RedKnot-MLA: prefix-cache-style reuse finally works for MLA models

MLA packs attention into a single latent KV stream, removing the per-head cache boundary that head-level reuse relies on. RedKnot-MLA processes each immutable document offline at canonical position 0 (keeping certified local-head contributions as MLA-Off); at serving time, query-side RoPE relocation restores request positions while recomputing a small global-head set and protected local token rows (MLA-On), merged before a shared output projection — the packed latents are never split. On DeepSeek-V4-Flash (37 reusable layers, 56/8 local/global split) it reaches 75.29% of the analytical head-row reuse ceiling (78.89% for Pro-0813); hot-artifact TTFT improves 2.02-3.84x, with 78.7-79.5% compute savings per major operator at 256K, with small quality deltas (aggregate F1 +3.24, EM +4.16; one dataset -2.81 F1). A ~2.0x QPS figure is labeled preliminary rather than archival.

Standard prefix caching is blind on MLA models (DeepSeek V4/V4.1 serving), which is exactly where RAG and multi-agent workloads reuse documents. RedKnot-MLA brings document-level reuse back to that architecture family — directly relevant to anyone serving DeepSeek models with shared corpora.