{"version":1,"type":"story","url":"https://digestai.news/story/more-performance-from-every-byte-how-full-stack-codesign-solves-ais-me","json":"https://digestai.news/story/more-performance-from-every-byte-how-full-stack-codesign-solves-ais-me.json","markdown":"https://digestai.news/story/more-performance-from-every-byte-how-full-stack-codesign-solves-ais-me.md","slug":"more-performance-from-every-byte-how-full-stack-codesign-solves-ais-me","headline":"Supermicro, IBM and NVIDIA launch shared KV cache for long-context AI inference","summary":"Supermicro, IBM and NVIDIA announced a **KV cache storage solution** on November 5, 2026, to handle reusable AI inference context beyond GPU memory. The system combines Supermicro’s Petascale all-flash storage servers (using AMD EPYC 9005 processors), IBM Storage Scale software, NVIDIA’s Dynamo inference framework, and Spectrum-X Ethernet networking. It targets multi-turn applications, coding assistants, multimodal AI, and long-context retrieval-augmented generation, where large datasets (codebases, documents, images, or conversation histories) exceed GPU/CPU memory limits.\n\nThe companies cite **up to 56x faster time to first token** on a 130,000-token prompt, based on prior benchmarking (June–August 2026). A 200-request test with 24M tokens and 825 GB of KV cache data showed throughput rising from **0.19 to 4.26 requests per second** and total processing time dropping from **1,048.56 to 46.94 seconds**. Under noisy-neighbor conditions, throughput dropped **18%** but remained efficient. The architecture supports shared access across GPU nodes, non-disruptive scaling, and cache-aware routing via Dynamo’s KV Block Manager and NIXL transfer library. Deployment sizing depends on workloads, with Supermicro offering factory rack integration to cut deployment time from weeks to days.","keyPoints":["Supermicro, IBM and NVIDIA launched a shared KV cache storage solution for long-context AI inference on November 5, 2026","Benchmarks show up to 56x faster time to first token on 130,000-token prompts, with throughput improving from 0.19 to 4.26 requests per second","System uses Supermicro Petascale servers, IBM Storage Scale, NVIDIA Dynamo, and Spectrum-X networking for multi-turn and agentic applications"],"whyItMatters":"This storage-backed KV cache could reduce inference costs and latency for large-scale AI workloads by reusing context across requests, easing memory constraints for long-context models.","category":{"slug":"hardware","name":"Hardware & Compute","url":"https://digestai.news/category/hardware"},"entities":{"companies":["Supermicro","IBM","NVIDIA"],"models":[],"people":[]},"firstPublishedAt":"2026-11-05T16:00:00Z","updatedAt":"2026-11-05T16:00:00Z","sourceCount":2,"hasPrimarySource":true,"sources":[{"outlet":"Unite.AI","title":"Supermicro Launches Shared KV Cache Storage for Long-Context AI","url":"https://unite.ai/supermicro-launches-shared-kv-cache-storage-for-long-context-ai","publishedAt":"2026-11-05T14:40:03Z","type":"press","primary":false,"lead":true},{"outlet":"NVIDIA Blog","title":"More Performance From Every Byte: How Full-Stack Codesign Solves AI’s Memory Challenge","url":"https://blogs.nvidia.com/blog/memory-full-stack-codesign","publishedAt":"2026-11-05T16:00:00Z","type":"primary","primary":true,"lead":false}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"Supermicro, IBM and NVIDIA launch shared KV cache for long-context AI inference\", 5 November 2026, https://digestai.news/story/more-performance-from-every-byte-how-full-stack-codesign-solves-ais-me","publisher":"Digest AI","title":"Supermicro, IBM and NVIDIA launch shared KV cache for long-context AI inference","datePublished":"2026-11-05T16:00:00Z","url":"https://digestai.news/story/more-performance-from-every-byte-how-full-stack-codesign-solves-ais-me"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}