Summary
Fahd Mirza runs a controlled needle-in-a-haystack experiment to test whether Luce KVFlash can reliably retrieve a specific fact from a 256,000-token context — the entirety of War and Peace — while keeping GPU memory consumption practical on a single NVIDIA RTX A6000 with 48GB VRAM. The setup: a secret passphrase is hidden around line 5,000 of the novel, and the model must answer a query about it from the end of the document.
The experiment has two runs that tell the whole story. With recency-only (LRU) paging — keeping only the most recent 16K-token pool on the GPU — the model completely fails, hallucinating a passphrase from Red Dead Redemption because the actual answer was evicted long before the query arrives. With relevance-scored paging enabled, a 0.6B quantized draft model reads the query, scores every chunk sitting in system RAM, and pulls the relevant section back onto the GPU even though it was written near the beginning of the novel. The second run succeeds cleanly.
Mirza explains the two-component architecture clearly: paging buys memory efficiency by offloading most of the KV cache to system RAM, while the small draft model ensures retrieval accuracy by deciding which chunks deserve a slot in the GPU pool. Together they make 256K-token inference practical on prosumer hardware — a meaningful step for anyone running large models locally who needs to work with full-length books, codebases, or long document sets without paying for cloud API access.
📺 Source: Fahd Mirza · Published June 17, 2026
🏷️ Format: Hands On Build
