Where Data Flows, Discovery Follows
Building your AI Factory starts with a solid foundation – and that’s more than just hardware. NeuralMesh™ connects your data to AI pipelines to deliver real, transformative business value.
AI Is Constantly Changing. You Need a Dynamic Foundation.
The AI Factory you build today has to run the AI workloads of tomorrow. Agentic AI doesn’t run on batch pipelines — it runs on live, connected data. NeuralMesh provides a single data layer that spans training, fine-tuning, inference, and agent orchestration across hybrid and multi-cloud environments — so your factory doesn’t need a full rebuild every time the AI landscape shifts.
AI Factories Need AI-Ready Storage
NeuralMesh makes it possible to build the AI Factory you’ll need tomorrow by maximizing the value of what you have today.
Connect the AI Stack
NeuralMesh doesn’t try to run your AI Factory — it connects it. From compute and networking to orchestration, enterprise software, and APIs, NeuralMesh integrates seamlessly with the components you’ve already invested in. No rip-and-replace. No vendor lock-in. Just a high-performance data layer that makes everything around it work better.
Keep Every GPU Working
An AI Factory is only as productive as its slowest component. Most storage architectures were built before AI. NeuralMesh is engineered specifically for compute density, delivering the IOPS and throughput that enterprise-class GPU and TPU clusters demand, so every node in your AI Factory turns compute cycles into output, not idle time.
Drive Real-Time AI Performance
Inference latency isn’t a network problem — it’s a data movement problem. NeuralMesh eliminates the storage gap between your models and your GPUs, leveraging GDS-accelerated data paths and intelligent caching to cut time-to-first-token without sacrificing scale or adding infrastructure complexity.
Build Reliability Without Tradeoffs
Checkpointing shouldn’t be a gamble, and a node failure shouldn’t erase weeks of work. NeuralMesh writes and recovers checkpoints at wire speed — so when something goes wrong mid-training, you lose minutes, not weeks. Built on a containerized, software-defined architecture that scales resilience alongside performance, without compromise.
The AI Factory Blueprint: Designing for Scalable, Efficient Inference
As AI moves from pilot projects to full-lifecycle production, enterprises face a critical question: how do we operationalize AI without breaking performance or cost boundaries?
Watch this webinar to:
- Learn how to build infrastructure that is ready for inference, with hybrid architectures that bridge edge, core, and cloud deployments
- Hear real-world examples of how NVIDIA’s AI solutions and NeuralMesh remove traditional data and throughput constraints
- Get actionable advice on how to make architectural decisions around memory, data paths and token warehousing that influence TCO
Walk away with forward-thinking deployment strategies can future-proof your infrastructure investment.
Step Inside the World of AI Factories
Accelerate Your AI Factory with NeuralMesh AIDP
Deploy an AI Factory at scale with NeuralMesh AI Data Platform.
Operationalize AI Factories at Enterprise Scale
Turn data pipelines and shared inference context into a repeatable, operationalized reality with NeuralMesh AIDP.
Common Questions, Straight Answers
Most AI Factories stall because conventional storage was never engineered for the parallel, high-concurrency demands of Enterprise-scale AI. As enterprises invest in GPU infrastructure, many discover a hard production limit: GPUs sit idle 30–70% of the time because data pipelines cannot deliver data fast enough.
AI-ready storage is purpose-built to match the throughput, concurrency, and latency demands of modern Agentic Pipelines — not retrofit from general-purpose NAS or object storage architectures. By utilizing accelerated networking data moves from flash media to GPU memory with sub-millisecond latency — ensuring models train faster, inference stays responsive, and the entire AI Factory operates without data stalls.
Most organizations achieve less than 30% GPU utilization across their machine learning workloads — not because of the GPUs themselves, but because the data layer underneath them can’t keep up. Every second a GPU waits on storage is a wasted compute cycle. NeuralMesh uses GDS-accelerated data paths and distributed metadata architecture to drive GPU utilization toward its ceiling, maximizing the return on your most expensive infrastructure investment.
Every layer of the data stack is being stress-tested by AI workloads, and the infrastructure that powered the past decade of digital business wasn’t designed for the relentless demands of AI agents. Rather than requiring a full rip-and-replace, NeuralMesh integrates across your existing compute, networking, orchestration, and enterprise software layers — acting as the connective data layer that makes everything around it perform better.
Traditional storage, compute, and networking stacks were not built for AI’s extremely large model weights, high parallelism across GPUs, and vast volumes of data that must be moved, streamed, and cached efficiently. NAS chokes on the random I/O patterns of inference workloads, and object storage latency — measured in hundreds of milliseconds — is incompatible with the microsecond demands of real-time AI.
Industrialize the Path from Data to Discovery
The demand for AI value is only growing. It’s time to ensure your business is prepared to deliver it. Unleash the full power of your infrastructure and start building the foundation for the future of AI with the NeuralMesh AI Data Platform.