC research on AWS with faster data pipelines, higher GPU utilization, and simpler cluster management. Accelerate HPC workloads by shrinking epoch times from months to days. Maximize GPU-accelerated infrastructure utilization while controlling infrastructure costs. Run demanding workloads like molecular dynamics and seismic imaging with cloud scalability. Boost storage performance with zero-tuning, zero-copy architecture optimized for millions of small files. Simplify deployment using Terraform templates, Cloud Deployment Manager, and native AWS integrations. ## [AI Tokenomics: More Than Talk – See Results from Our Labs](/article/ai-tokenomics-more-than-talk-see-results-from-our-labs) **Published:** February 12, 2025 **Author:** Maor Ben-Dayan See how WEKA’s AI-optimized storage architecture radically accelerates token processing for teams scaling demanding inference workloads. Cut token prefill times by up to 41x without compression or quantization. Extend effective GPU memory using ultra-fast storage and NVIDIA Magnum IO GPUDirect Storage. Reduce GPU idle time and decouple compute from strict memory limits. Maintain full model accuracy while improving throughput and lowering inference costs. Consolidate training and inference on shared infrastructure for higher utilization and flexibility. ## [Unleashing Breakthrough Innovation: NeuralMesh Now Supports Microsoft Azure CycleCloud](/article/unleashing-breakthrough-innovation-neuralmesh-now-supports-microsoft-azure-cyclecloud) **Published:** February 11, 2025 **Author:** Phil Curran Supercharge your Azure HPC and AI workloads with WEKA’s NeuralMesh™ integration for Azure CycleCloud. Accelerate time-to-insight for genomics, drug discovery, geoscience, and AI workloads. Eliminate data bottlenecks with a high-performance, single-namespace data platform. Boost GPU utilization to over 90% and cut epoch times from weeks to hours. Reduce storage costs through seamless integration with Azure Blob storage. Deploy quickly in your Azure tenant using marketplace listings and Terraform templates. ## [Raising the Bar: WEKA and HPE Achieve Unmatched SPECstorage Performance](/article/raising-the-bar-weka-and-hpe-achieve-unmatched-specstorage-performance) **Published:** February 6, 2025 **Author:** Boni Bruno Discover how NeuralMesh and HPE Alletra 4110 deliver record-breaking storage performance for demanding AI and data-intensive workloads. See independent SPECstorage® benchmarks validating real-world performance across five critical workloads. Understand how ultra-low latency accelerates AI training, EDA, genomics, and video analytics. Consolidate diverse workloads on one high-performance, zero-tuning storage infrastructure. Maximize GPU utilization by eliminating legacy storage bottlenecks and data pipeline slowdowns. Gain a secure, scalable, and efficient platform jointly engineered by WEKA and HPE. ## [A Strategic Step Forward for WEKA: An Open Letter From Our CEO](/article/a-strategic-step-forward-for-weka-an-open-letter-from-our-ceo) **Published:** February 4, 2025 **Author:** Liran Zvibel Discover how WEKA’s CEO is steering the company’s next phase of AI-driven growth for customers, partners, and future employees. Celebrate WEKA’s milestone year reaching unicorn status and $100M+ ARR. Understand how explosive AI market growth shapes WEKA’s long-term strategy. See why WEKA aims to lead the AI data platform market it helped pioneer. Explore how strategic go-to-market changes support customers in a dynamic AI landscape. Consider joining WEKA’s expanding team across all business functions. ## [Bridging the Gap: How NeuralMesh Redefines Networking for AI and HPC Workloads using NVIDIA Spectrum-X](/article/bridging-the-gap-how-neuralmesh-redefines-networking-for-ai-and-hpc-workloads-using-nvidia) **Published:** February 4, 2025 **Author:** Product Marketing Team Supercharge your AI or HPC cluster with Ethernet-based networking that rivals InfiniBand while simplifying large-scale data infrastructure. Compare InfiniBand and Ethernet approaches for demanding AI and HPC workloads. Understand how NeuralMesh™ optimizes storage traffic across both network fabrics. See how NVIDIA Spectrum-X boosts throughput and reduces congestion for AI factories. Discover the role of BlueField-3 SuperNICs and DPUs in accelerating data operations. Explore how WEKA’s roadmap enhances visibility, scalability, and reliability for future AI environments. ## [Three Ways Token Economics Are Redefining Generative AI](/article/three-ways-token-economics-are-redefining-generative-ai) **Published:** January 30, 2025 **Author:** Val Bercovici Discover how new token economics slash generative AI costs and latency for teams scaling real-world applications. Cut token generation expenses with intelligent context caching and SSD-based storage. Boost token throughput by reducing inference latency with GPU-optimized, ultra-low-latency architectures. Scale AI workloads beyond memory limits using high-performance persistent storage as an extra memory tier. Support real-time AI use cases with higher token volumes and fewer compute resources. Future-proof AI infrastructure by optimizing token costs without sacrificing accuracy or performance. ## [Product-Market Fit Is Defining Cool Tech in the AI Era](/article/product-market-fit-is-defining-cool-tech-in-the-ai-era) **Published:** January 22, 2025 **Author:** Liran Zvibel Discover how WEKA achieved true product-market fit in the AI era and what it means for leaders building data-intensive, AI-driven products. Understand why timing and real market need matter more than "cool" tech. See how unified data platforms unlock speed, scale, and efficiency for AI workloads. Compare successful and failed ventures to grasp the impact of product-market fit. Recognize how WEKA’s NeuralMesh™ was battle-tested with demanding AI and HPC customers. Get inspired by how customers use WEKA to tackle massive data