Expose the two speed knobs in the /admin Settings panel instead of TOML-only:
- [cmf].gpu (new) -> exports CMF_GPU=1 to the managed cortiq serve (Metal/wgpu).
On Apple Silicon decode is memory-bandwidth bound so this is ~neutral for
generation (helps prefill); off by default.
- [cmf].threads (added earlier) now has a UI number input.
- config.rs: CmfCfg.gpu bool (serde default false).
- cmf_runtime.rs: spawn_serve sets CMF_GPU=1 when gpu is on.
- admin/mod.rs: put_settings flags needs_restart on threads/gpu change (the
server is spawned once at startup with those env vars).
- settings.js: threads input + gpu checkbox + save payload + speed hint.
- i18n en/ru: threads/gpu/speedNote keys (other locales fall back to en, as the
whole cmf section already does there).
The gateway spawned `cortiq serve` with no thread hint, so the runtime ran the
decode loop single-threaded (~0.25 tok/s on a 15B CPU model). Export CMF_THREADS
from a new [cmf].threads config field (default 8) in spawn_serve.
Measured: 0.25 -> 0.81 tok/s (~3.2x) on qwopus-vbit7 (15B). Scaling is sublinear
(GatedDeltaNet state recurrence is serial per token, Amdahl-bound); a real jump
needs the GPU path.
Run models in the CMF (.cmf) format locally, via the native `cortiq`
CLI from crates.io — no Python and no external model server.
- cmf_runtime: install/update `cortiq-cli` from crates.io, spawn and
health-check `cortiq serve` as a managed local backend.
- import: model-import jobs shell out to the native `cortiq convert`
(HF repo -> .cmf, quant mapped Q8_2F->q8_2f / Q8_ROW->q8 / Q4->q4);
the Python converter is used only for advanced/exotic options.
- pipeline: local-first routing — serve from the local CMF model when
the router is disabled/unreachable, with a "local only" hard switch.
- registry/state: register the local CMF server as an OpenAI provider
and track local candidates.
- config: data-dir resolution + CmfCfg (auto-install/update, managed
server, host/port/model) and relaxed validation for the managed model.
- admin + settings UI (en/ru): "Local models (CMF)" settings card.
- cache.rs: cosine-similarity store (threshold, TTL, ring buffer), partitioned by
routing signature; tracks hit rate and model cost saved
- pipeline: embed routing text, look up before the model call (route_source=cache on
hit), insert successful responses; stats record cache hits
- config [cache] + admin settings card; dashboard shows hit-rate and savings
- embedding model resolved from the pool (or first kind="embedding")
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
infosave2007