* feat(server)!: add --gpu to choose the GPU on multi-GPU machines
--gpu takes a GPU UUID (as nvidia-smi -L prints) or an nvidia-smi index.
It is applied as CUDA_VISIBLE_DEVICES in the parent before the workers spawn, so the engine still binds cuda:<rank>.
/v1/stats reports the engine's GPU under "gpus".
ft bench bw takes the same --gpu and writes one profile per GPU (benchbw/<gpu-uuid>.json); the legacy benchbw.json is still read by GPU name.
The daemon's /bench/profile returns the running serve's GPU profile.
BREAKING CHANGE: ft checkpoint --device is removed; use --gpu.
* refactor!: resolve --gpu via NVML and bind by UUID, not CUDA_VISIBLE_DEVICES
BREAKING CHANGE: ft bench bw --device is removed; use --gpu.
* refactor: name the id namespaces and drop the multi-device e4m3 scan
Splits the published id into _assigned_physical (UUID) and
_assigned_visible (CUDA ordinal). One process runs on one GPU, so
e4m3_native() judges that card instead of scanning every visible device.
The Desktop paragraph promised a GUI for running models and tuning the engine
without showing either. The shot is the console with DeepSeek-V4-Flash FP4
resident on a 32 GiB card, so the cache sliders carry real numbers: 512K KV
tokens for 3.40 GiB against 1024 of 11008 expert slots for 12.8 GiB.
Framed as a floating window (rounded, drop shadow, transparent margin) so it
sits on both the light and dark README themes.
The request ring only carried whole-request duration, so /v1/stats exposed no
first-token latency — the number that says how responsive a local model feels,
independent of decode speed. Streaming generations now stamp the first delta
they emit; non-streaming rows carry None and are skipped by the mean, so a
/health probe can never drag it toward zero.
Surfaced as requests.ttft_mean_ms in /v1/stats and in `ft ctl stats`.
Xiaoze Fan