challenges. ## [Revolutionizing Research: NeuralMesh IO500 Benchmark Success Powers AI, Genomics, and HPC Innovation](/article/revolutionizing-research-neuralmesh-io500-benchmark-success-powers-ai-genomics-and-hpc) **Published:** January 7, 2025 **Author:** Boni Bruno Discover how NeuralMesh’s IO500-winning storage performance empowers HPC, AI, and genomics teams to do more with less. See how Memorial Sloan Kettering Cancer Center (MSKCC)’s IRIS supercluster uses NeuralMesh to accelerate cancer research. Compare IO500 results showing high performance with dramatically fewer client nodes. Understand NeuralMesh’s metadata advantages for AI/ML, genomics, and large-scale simulations. Explore how efficiency gains reduce hardware, power, and operational complexity. Recognize why NeuralMesh is positioned as a future-proof HPC storage platform. ## [Shaping the AI Future: WEKA's Top IT Predictions for 2025](/article/shaping-the-ai-future-wekas-top-it-predictions-for-2025) **Published:** December 20, 2024 **Author:** Shimon Ben-David Stay ahead of 2025’s AI-driven IT shift with practical predictions tailored for forward-looking technology leaders. Focus AI strategies on inferencing and fine-tuning pre-trained models for faster ROI. Rethink infrastructure around power efficiency as a core competitive advantage. Begin future-proofing data platforms for emerging exascale-scale workloads. Integrate DPUs to offload networking, storage, and security for higher efficiency. Align IT roadmaps with WEKA’s exascale-ready, cloud-native data infrastructure vision. ## [How NeuralMesh Enhances the Jupyter Notebook Experience](/article/how-neuralmesh-enhances-the-jupyter-notebook-experience) **Published:** December 18, 2024 **Author:** Product Marketing Team Stop waiting on slow Jupyter Notebooks—see how NeuralMesh speeds up your workflows across AI, data science, and HPC. Accelerate Python library imports and kernel startups from minutes to seconds. Eliminate small-file and metadata bottlenecks that stall Jupyter environments. Reduce reliance on fragile workarounds like caching, trimming imports, and local copies. Improve stability and reliability of data infrastructure supporting notebook workloads. Unlock faster time-to-insight for data-intensive research, modeling, and experimentation. ## [AWS re:Invent 2024 Recap: Part 1](/article/aws-reinvent-2024-recap-part-1) **Published:** December 16, 2024 **Author:** Phil Curran Discover what AWS’s latest AI and cloud announcements mean for architects, data leaders, and AI teams. Explore how AWS is rebuilding data centers for large-scale AI workloads. Understand the role of custom silicon like Trainium 2 in AI performance. See why next-generation AI demands equally advanced data storage infrastructure. Discover how WEKA NeuralMesh™ accelerates SageMaker HyperPod distributed training. Review concrete results from Stability AI’s NeuralMesh deployment on AWS. ## [How NeuralMesh Takes S3 to the Next Level](/article/how-neuralmesh-takes-s3-to-the-next-level) **Published:** December 10, 2024 **Author:** Product Marketing Team Supercharge your AI and data pipelines with S3-compatible storage purpose-built for millions of small, performance-sensitive objects. Understand why traditional S3 storage struggles with modern AI and small-object workloads. Discover how NeuralMesh delivers ultra-low latency and high throughput for S3 traffic. See how linear scalability keeps performance consistent as your datasets and clusters grow. Unify access with multi-protocol support across POSIX, S3, NFS, SMB, and GPUDirect Storage. Run the same high-performance S3 interface seamlessly across cloud and on-premises environments. ## [From Insight to Impact: Accelerating Data Management](/article/from-insight-to-impact-accelerating-data-management) **Published:** December 3, 2024 **Author:** Boni Bruno Supercharge your HPC and AI data operations with faster, smarter metadata management built for petabyte-scale environments from Starfish Storage and NeuralMesh. Accelerate metadata scanning to hundreds of thousands of operations per second. Gain rich data visibility with automated cataloging and tagging across diverse file types. Improve storage efficiency through automated compression and deep archive tiering. Strengthen security and compliance with real-time permissions auditing and PII tracking. Streamline large-scale data migration, replication, and hybrid cloud workflows for DR/HA. ## [Accelerate Distributed Model Training with NeuralMesh Support for Amazon SageMaker HyperPod](/article/accelerate-distributed-model-training-with-neuralmesh-support-for-amazon-sagemaker-hyperpod) **Published:** November 25, 2024 **Author:** Phil Curran Supercharge large-scale AI training on AWS by eliminating data bottlenecks and maximizing SageMaker HyperPod GPU utilization. Accelerate distributed model training with high-performance, massively scalable data infrastructure. Cut model checkpoint times by 90% to speed training cycles. Reduce data load times by 50% to boost research productivity. Increase GPU utilization to over 90% for better infrastructure ROI. Simplify data operations with automated tiering between Amazon S3 and high-performance flash storage. ## [Driving the Future of AI and HPC: WEKA at SC24](/article/driving-the-future-of-ai-and-hpc-weka-at-sc24) **Published:** November 19, 2024 **Author:** Product Marketing Team Discover how WEKA’s latest AI and HPC innovations at SC24 help technical leaders scale, accelerate, and productionize demanding data-intensive workloads. Accelerate AI and HPC workloads with NeuralMesh™ and NVIDIA Grace CPU Superchip. Reduce I/O bottlenecks while improving energy efficiency and data center footprint. Boost GPU utilization and AI performance with AI-native data architecture. Deploy WARRP, a cloud-agnostic RAG reference platform for production inference. Scale AI pipelines consistently across data centers and