Real Long-Term Memory for AI: A 50-Million-Token Window That Is Faster and Cheaper Than Recompute

A memory layer called galahad-kv saves the key-value (KV) state of each roughly 16,000-token block to encrypted local NVMe disk and reloads it byte-exact without recomputation, and was tested on 50,000,000 tokens. The public package targets the fact that large language models can only use text fitting in their context window and recompute KV state for a prompt on every send. The test claims the approach is faster and cheaper than recompute for long-term memory.