PEAK:AIO, the information infrastructure pioneer redefining AI-first information acceleration, immediately unveiled the primary devoted resolution to unify KVCache acceleration and GPU reminiscence growth for large-scale AI workloads, together with inference, agentic methods, and mannequin creation.
As AI workloads evolve past static prompts into dynamic context streams, mannequin creation pipelines, and long-running brokers, infrastructure should evolve, too.
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“Whether or not you’re deploying brokers that assume throughout classes or scaling towards million-token context home windows, the place reminiscence calls for can exceed 500GB per mannequin, this equipment makes it attainable by treating token historical past as reminiscence, not storage,” stated Eyal Lemberger, Chief AI Strategist and Co-Founding father of PEAK:AIO “It’s time for reminiscence to scale like compute has.”
As transformer fashions develop in measurement and context, AI pipelines face two vital limitations: KVCache inefficiency and GPU reminiscence saturation. Till now, distributors have retrofitted legacy storage stacks or overextended NVMe to delay the inevitable. PEAK:AIO’s new 1U Token Reminiscence Function modifications that by constructing for reminiscence, not recordsdata.
The First Token-Centric Structure Constructed for Scalable AI
Powered by CXL reminiscence and built-in with Gen5 NVMe and GPUDirect RDMA, PEAK:AIO’s function delivers as much as 150 GB/sec sustained throughput with sub-5 microsecond latency. It permits:
- KVCache reuse throughout classes, fashions, and nodes
- Context-window growth for longer LLM historical past
- GPU reminiscence offload through true CXL tiering
- Extremely-low latency entry utilizing RDMA over NVMe-oF
That is the primary function that treats token reminiscence as infrastructure relatively than storage, permitting groups to cache token historical past, consideration maps, and streaming information at memory-class latency.
Not like passive NVMe-based storage, PEAK:AIO’s structure aligns straight with NVIDIA’s KVCache reuse and reminiscence reclaim fashions. This supplies plug-in help for groups constructing on TensorRT-LLM or Triton, accelerating inference with minimal integration effort. By harnessing true CXL memory-class efficiency, it delivers what others can’t: token reminiscence that behaves like RAM, not recordsdata.
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“Whereas others are bending file methods to behave like reminiscence, we constructed infrastructure that behaves like reminiscence, as a result of that’s what trendy AI wants,” continued Lemberger. “At scale, it’s not about saving recordsdata; it’s about protecting each token accessible in microseconds. That may be a reminiscence drawback, and we solved it at embracing the newest silicon layer.”
The absolutely software-defined resolution makes use of off-the-shelf servers is anticipated to enter manufacturing by Q3. To debate early entry, technical session, or how PEAK:AIO can help AI infrastructure wants,
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