multiple cloud providers. ## [Accelerate EDA Workloads with NeuralMesh on Azure: The Fastest Data Platform in the Cloud](/article/accelerate-eda-workloads-with-neuralmesh-on-azure-the-fastest-data-platform-in-the-cloud) **Published:** November 13, 2024 **Author:** Boni Bruno Supercharge your semiconductor EDA workflows on Azure with NeuralMesh, built for teams demanding extreme performance and scalability. Accelerate chip design validation and tape-out with ultra-low latency storage. Maximize throughput for intensive frontend, backend, and blended EDA workloads. Scale seamlessly to millions of operations per second without performance degradation. Reduce complexity using a single namespace and simplified performance management. Rely on benchmark-leading SPEC Storage 2020 results for proven cloud performance. ## [Winning With WEKA: Dead & Company](/article/winning-with-weka-dead-and-company) **Published:** November 4, 2024 **Author:** WEKA Discover how WEKA powered Dead & Company’s immersive Sphere residency for creators pushing data-intensive live experiences. Explore how WEKA supported 1.5 petabytes of ultra-high-resolution concert visuals. See why Dead & Company’s production team trusted NeuralMesh™ for massive performance and scale. Understand how WEKA removed technical bottlenecks to keep the creative process flowing. Appreciate how WEKA enabled real-time, improvisational visuals matching the band’s live energy. ## [Ultra Fast Data Platform That’s Redefining Performance Efficiency](/article/ultra-fast-data-platform-thats-redefining-performance-efficiency) **Published:** October 24, 2024 **Author:** Product Marketing Team Discover how NeuralMesh™ delivers ultra-fast, efficient storage for AI, HPC, and data-intensive enterprises. Accelerate AI pipelines with distributed metadata and virtual metadata servers. Cut latency using kernel bypass for both networking and NVMe access. Maximize NVMe performance with 4K-granularity writes across all IO patterns. Reduce racks, cabling, and power vs. legacy storage architectures. Scale linearly on-premises and in the cloud while improving sustainability. ## [Building Bigger, Better: The Future of AI Starts Here](/article/building-bigger-better-the-future-of-ai-starts-here) **Published:** October 11, 2024 **Author:** Liran Zvibel Discover how NeuralMesh™ helps enterprises modernize infrastructure for real-world AI at exascale. Understand why traditional, piecemeal storage can’t keep up with AI-era data demands. Explore a unified, AI-native data platform built for exascale performance and simplicity. See how NeuralMesh supports hybrid and multicloud environments for dynamic data pipelines. Recognize WEKA’s role in shaping Gartner’s new file and object storage platform category. Grasp why becoming an AI-native company is now essential for long-term competitiveness. ## [Efficient Multitenancy: How NeuralMesh Solves Modern Cloud Challenges](/article/efficient-multitenancy-how-neuralmesh-solves-modern-cloud-challenges) **Published:** October 1, 2024 **Author:** Product Marketing Team Discover how NeuralMesh™ helps GPU cloud providers and large enterprises run secure, scalable multitenant environments without sacrificing performance or efficiency. Maximize GPU infrastructure utilization while keeping tenant data and operations isolated. Eliminate noisy neighbor issues through dedicated drives, cores, and memory per tenant. Scale to thousands of tenants with Kubernetes-based composable clusters. Reduce costs and environmental impact by sharing resources efficiently and securely. Accelerate tenant onboarding with fast, automated cluster creation and allocation. ## [Fit for Purpose: GPU Utilization](/article/fit-for-purpose-gpu-utilization) **Published:** October 1, 2024 **Author:** Product Marketing Team Maximize your AI GPUs instead of wasting them—this post shows AI leaders and platform teams how fit for purpose storage drives higher utilization and lower costs. Understand why GPU utilization is the critical efficiency and cost metric for AI. See how storage bottlenecks directly throttle training and inference performance. Discover how NeuralMesh sustains 90%+ utilization across demanding MLPerf workloads. Compare audited MLPerf Storage results to evaluate AI data platforms fairly. Quantify data center footprint, bandwidth, and cost savings vs. legacy scale-out NAS. ## [Fit for Purpose: Part Three – Cloud Service Providers](/article/fit-for-purpose-part-three-cloud-service-providers) **Published:** September 25, 2024 **Author:** Product Marketing Team Transform how you deliver AI in the cloud with storage built specifically for massive, power-efficient GPU workloads. See how NeuralMesh™ boosts GPU utilization and cuts infrastructure power by up to 10x. Compare NeuralMesh’s compact footprint against legacy multi-rack, power-hungry solutions. Explore NVIDIA Cloud Partner certification and support for 32K+ GPUs in one cluster. Optimize end-to-end AI data pipelines with zero-tuning, cloud-native storage architecture. ## [Fit for Purpose: Part Two – Client Access](/article/fit-for-purpose-part-two-client-access) **Published:** August 19, 2024 **Author:** Adam Fowler Stop letting NFS bottleneck your AI, ML, and HPC workloads—see how NeuralMesh™ client unlocks faster, more efficient data access for modern environments. Understand why NFS falls short for data-intensive, latency-sensitive workloads. Discover how NeuralMesh client delivers up to 7.5x throughput and 13x IOPS vs NFS. See how NeuralMesh uses fewer CPU resources per unit of data processed. Explore POSIX-compliant, high-performance client access with adaptive caching and full coherency. Compare Dedicated and Shared client modes to match performance and CPU utilization needs. ## [Fast Data, Now Simpler With the WEKA Cloud Deployment Manager](/article/fast-data-now-simpler-with-the-weka-cloud-deployment-manager) **Published:** August 12, 2024 **Author:** Phil Curran Discover how WEKA Cloud Deployment Manager streamlines deploying high-performance WEKA clusters consistently across AWS, Azure, and Google Cloud. Simplify cloud deployment with a guided, GUI-based workflow. Generate ready-to-use Terraform templates for your cloud provider. Configure networking, security, and tiering to object storage in one place. ## [WEKA Now Supports AWS Graviton and Azure Ampere Instances](/article/weka-now-supports-aws-graviton-and-azure-ampere-instances) **Published:** August 5, 2024 **Author:** Phil Curran Unlock higher price-performance and energy efficiency for your AI and HPC workloads using NeuralMesh with ARM-based AWS Graviton and Azure Ampere instances. Adopt ARM-based cloud compute for demanding AI, GenAI, and HPC workloads. Combine NeuralMesh with ARM to maximize performance and resource efficiency. Reduce storage costs and energy consumption while scaling performance-intensive workflows. Address GPU scarcity with more efficient, scalable cloud infrastructure options. Use WEKA POSIX clients on supported AWS and Azure ARM virtual machines. ## [Fit for Purpose: Part One - Networking](/article/fit-for-purpose-part-one-networking) **Published:** June 27, 2024 **Author:** Adam Fowler Stop forcing legacy storage to handle modern AI—see how NeuralMesh simplifies high-performance networking for large-scale AI teams. Compare purpose-built AI-native infrastructure with retrofitted legacy systems. Reduce network ports, cabling, and hidden infrastructure costs. Maximize bandwidth, IOPS, and latency efficiency for large AI workloads. Simplify physical network design and ongoing maintenance at scale. Future-proof AI environments with support for the latest high-speed networking. ## [Putting the Purple in the Rainbow](/article/putting-the-purple-in-the-rainbow) **Published:** June 25, 2024 **Author:** Colin Gallagher Discover what Pride Month truly represents, why it matters in tech, and how WEKA supports LGBTQ+ inclusion. Explain the history and meaning behind Pride Month and the Pride flag. Connect Pride to ongoing LGBTQ+ rights, visibility, and social justice efforts. Show why diversity and avoiding groupthink are critical for innovative tech companies. Highlight how inclusive workplaces benefit employees, culture, and business performance. Showcase WEKA’s global Pride initiatives and encouragement for allyship and participation. ## [Streamlining and Future Proofing AI LLM Inference](/article/streamlining-and-future-proofing-ai-llm-inferencing) **Published:** June 14, 2024 **Author:** Shimon Ben-David Supercharge your LLM inference pipeline with faster model loading, higher GPU utilization, and future-ready storage infrastructure. Boost GPU utilization by cutting model load times and underused instance costs. Accelerate model loading with high-performance file system mounts instead of S3. Scale inference farms quickly by spinning up GPU instances in less than half the time. Share and replicate large models seamlessly across multiple cloud environments. Future-proof infrastructure with GPUDirect Storage and rapid load/unload of GPU memory. ## [Winning With WEKA: Upstage AI](/article/winning-with-weka-upstage-ai) **Published:** June 10, 2024 **Author:** WEKA Discover how Upstage powers cutting-edge LLMs with NeuralMesh™. Understand why fast, scalable storage is critical for modern AI model training. See how Upstage’s Depth-Up Scaling (DUS) boosts LLM performance and reliability. Explore how NeuralMesh eliminates GPU–storage bottlenecks for large-scale AI workloads. Recognize NeuralMesh’s role in stable, at-scale performance monitoring and management. Get inspired by a real-world partnership driving next-generation generative AI innovation. ## [NeuralMesh Autoscaling: Bringing Elasticity to Cloud Storage](/article/weka-autoscaling-bringing-elasticity-to-cloud-storage) **Published:** May 30, 2024 **Author:** Barbara Murphy Unlock truly elastic cloud storage with NeuralMesh autoscaling, built for teams running performance-intensive, cost-sensitive applications in AWS, Azure, or Google Cloud. Match storage performance and capacity to application demand automatically. Eliminate over-provisioning and reduce costs from unused storage resources. Combine high-performance NVMe with low-cost object storage in one architecture. Support multiple protocols so diverse applications share a single data set. Automate deployment and scaling using Terraform across major cloud providers. ## [Elasticity for Your Data: What It Costs You, and What You can Do About It.](/article/elasticity-for-your-data-what-it-costs-you-and-what-you-can-do-about-it) **Published:** May 23, 2024 **Author:** Phil Curran Stop overpaying for cloud storage by making your data as elastic as your compute. Understand where cloud storage elasticity falls short across object, block, and file services. Discover how autoscaling data clusters eliminate overprovisioning and idle capacity costs. Separate performance from capacity using a single namespace across flash and object storage. Automate storage environments with Terraform and integrate them into existing operations. Move data easily across on-prem, edge, and multiple clouds with portable snapshots. ## [If You Were Waiting For a Sign That It’s Time to Modernize Your Data Stack, This Is It.](/article/if-you-were-waiting-for-a-sign-that-its-time-to-modernize-your-data-stack-this-is-it) **Published:** May 15, 2024 **Author:** Liran Zvibel Stop wrestling with legacy storage and see what an AI-native, platform-based data stack can do for modern, performance-intensive enterprises. Understand why traditional storage models can’t keep up with AI and HPC demands. Explore the eight core requirements of an AI-native, data pipeline-oriented architecture. See how a software-defined data platform simplifies management across edge, cloud, and on-premises. Discover how modern data platforms improve