Sietse Schelpe

The authors release the public package galahad-kv, which writes the KV state a model computes for each 16,000-token block to encrypted local NVMe and later restores any block byte-exact with zero recompute; on a 50-million-token real corpus served through vLLM on one NVIDIA H100, all 100 probed blocks were restored without recompute for both Gemma 4 12B and 31B, restores were 2.8–4.3× faster and used 8.8–12.3× less GPU energy than recompute, VRAM stayed flat, and planted-fact recall was 82/100 and 98/100 with zero fabrication.

The authors release the public package galahad-kv, which writes the KV state a model computes for each 16,000-token block to encrypted local NVMe and later restores any block byte-exact with zero recompute; on a 50-million-token real corpus served through vLLM on one NVIDIA H100, all 100 probed blocks were restored without recompute for both Gemma 4 12B and 31B, restores were 2.8–4.3× faster and used 8.8–12.3× less GPU energy than recompute, VRAM stayed flat, and planted-fact recall was 82/100 and 98/100 with zero fabrication.

Sietse Schelpe of Corbenic AI presents Galahad, a memory layer in which Taliesin saves and reloads byte-exact KV state and Blaise passes the model only the section a question needs; on a recall test with 100 facts hidden in a 97,000-token corpus (Gemma 4 31B), Taliesin alone answered 98 of 100 on llama.cpp at 3.0 s and 572 J per question against 10 of 100, 9.3 s and 2,754 J without Galahad, and with Blaise added the model answered 100 of 100 on all three runtimes at 0.59–0.64 s and 200–213 J, while a tuned RAGFlow pipeline answered 77.

Sietse Schelpe of Corbenic AI presents Galahad, a memory layer in which Taliesin saves and reloads byte-exact KV state and Blaise passes the model only the section a question needs; on a recall test with 100 facts hidden in a 97,000-token corpus (Gemma 4 31B), Taliesin alone answered 98 of 100 on llama.cpp at 3.0 s and 572 J per question against 10 of 100, 9.3 s and 2,754 J without Galahad, and with Blaise added the model answered 100 of 100 on all three runtimes at 0.59–0.64 s and 200–213 J, while a tuned RAGFlow pipeline answered 77.