Breaking the Transformer Memory Wall: How Isometric Associative Memory (ISOM) Achieves O(1) Inference at 128K Context (Open Weights & Benchmarks)
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TL;DR: At 128K context, standard Transformer models run out of memory because the Key-Value cache never stops growing. I built ISOM (Isometric Associative Memory) —an open-source architecture that keeps inference memory completely flat, no matter how long the context gets. Verified on NVIDIA Tesla…
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Timeline · 2 reports
- 2026-09-09 20:29 · r/deeplearning
Beyond the Attention Wall: How Isometric Associative Memory (ISOM) Achieves Infinite Context with Constant Memory - 2026-09-09 20:18 · r/machinelearningnews
Breaking the Transformer Memory Wall: How Isometric Associative Memory (ISOM) Achieves O(1) Inference at 128K Context (Open Weights & Benchmarks)