# NeuralMesh Across Eight Industries: One Platform for Evolving AI Workloads **Author:** Steven Miller **Published:** August 31, 2026 ![A stark, elegant composition that visually conveys unified, high-speed AI, HPC, and data-intensive workloads across industries operating seamlessly on one secure, shared platform.](https://cdn.sanity.io/images/ult5g8gw/production/f1a5f15e9b53ebbd09a0d45ddf4abc43f88caacc-1408x768.jpg) ## TL;DR Discover how NeuralMesh 6 removes AI storage bottlenecks to keep GPUs fully utilized across industries. - Unify AI, HPC, and data-intensive workloads on one high-performance platform. - Eliminate storage bottlenecks with parallel I/O, real-time rebuilds, and data reduction. - Support strict security, multi-tenancy, and observability for regulated, shared environments. Modern infrastructure runs on three things: compute, networking, and storage. The industry spent recent decades modernizing both compute and networking, accelerating compute which is built around GPUs and optimizing networking for speed and low latency. However, when it comes to the actual storage architecture, modernization has fallen behind. It was “simply good enough” for most uses when the environment had very predictable files, steady access, and relatively modest concurrency across high-performance computing (HPC) workloads. That’s changing as AI workloads become the focal point. In 2026, storage is now the bottleneck. Simulation, training, and inference land on the same infrastructure, push concurrency and metadata in different directions, and expose the storage layer as the place where the data pipeline breaks down. When storage fails to keep up with data, agentic, inference, and AI model training pipelines stall out, causing GPUs, the most expensive hardware in the building, to sit idle and underutilized. That gap now shows up everywhere and in every industry integrating AI workflows into their HPC environments. A bank retraining fraud models against live market data, a studio rendering in 8K, a research lab reconstructing genomes, an automaker training on fleet sensor data: all of them now run AI alongside HPC on the same infrastructure, and all of them hit the same wall at the storage layer. NeuralMesh™ 6 is [the data layer built for that reality](/pressRelease/neuralmesh-6-enterprise-agentic-ai). It doesn’t run your models, it keeps them fed, so the GPUs you’re paying for stay busy. It enables organizations to run AI training, inference, and accelerated compute on a single software stack with [native multi-tenancy](/product/neuralmesh#multitenancy), a full [S3 object stack](/product/neuralmesh/#s3-object-storage) on NVMe, intelligent [replication](/product/neuralmesh/#replication), always-on [data reduction](/product/neuralmesh/#data-reduction) with a contractual guarantee, [data resiliency that rebuilds in real time](/resources/demo/neuralmesh-rebuilds-data-in-realtime/), and [NeuralMesh Observe](/product/neuralmesh/#observe) for operational visibility across every cluster. This equates to faster, more efficient, more scalable, and more profitable outcomes. And those economics of NeuralMesh are already proven in production, not just marketing lingo. On Oracle Cloud Infrastructure, NeuralMesh with [Augmented Memory Grid™](/product/augmented-memory-grid/) reached 10x higher token throughput, 10x more concurrent users, and 7x more tokens per GPU on H100 infrastructure. Here is what that foundation means for the industries WEKA serves, including financial services, healthcare and life sciences, higher education and research, media and entertainment, manufacturing, energy, government agencies, autonomous vehicles, and others. ## Financial services Across [financial services](/solutions/financial-services/) workloads, including trading, risk analysis, banking services, prediction markets, and fraud detection, organizations live in a world where microseconds matter most. [High frequency trading](/resources/solution-brief/high-frequency-trading-financial-services/) and [quantitative trading](/resources/solution-brief/quantitative-trading-financial-services/) strategies pull data from hundreds of sources simultaneously. The models behind fraud detection and risk analysis retrain constantly against fresh data. [Sports and prediction markets](/resources/solution-brief/real-time-sports-and-predicion-markets/) process data from live events and update odds and user opportunities in real-time. NeuralMesh keeps data moving because it serves it in parallel from every node at once, with metadata operations fast enough that thousands of concurrent reads don't queue. For a quant shop, that means a model retrains on the last hour of market data, not last