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RAM Coffers– I built conditional memory for LLMs 27 days before DeepSeek'sEngram

2 points by AutoJanitor 8mo ago

AutoJanitor 8mo ago
I'm an independent researcher running LLMs on an IBM POWER8 server (320GB RAM, vintage 2015 hardware). On December 16, 2025, I developed "RAM Coffers" - a NUMA-distributed conditional memory system that selectively houses model weights in RAM banks with resonance-based routing for O(1) knowledge retrieval. On January 12, 2026, DeepSeek published their "Engram" paper (arXiv:2601.07372) describing the same core concept: separating static knowledge from dynamic computation via O(1) lookup. Same idea. 27 days apart. No connection. DOI: 10.6084/m9.figshare.31093429 GitHub: https://github.com/Scottcjn/ram-coffers DeepSeek paper: https://arxiv.org/abs/2601.07372 Running on "obsolete" POWER8 hardware, I hit 147 tokens/sec on TinyLlama - 8.8x faster than stock llama.cpp. Sometimes the garage beats the lab.

2 points by AutoJanitor 8mo ago

I'm an independent researcher running LLMs on an IBM POWER8 server (320GB RAM, vintage 2015 hardware). On December 16, 2025, I developed "RAM Coffers" - a NUMA-distributed conditional memory system that selectively houses model weights in RAM banks with resonance-based routing for O(1) knowledge retrieval. On January 12, 2026, DeepSeek published their "Engram" paper (arXiv:2601.07372) describing the same core concept: separating static knowledge from dynamic computation via O(1) lookup. Same idea. 27 days apart. No connection. DOI: 10.6084/m9.figshare.31093429 GitHub: https://github.com/Scottcjn/ram-coffers DeepSeek paper: https://arxiv.org/abs/2601.07372 Running on "obsolete" POWER8 hardware, I hit 147 tokens/sec on TinyLlama - 8.8x faster than stock llama.cpp. Sometimes the garage beats the lab.