WEKA introduced something that could seriously change how AI workloads operate at scale. The company’s new product, NeuralMesh Axon, is a storage system specifically designed to meet the insane demands of exascale AI. And instead of following the same old approach, it does something smarter it connects directly to GPU servers and AI factories.

What does that mean in plain English? Faster results, fewer delays, and better use of expensive hardware that would otherwise sit around doing nothing.

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Smart Storage That Actually Understands AI

NeuralMesh Axon builds on WEKA’s earlier storage tech, but now it’s optimized for containerized microservices and real-world AI workflows. Especially when paired with NVIDIA AI Enterprise tools, the system really comes alive.

It doesn’t just improve training it speeds up inference too. The time it takes for an AI model to generate its first token drops sharply, and the system can push out more tokens per second overall. For AI developers, that’s a big deal.

So What’s the Problem It Solves?

AI teams today are trying to train massive models, think language models the size of cities, and most storage systems just aren’t built for it. They slow everything down. There’s too much replication, too much wasted NVMe capacity, and too many bottlenecks.

The result? GPU servers sit around waiting. You spend money on the hardware, but you don’t actually use it efficiently. That’s not just frustrating; it’s expensive.

NeuralMesh Axon flips that problem on its head.

A Different Kind of Setup

Instead of separating storage and compute, WEKA blends them together. NeuralMesh Axon uses existing NVMe drives, spare CPU cores, and your current network to build something fast like really fast. Latency drops to microseconds. That means no more long waits for data to arrive.

And thanks to a feature called Augmented Memory Grid, it can deliver near-memory speeds for key-value cache loads. That helps inference systems respond faster, even when the models are huge.

Designed for Teams Who Can’t Afford to Wait

This isn’t a solution you grow into. It’s made for teams that already operate at a massive scale. AI cloud providers, large enterprises, and regional AI hub if you’re already building serious AI infrastructure, this is probably for you.

What It Looks Like in Action

One of WEKA’s early adopters, Cohere, rolled out NeuralMesh Axon in its cloud setup to fix a major problem: underutilized GPUs. They needed to unify their infrastructure and remove the lag from their training and inference steps.

“We went from five-minute inference times to 15 seconds. That alone was a game-changer,” said Autumn Moulder, VP of Engineering at Cohere. “Checkpoints are 10 times faster now, and we’re pushing models like North to market way faster.”

They’re currently working with CoreWeave Cloud to expand this setup for secure AI agents.

CoreWeave’s CTO, Peter Salanki, added that their integration with NeuralMesh Axon lets each GPU server hit 30+ GB/s read speeds and a million IOPS. Translation: no more data bottlenecks, and GPUs stay busy doing what they’re built for.

Even NVIDIA Thinks It’s a Big Deal

Marc Hamilton, a VP at NVIDIA, weighed in: “The future of AI doesn’t just need more compute it needs smarter infrastructure. Tools like NeuralMesh Axon give organizations the edge they need to move fast, at scale, with better efficiency.”

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What You Actually Get

  • More Memory, Faster Tokens
    Tight integration with GPU memory means larger AI models run smoother and respond quicker 20x faster to the first token, in fact.
  • Up to 90% GPU Utilization
    Some customers are hitting three times the industry average for model training efficiency.
  • Scale on Your Terms
    You don’t have to wait to grow. Axon supports massive scale now, with steady performance across clouds and hybrid environments.
  • No Extra Baggage
    It works with Kubernetes, containers, and what you already have no need to bolt on more infrastructure.

FAQs

1. What exactly are exascale AI workloads?

These are AI tasks so large and complex that they require infrastructure capable of handling exabytes of data and trillions of parameters. It’s the kind of thing used in massive language models and next-gen generative AI.

2. Why do older storage systems fail at this scale?

They weren’t built for real-time, high-volume data. Traditional systems create delays, overuse resources like NVMe, and underutilize expensive GPUs.

3. How does NeuralMesh Axon fix this?

It eliminates the divide between compute and storage. Data gets to the GPU faster, and everything works as a unified system making AI pipelines quicker and smoother.

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