night's, and the strategy ships before the edge decays. For firms running mixed workloads on shared infrastructure, per-tenant encryption with independent keys, network isolation, and per-tenant quality of service give each team a hard compliance boundary. You satisfy an auditor without standing up a second cluster for every regulated workload. NeuralMesh also supports the full S3 stack on NVMe, enabling firms to consolidate years of tick and market data into one namespace that's addressable as file and object at once. Put simply, this allows backtesting and live models to read the same data without a copy step. And when a drive or node fails mid-session, NeuralMesh rebuilds in real time across the cluster, so trading and risk stay online instead of dropping into a degraded state during market hours. Read our article: [AI in Financial Services | How Does It Work?](/learnArticle/gpu-ai-in-financial-services), to dive into how AI is used in financial services and how your organization can maximize its benefits. ## Healthcare and life sciences Across [healthcare and life sciences](/solutions/healthcare-life-sciences/) (HCLS) workloads, from drug discovery and next-generation sequencing to cryo-EM and bio-imaging, data arrives faster than most storage can absorb it, and the science stalls when researchers stage files instead of running experiments. These are shared environments, where one platform serves hundreds of scientists running very different pipelines at the same time. Similarly to financial services, [HCLS workloads](/resources/solution-brief/neuralmesh-for-healthcare-life-sciences/) feature datasets that typically need to be addressable as file and object at once. For example, a genomics pipeline moves from ingest to analysis without copying the dataset between systems. NeuralMesh is how [Genomics England](/customers/genomics-england/) holds a 41 PB dataset in a single namespace while cutting infrastructure costs by 75% by allowing [Genomics England](/caseStudyResource/genomics-england-case-study)’s researchers to query and process the same data in place instead of staging copies. In clinical research settings, there are dozens if not hundreds of researchers working in tandem. Native multi-tenancy supports the shared side of research with per-tenant encryption, independent KMS, and per-tenant quality of service creating a single environment with encryption in-flight. In practice, this means organizations like the [Swiss Institute of Bioinformatics](/company/swiss-institute-bioinformatics), which has more than 800 scientists, are able to [easily work concurrently](/caseStudyResource/sib-swiss-institute-of-bioinformatics-case-study) without storage becoming the bottleneck. To see how to break down the blockade, read this article: [The bottleneck in cancer research isn't the algorithm](/article/cancer-research-bottleneck-isnt-the-algorithm). ## Higher education and research In [higher education research](/solutions/higher-education-research/) settings, computing serves many disciplines on one shared platform, from climate modeling to astrophysics and AI research, and it can’t pause to tune storage for each group. [Higher education research](/resources/solution-brief/higher-education-research/) budgets are grant-funded and IT teams are lean, so the research platform has to stay predictable without constant babysitting. Otherwise researchers are spending time working on infrastructure instead of science. That’s not where researchers excel, so it’s essentially a waste of already limited time and resources. Native multi-tenancy makes that shared model work, adding per-tenant quality of service so one heavy job doesn’t starve the rest. The [Wharton School](/company/the-wharton-school) runs more than 20 research centers, with over 225 faculty and 150 graduate students performing research on a single system. Adding NeuralMesh allowed them to move off its old NFS setup and [changed what researchers could get done](/caseStudyResource/flexibility-in-the-cloud-elevates-innovation-and-research-impact-at-the-wharton-school), increasing overall throughput and letting researchers hit publication deadlines. In many studies, research runs can last for days, so when a node fails mid-run, it’s a massive issue. That’s where the ability to rebuild nodes across the cluster on the fly allows long jobs to finish instead of restarting. That type of predictability is how [Deakin University](/resources/case-study/deakin-university-powers-transformative-ai-research-with-weka/) saw a 10x performance improvement and cut training epochs from ~40 minutes to ~6 minutes across the projects of more than 80 researchers. Watch this Impossibles video to see how Stanford Linear