Supermicro, IBM and Nvidia unveil KV-cache platform for faster AI inference

The joint system stores AI models' working memory on shared flash, and benchmarks show up to 56x faster first responses on long prompts Super Micro Computer, IBM and Nvidia have teamed up on a fix for one of AI’s more expensive habits: doing the same math twice. On November 5, 2026, the three companies announced a joint key-value (KV) cache platform built for large-scale AI inference. The goal is to stop GPUs from recomputing context they have already processed. Benchmarks from a validated reference architecture show time-to-first-token speedups of up to 56x on contexts reaching 130,000 tokens. What the three companies actually built When a model reads a prompt, it builds an internal record of that context, known as the KV cache. If the cache gets evicted from memory, the GPU has to rebuild it from scratch on the next turn. That rebuilding is called recomputation, and it burns expensive GPU cycles. The new platform is designed to keep that record around. It combines four pieces of hardware and software: Supermicro’s Petascale storage servers provide the physical storage layer. IBM Storage Scale software manages high-performance shared storage across the system. Nvidia Dynamo handle...