CoMem cuts LLM memory use from 89 GB to 18 GB for 128k-token contexts

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Researchers published a paper on July 30, 2026, introducing CoMem (Comprehension Memory), a new approach to long-context inference in large language models. The method exploits a functional split in transformer networks, where lower layers build semantic understanding and upper layers handle prediction, caching hidden states only at that intermediate boundary. At query time, CoMem retrieves the most relevant cached states and recomputes only the upper layers, rather than processing the full context again. Tested on the Qwen3-8B architecture with 128k tokens, CoMem reduced VRAM usage from 89.36 GB to 18.26 GB and delivered a 7.83x prefill speedup while maintaining strong benchmark scores. The approach aims to address memory and accuracy limitations seen in both standard KV caching and retrieval-augmented generation pipelines.

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