Accelerator Center (SLAC) deployed NeuralMesh at the Vera C. Rubin Observatory to make sure their massive, petabyte sized datasets could move globally with extremely low latency requirements at the far edge of scale. ## Media and entertainment In the [media and entertainment](/solutions/media-and-entertainment/) (M&E) industry, projects from visual effects (VFX) rendering, to post-production, to 8K and live immersive entertainment, require storage to support AI workloads that move enormous files and demand steady throughput as more artists and nodes come online. Studios are now also building generative video, which puts training and inference on the same platform as the creative pipeline. [Media and entertainment](/resources/solution-brief/weka-for-media-and-entertainment/) budgets live and die on storage economics. A single show can generate 90 TB, facilities keep petabytes online across active and archived projects, and every terabyte kept on fast storage is a line item. That's the problem that always-on data reduction attacks: [Dead & Company](/caseStudyResource/dead-company-dead-forever-at-sphere-cs) managed 1.5 PB for its Dead Forever residency at Sphere while keeping active projects instantly available. Per Brandon Kraemer, Technical Director for [Dead Forever](/customers/dead-and-company/), “Technology is at its best when it gets out of the way of the creative process, and that is exactly what NeuralMesh did for us at Sphere.” Generative video is different again: training and inference run continuously, and steady, low-latency reads are what keep expensive GPUs busy instead of idling between batches. [Luma AI](/resources/case-study/building-intelligence-that-sees-and-creates-how-luma-ai-accelerates-every-experiment-with-weka/) builds at that pace, cutting training startup times by 40x and holding GPU utilization between 95-100%. Similarly, for studios that spin cloud environments up and down per project, intelligent replication and remote caching make cloudburst rendering practical without copying full datasets between sites. [Parliament VFX](/caseStudyResource/optimizing-cloud-resources-is-the-key-to-digital-agency-profitability) quartered its cloud storage costs after adding NeuralMesh, prompting Tom Taylor to state: “The first thing that happened after we migrated to NeuralMesh was our CFO hugged me._”_ ## Manufacturing [Manufacturing](/solutions/manufacturing/) spans robotics, industrial, aerospace and defense, automotive, and semiconductor workflows, and the storage pressure is the same across all of them. [Manufacturing](/resources/solution-brief/neuralmesh-improves-quality-control-and-expedites-production-for-manufacturers/) runs two heavy workloads at once. Simulation (crash models, CFD, finite element analysis) writes terabytes per time step across jobs that run for days. At the same time, AI runs quality control on the line, ingesting telemetry, camera, and LiDAR streams and making inference calls in real time while digital twins mirror the floor. One platform has to feed both without either one starving. Aerospace and defense simulation needs low-latency writes and can’t afford lost jobs, so data resiliency comes first. As is the case in other industries, NeuralMesh rebuilds in real time across the cluster to keep multi-day runs from failing and wasting the compute already spent on them. A modern plant is rarely contained to a single physical location, and quality-control models and digital twins run across factories and edge locations at once. NeuralMesh Observe gives operations teams multi-cluster visibility, client-level diagnostics, and alerting from one dashboard, so a slowdown on one line surfaces before it stalls production. For inference that lives on the floor, [NeuralMesh Axon™](/product/neuralmesh-axon/) runs storage directly on the GPU servers with no separate tier to feed, and always-on data reduction holds growing training and telemetry datasets at up to 6x capacity savings, with less than 5% write overhead and a guarantee on both the ratio and the performance impact. For a more complete picture of where these bottlenecks show up, read our article: [Manufacturing AI has a storage problem](/article/storage-built-for-manufacturing-ai-feed-every-gpu-catch-every-defect-keep-production-moving). ## Energy [Energy](/solutions/energy/) exploration turns seismic reflections into subsurface models, and the [datasets are enormous](/resources/solution-brief/weka-for-energy-ai/)). That same accelerated infrastructure now carries physics-informed AI and predictive digital twins for fusion research and grid planning, and because data is often captured in the field and processed elsewhere, the pipeline spans sites and