sustainability, security, safety, and cost efficiency. Gain perspective on where enterprise data stacks are heading by 2028 and beyond. ## [Get More Out of AWS Custom Silicon](/article/get-more-out-of-aws-custom-silicon) **Published:** May 3, 2024 **Author:** Phil Curran Unlock higher GPU utilization on AWS by converging storage and compute, ideal for AI and HPC teams battling data bottlenecks. Explain how AWS Nitro-driven compute densification changes AI and HPC infrastructure. Expose why traditional cloud storage starves powerful GPU instances of data. Describe converged architecture using WEKA NeuralMesh Axon on AWS instances. Detail resource allocation on P4d/P5 instances to maximize performance and efficiency. ## [Winning With WEKA: Sustainable Metal Cloud](/article/winning-with-weka-sustainable-metal-cloud) **Published:** April 25, 2024 **Author:** WEKA Discover how Sustainable Metal Cloud and WEKA power high-performance AI while dramatically cutting data center energy use. Explore SMC’s immersion-cooled HyperCube infrastructure for large-scale GPU clusters. Understand how advanced cooling slashes power costs and CO2e emissions by up to 50%. See why SMC chose NeuralMesh for efficient, high-performance AI data pipelines. Gain insight into SMC’s rapid APAC expansion to 16,000 sustainable GPUs. Recognize how this partnership enables responsible, large-scale enterprise AI innovation. ## [WEKA Sets New Records with Unofficial MLPerf Storage v0.5 Benchmark](/article/weka-sets-new-records-with-unofficial-mlperf-storage-v0-5-benchmark) **Published:** April 22, 2024 **Author:** Boni Bruno Discover how WEKA’s NeuralMesh delivers record-setting MLPerf Storage v0.5 performance for AI teams running large-scale, cloud-based training workloads. See why MLPerf Storage matters for real-world AI and ML workloads. Compare NeuralMesh’s cloud results against on-premises benchmark records. Explore record scalability for Unet-3D and BERT model storage performance. ## [Winning With WEKA: Yotta](/article/winning-with-weka-yotta) **Published:** April 18, 2024 **Author:** WEKA Discover how Yotta and WEKA power affordable, sustainable GPU cloud infrastructure for AI builders in India. Explore Yotta’s GPU-, CPU-, and cloud-as-a-service platform for AI workloads. Understand how Shakti Cloud cuts large-scale AI training and inference costs dramatically. See why Yotta selected NeuralMesh™ for high-performance, sustainable data infrastructure. Discover how NeuralMesh helps Yotta meet NVIDIA reference architecture performance requirements. Preview Shakti Cloud’s massive NVIDIA H100 and Blackwell GPU footprint in Mumbai. ## [Accelerate Autodesk Flame Workflows with a Certified Storage Solution](/article/accelerate-autodesk-flame-workflows-with-a-certified-storage-solution) **Published:** April 11, 2024 **Author:** Phil Curran Supercharge your Autodesk Flame workflows with WEKA’s NeuralMesh, built for modern, cloud-first creative teams. Accelerate generative AI model training, tuning, and inference for creative workflows. Support globally distributed Flame artists with elastic, cloud-based performance. Render Flame from AWS at 120 frames per second with zero frame loss. Enable follow-the-sun collaboration using a single shared copy of media. Control infrastructure costs while maintaining high performance and artist productivity. ## [Unveiling the AI Future: Highlights from NVIDIA's GTC 2024](/article/unveiling-the-ai-future-highlights-from-nvidias-gtc-2024) **Published:** March 29, 2024 **Author:** Joel Kaufman Discover what NVIDIA’s latest GTC announcements mean for AI leaders building high-performance, sustainable infrastructure. Understand how NIMs and NeMo streamline building AI pipelines on NVIDIA hardware. Explore Omniverse and Earth-2 for digital twins and large-scale simulation. Compare Blackwell-based DGX GB200 systems with previous GPU generations and their power demands. Recognize why power has become the key constraint in AI data centers. See how WEKApod improves performance density while cutting storage power consumption for massive GPU farms. ## [Hot Take: How NVIDIA Just Changed the Game for AI in the Cloud](/article/hot-take-how-nvidia-just-changed-the-game-for-ai-in-the-cloud) **Published:** March 26, 2024 **Author:** Phil Curran NVIDIA’s new Inference Microservices and Blackwell platform transform enterprise AI speed, control, and deployment for technical and business leaders. Collapse AI app time to market from months to hours. Combine managed-service simplicity with full control over models, data, and infrastructure. Integrate containerized AI models directly into existing CI/CD pipelines. Deploy trained models flexibly across on-premises, DGX Cloud, and emerging GPU clouds. Pair NVIDIA NIMs with WEKA’s AI-native data platform for end-to-end deployment flexibility. ## [Accelerating AI Workloads: WEKA's Role in Storage Partner Validation for NVIDIA OVX Solutions](/article/accelerating-ai-workloads-wekas-role-in-storage-partner-validation-for-nvidia-ovx-solutions) **Published:** March 18, 2024 **Author:** Product Marketing Team Discover how WEKA’s NeuralMesh and NVIDIA OVX storage validation help you run faster, more efficient enterprise AI workloads. Understand the role of high-performance storage in maximizing GPU utilization. See how NVIDIA-Certified Systems ensure reliable, scalable AI infrastructure. Explore NeuralMesh’s unified architecture across on-premises and public cloud environments. Leverage validated WEKA and NVIDIA OVX solutions to accelerate diverse AI workloads. ## [New WEKApod Appliance Lets Storage Keep Up with Modern Compute and Networking](/article/new-wekapod-appliance-lets-storage-keep-up-with-modern-compute-and-networking) **Published:** March 18, 2024 **Author:** Product Marketing Team Supercharge your NVIDIA DGX SuperPOD with balanced, AI-native storage designed for modern high-speed networks. Eliminate storage bottlenecks that waste powerful GPUs and fast networks. Deploy a turnkey, pre-configured WEKApod appliance for faster time to value. Scale from a single petabyte to hundreds of high-performance storage nodes. Integrate seamlessly with NVIDIA ConnectX-7, InfiniBand, and Base Command Manager. Reduce energy use and carbon footprint with an efficient AI-native storage architecture. ## [WEKA Dominates SPECstorage Benchmark, Shattering Records: Update](/article/weka-dominates-specstorage-benchmark-shattering-2020-records) **Published:** March 18, 2024 **Author:** WEKA See how WEKA’s NeuralMesh storage shatters SPECstorage 2020 records for cloud and on-prem performance-focused teams. Compare NeuralMesh’s record-breaking SPECstorage 2020 results across AI, VDA, genomics, EDA, and software builds. Understand how NeuralMesh delivers higher job counts with lower overall response times. Evaluate cost efficiency using AWS and Azure infrastructure pricing examples. See why consistent performance across any IO profile accelerates real-world data pipelines. ## [Fast, Sustainable, and Accessible AI: A Shared Vision](/article/fast-sustainable-and-accessible-ai-a-shared-vision) **Published:** February 16, 2024 **Author:** WEKA Discover how WEKA and NexGen Cloud deliver fast, accessible, and sustainable GPU cloud infrastructure for AI builders and enterprises. Understand NexGen Cloud’s mission to democratize high-performance AI infrastructure. See how NeuralMesh powers elastic, future-ready GPU cloud performance. Explore how GPU infrastructure is optimized for speed, scale, and low latency. Recognize the role of renewable energy in sustainable AI data centers. Appreciate how efficient GPU utilization reduces costs and environmental impact. ## [Introducing Automated WEKA Deployment on AWS using Terraform](/article/introducing-automated-weka-deployment-on-aws-using-terraform) **Published:** December 7, 2023 **Author:** Phil Curran Streamline your AI and HPC storage on AWS with Terraform-driven, under-30-minute WEKA deployments built for cloud automation teams. Automate WEKA cluster deployment on AWS using standardized Terraform modules. Enable rapid, under-30-minute WEKA environment provisioning in new or existing VPCs. Scale WEKA clusters up or down via AWS Auto Scaling Groups to match demand. Maintain reliability with Terraform-driven auto-healing using AWS CloudWatch monitoring and events. Simplify multi-protocol data access for HPC and AI workflows with automatic client deployment. ## [Riding the Waves of Data Infrastructure Evolution to Help Customers Stay Ahead](/article/riding-the-waves-of-data-infrastructure-evolution-to-help-customers-stay-ahead) **Published:** November 7, 2023 **Author:** Liran Zvibel Stay ahead of fast-changing unstructured data demands with cloud-ready, visionary infrastructure built for modern, data-intensive organizations. Understand why traditional file and object storage can’t meet today’s distributed data needs. See how unified file and object capabilities support high-performance, scalable data pipelines. Explore why cloud has become central for AI, ML, and other demanding workloads. Recognize WEKA’s role as a Gartner-recognized visionary in data infrastructure. Appreciate how WEKA partners with forward-thinking customers across multiple data-intensive industries. ## [Get More Out of Your Cloud GPUs](/article/get-more-out-of-your-cloud-gpus) **Published:** September 7, 2023 **Author:** Phil Curran Stop wasting expensive cloud GPUs and discover how to keep them fully utilized for generative AI workloads. Expose why GPUs sit idle up to 70% of training time. Replace multi-hop data pipelines with a single, zero-copy namespace. Saturate GPU throughput with high-performance, low-latency AI storage. Support every generative AI workflow stage without creating new data silos. Cut epoch times dramatically, enabling far more experiments and faster results. ## [Simplify and Accelerate Containerized Cassandra on Azure with NeuralMesh](/article/simplify-and-accelerate-containerized-cassandra-on-azure-with-neuralmesh) **Published:** September 5, 2023 **Author:** Shimon Ben-David Supercharge your Cassandra clusters on Azure by containerizing with NeuralMesh to cut costs, simplify operations, and boost performance. Simplify Cassandra deployment and orchestration using Azure Kubernetes Service with NeuralMeshintegration. Replace local SSDs and block storage with a single high-performance system. Eliminate costly Cassandra rebuilds by rapidly remapping failed nodes to existing data. Scale Cassandra clusters and Azure instance sizes on the fly without data movement. Gain elastic capacity, instant snapshots, and integrated backup/DR across Azure regions. ## [WEKA is Now Available on Google Cloud Marketplace](/article/weka-is-now-available-on-google-cloud-marketplace) **Published:** May 23, 2023 **Author:** Phil Curran Discover how NeuralMesh on Google Cloud Marketplace helps teams run demanding, data-intensive workloads in the cloud. Deploy NeuralMesh directly from Google Cloud Marketplace with a simplified, "easy button" experience. Accelerate generative AI training with exabyte-scale, high-performance data storage in Google Cloud. Power cloud-based VFX studios with on-premises-like performance and responsive rendering for artists. Speed genomic sequencing pipelines by eliminating data bottlenecks across massive, complex datasets. Combine cloud scalability and simplicity with high performance to run previously "impossible" workloads. ## [Ready to Get More From Your Azure Workloads?](/article/ready-to-get-more-from-your-azure-workloads) **Published:** May 17, 2023 **Author:** WEKA Supercharge demanding Azure workloads with NeuralMesh, built for cloud, AI, and hybrid performance teams. Achieve up to 10x faster throughput and 7x higher IOPS than Azure NetApp Files. Run performance-intensive workloads like generative AI, genomics, and VFX rendering