clouds. Seismic processing is throughput-bound, and NeuralMesh moves data fast enough to keep those jobs saturated. One seismic workflow compressed runtime from 80 hours to under four, ran at 2x the performance of a prior cloud architecture, and provided 6x more throughput than cloud-managed Lustre on equivalent infrastructure, while grid interconnection study timelines dropped by 80%. Because acquisition and processing rarely happen in the same place, intelligent replication moves field data to wherever compute lives, with metadata-first replication and remote caching that make cloudbursting practical for peak processing without full-dataset copies. At this scale, operators also need to see what the infrastructure is doing, and NeuralMesh Observe gives them multi-cluster visibility and alerting from one dashboard across distributed seismic and research estates. Always-on data reduction keeps the underlying petabyte datasets economical without slowing the jobs that read them. The [UK Atomic Energy Authority](https://www.hpcwire.com/off-the-wire/uk-government-invests-45m-in-sunrise-ai-supercomputer-for-fusion-research/) points to where this is headed, combining high-fidelity simulation with physics-informed AI to build predictive digital twins that reduce the cost, risk, and time of physical testing. ## Government agencies [Government AI ](/solutions/government-agencies/)carries requirements most sectors do not, including isolation between missions, data sovereignty, and predictable performance under classification boundaries. WEKA named these operators at launch alongside AI clouds and frontier model providers. Native multi-tenancy gives hardware-level isolation per mission through composable clusters, while virtual multi-tenancy adds VPC-like network isolation, per-tenant encryption with independent KMS, and independent LDAP or AD authentication. Many programs run on shared infrastructure without crossing lines, and a new tenant provisions in under 30 minutes. With NeuralMesh Observe, operators have clear multi-cluster visibility, client-level diagnostics, and alerting from one dashboard at no additional cost. And because mission workloads can’t tolerate degraded states, real-time rebuilds keep operational systems available when hardware fails. ## Autonomous vehicles [Autonomous vehicles](/solutions/autonomous-vehicles/) train on exabytes of camera, LiDAR, and radar data, and the [training loop is the business](/resources/solution-brief/neuralmesh-for-autonomous-vehicles/). Sensor data is captured across a distributed fleet, then ingested, curated, and replayed through simulation and validation before it ever reaches a training run, and all of it lands on the same platform. Because fleet data lives across sites and clouds, intelligent replication makes remote datasets immediately browsable and hydrates data on demand, so teams place training where GPU capacity exists today rather than where the data was first written. [WeRide](/company/weride) works at that scale, processing multiple petabytes of fleet sensor data daily across 2 million kilometers of driving. At exabyte scale, capacity is a first-order cost, and always-on data reduction shrinks video and sensor archives with a guarantee that the savings never come at the expense of read speed during training. The payoff is raw performance at scale. [Cerence](/caseStudyResource/cerence-chose-wekafs-high-performance-data-storage) runs both POSIX and object workloads on one platform, 900 TB on NeuralMesh alongside 3.2 PB of object storage, and describes the performance and latency as far superior to any other solution it evaluated. The gains compound elsewhere too: one AV developer cut a full training pass from 80 hours to four, and [Innoviz](/pressRelease/wekafs-selected-by-innoviz-to-accelerate-ai-for-autonomous-vehicle-innovations) drives more than 70 GB per second to a single GPU node, roughly 10x what NFS delivered. ## One platform, wherever the work runs The same handful of capabilities, replication, multi-tenancy, observability, and data reduction, show up in every one of these industries. Whatever you're building, the job underneath it is the same: keep GPUs and CPUs fed, stay predictable at scale, and hold up when hardware fails. [NeuralMesh 6](https://www.youtube.com/watch?v=pjPLCx5KgWI) does that on one software stack, on-prem or in the cloud, from dedicated storage nodes or directly on GPU servers with NeuralMesh Axon. For teams that want it turnkey, [WEKApod™ 3](/product/wekapod) ships with the software preinstalled. Your models aren't slow. Your data is. It’s time to fix that bottleneck. [Talk to WEKA today](/lp/contact-us/) to explore what NeuralMesh 6 does for your industry.