in Azure. Deploy a single namespace with automatic tiering across NVMe flash and Azure Blob storage. Scale performance and capacity independently using Azure VM Scale Sets and Terraform automation. Migrate or burst to Azure easily with Snap-to-Object snapshots across on-premises and cloud. ## [How to Accelerate Drug Discovery with Cryo-EM in the Cloud](/article/how-to-accelerate-drug-discovery-with-cryo-em-in-the-cloud) **Published:** April 13, 2023 **Author:** Phil Curran Supercharge structure-based drug discovery by running data-hungry Cryo-EM workflows on flexible, scalable cloud infrastructure. Address massive Cryo-EM compute and storage demands with on-demand cloud resources. Accelerate protein structure processing to speed structure-based drug discovery decisions. Reduce HPC infrastructure, storage, and software license costs through elastic scaling. Eliminate hardware procurement delays with rapid, automated Cryo-EM environment deployment. Gain continuous access to the latest GPUs, CPUs, and high-performance storage in the cloud. ## [Accelerate Drug Discovery with Cryo-EM in the Cloud](/article/accelerate-drug-discovery-with-cryo-em-in-the-cloud) **Published:** April 13, 2023 **Author:** Phil Curran Transform your Cryo-EM drug discovery workflows with a cloud reference design built for HPC teams. Accelerate protein structure determination with optimized GPU, CPU, and storage resources. Deploy complete Cryo-EM data processing clusters in days, not months. Streamline data ingest and management with a single high-performance WEKA file system. Scale compute and storage automatically to control cloud and licensing costs. Reduce operational burden for researchers, IT teams, and cloud architects. ## [WEKA Responds to Unfounded Allegations Made by MinIO Regarding Open Source Licensing](/article/weka-responds-to-unfounded-allegations-made-by-minio-regarding-open-source-licensing) **Published:** March 27, 2023 **Author:** Nilesh Patel Get clear, factual reassurance about WEKA’s open source licensing dispute with MinIO and what it means for you. ## [Accelerate Genomics Research with an Storage System for Speed, Simplicity, and Scale](/article/accelerate-genomics-research-with-an-storage-system-for-speed-simplicity-and-scale) **Published:** February 15, 2023 **Author:** Shimon Ben-David Stop letting fragmented storage slow genomics research—see how storage transforms data access for life sciences teams. Consolidate sequencer and application data into a single, always-accessible system. Eliminate manual data movement and constant IT support tickets. Maintain seamless access to historical datasets for retrospective analysis. Optimize storage costs with automated, intelligent tiering across on-premises and cloud. Scale effortlessly to support rapidly growing compute and data demands. ## [More is More: Lenovo and WEKA’s New Turnkey Solutions Offer Choice and Simplicity](/article/more-is-more-lenovo-and-wekas-new-turnkey-solutions-offer-choice-and-simplicity) **Published:** February 7, 2023 **Author:** Product Marketing Team Unlock faster, simpler AI and analytics infrastructure with Lenovo–WEKA turnkey data solutions built for performance-intensive, latency-sensitive workloads. Gain flexible, software-defined infrastructure that adapts to changing hardware availability and requirements. Simplify complex hardware choices with validated WEKA Ready Node configurations from Lenovo. Accelerate AI training and data-intensive workloads with up to 10x higher performance than all-flash NAS. Scale performance linearly as infrastructure grows for efficient resource utilization. Start small and expand seamlessly while maintaining high performance, reliability, and low latency. ## [Build your VFX and Finishing Studio in the Cloud](/article/build-your-vfx-and-finishing-studio-in-the-cloud) **Published:** February 6, 2023 **Author:** Product Team Transform your VFX and finishing pipeline by moving Flame-based studios to a global, cloud-powered workflow. Deploy a full VFX and finishing studio in hours instead of days. Cut cloud infrastructure costs by up to 50% while scaling efficiently. Speed up Autodesk Flame workflows by as much as 10x. Support multi-petabyte, high-resolution media with low-latency global access. Enable follow-the-sun collaboration for artists across regions and time zones. ## [WEKA named to the Leader's Circle by GigaOM](/article/weka-named-to-the-leaders-circle-by-gigaom) **Published:** January 6, 2023 **Author:** Phil Curran Discover why IT leaders running next-generation, data-intensive workloads choose WEKA for high-performance cloud file storage. Understand what GigaOm’s Cloud File Systems Radar Report evaluates and why it matters. See how WEKA addresses performance, scale, simplicity, and cost without forcing trade-offs. Explore why WEKA fits AI/ML, HPC, HFT, and other next-generation workloads. Recognize the importance of consistent storage across data center, edge, and cloud deployments. Gain insight into how the right storage choice accelerates digital transformation initiatives. ## [Machine Learning In The Cloud: What Are The Benefits?](/article/machine-learning-cloud) **Published:** September 28, 2022 **Author:** Product Marketing Team Explore how cloud infrastructure powers scalable, high-performance machine learning and what to prioritize in an ML-ready cloud. Explain key cloud benefits like big data, GPUs, and elastic scaling. Outline must-have ML cloud features in hardware, software, and containers. Highlight NeuralMesh capabilities for advanced, secure ML workloads. ## [Half an Exabyte at SLAC. What does that “scale” really mean?](/article/half-an-exabyte-at-slac-what-does-that-scale-really-mean) **Published:** September 6, 2022 **Author:** Product Team Discover what handling half an exabyte of data really involves if you're tackling massive, fast-growing datasets. Understand how SLAC’s Vera C. Rubin project generates and processes hundreds of petabytes. See how NeuralMesh enables linear performance growth as telescope data volume increases. Learn how SLAC reduces costs by tiering data to economical object storage while scaling capacity independently. ## [How Do HPC & AI Work Together? What Can They Do?](/article/hpc-ai) **Published:** August 31, 2022 **Author:** WEKA Explore how high-performance computing powers demanding AI workloads across analytics, modeling, autonomy, and genomics. Understand the core HPC components that enable large-scale AI training and inference. See how AI and HPC combine for predictive analytics, physics, and autonomous systems. Get to know NeuralMesh infrastructure for scalable, secure, GPU-accelerated AI workloads. ## [WEKA and …Oracle?](/article/oracle-oci) **Published:** April 19, 2022 **Author:** Product Marketing Team Discover how running NeuralMesh on Oracle Cloud Infrastructure supercharges demanding HPC, AI/ML, and GPU workloads for data-intensive teams. Eliminate data stall by removing copies across your end-to-end data pipelines. Keep GPU utilization high with massively performant IO for bandwidth- and IOP-driven workloads. Scale to petabytes using NVMe SSDs for hot data and OCI Object Storage for warm/cold tiers. Combine OCI capabilities with WEKA to accelerate time-to-value for EDA, AI/ML, and analytics. ## [WEKA Doesn’t Make the GPU, WEKA Makes the GPU 20X Faster](/article/weka-doesnt-make-the-gpu-weka-makes-the-gpu-20x-faster) **Published:** March 15, 2022 **Author:** Product Marketing Team Stop starving your GPUs—this post shows teams how NeuralMesh removes data bottlenecks to radically speed deep learning training. Eliminate multi-hop data copies that keep GPUs idle and waste training time. Fuse high-capacity object storage with high-speed storage in a single namespace. Saturate NVIDIA GPUs using GPUDirect Storage and a zero-copy data pipeline. Cut latency with 4K blocks, distributed metadata, and kernel-bypass networking. Achieve up to 20X faster training epochs versus traditional data pipelines. ## [Bioinformatics Pipeline & Tips For Faster Iterations](/article/bioinformatics-pipeline) **Published:** February 20, 2022 **Author:** Greg Mazzu Accelerate your genomics and life sciences research with practical guidance for designing faster, GPU-optimized bioinformatics pipelines. Clarify what bioinformatics and bioinformatics pipelines are in simple terms. Understand why next generation sequencing demands high-performance, scalable infrastructure. Compare class-based, server-based, and cloud-based pipeline frameworks. Apply best practices to reuse computations and optimize workflow design. Evaluate how WEKA’s cloud platform supports large-scale, GPU-accelerated bioinformatics workloads. ## [Life Science Data | Why Big Data, AI & Analytics Matter](/article/life-science-data) **Published:** November 30, 2021 **Author:** Greg Mazzu Unlock how modern cloud, AI, and big data transform life science research for data-driven teams. Understand what life science data is and where it comes from. See how cloud platforms accelerate genomics, trials, diagnosis, and personalized treatment. Recognize key benefits of AI-driven analytics for clinicians and researchers. Anticipate security, ethics, performance, collaboration, and scalability challenges. Explore how NeuralMesh supports high-performance, scalable life science workloads. ## [Is Enterprise NAS the Best Option for Your Organization?](/article/enterprise-nas) **Published:** November 17, 2021 **Author:** Barbara Murphy Compare enterprise NAS with modern alternatives to decide how to store and power your data. Explain what enterprise NAS is and where it excels. Weigh benefits, challenges, and scalability, performance, and security needs. Consider NeuralMesh for demanding AI, HPC, and data-intensive workloads. ## [Computer Vision vs. Machine Learning | How Do They Relate?](/article/computer-vision-vs-machine-learning) **Published:** November 2, 2021 **Author:** WEKA Clarify how machine learning and computer vision connect so you can choose the right approach for your AI projects. Define machine learning and its main learning approaches and techniques. Explain what computer vision is and how it processes visual data. Show how deep learning and neural networks power both ML and computer vision. Clarify why computer vision is a subset of machine learning and deep learning. Illustrate real-world computer vision applications in cars, retail, and healthcare. ## [Scale-Up vs Scale-Out Data Center Architectures](/article/scale-up-vs-scale-out) **Published:** April 14, 2021 **Author:** WEKA Compare scale-up and scale-out architectures to choose the right data storage strategy. Weigh upfront costs against long-term scalability and management complexity. Understand reliability, performance limits, and upgrade constraints of scale-up systems. Evaluate cloud-based flexibility, downtime reduction, and future growth support with scale-out. ## [Stateless vs Stateful Kubernetes](/article/stateless-vs-stateful-kubernetes) **Published:** February 4, 2021 **Author:** WEKA Compare stateless and stateful Kubernetes workloads, storage options, and how WekaFS supports demanding, data-intensive applications. Clarifies stateless versus stateful containers and typical use cases. Explains Kubernetes persistent storage, volumes, and provisioning methods. Highlights WekaFS benefits for high-performance, portable stateful workloads. ## [GPFS Parallel File System Explained](/article/gpfs-parallel-file-system) **Published:** October 31, 2020 **Author:** Barbara Murphy Compare legacy GPFS with modern parallel file systems optimized for flash, AI, and GPU workloads. Highlight GPFS limitations for small-file, low-latency, I/O-intensive workloads. Explain how distributed metadata and NVMe unlock massive parallel performance. Contrast WEKA and GPFS on throughput, scalability, and ease of management.