Files
esh-pfi-infrastructure/stacks/voxtral
vh 68f3cd05fe voxtral: mount patched stage YAML to dodge hardcoded 0.8 GPU util; fish-s2: --half + streaming wins
Voxtral final fix (8th iteration):
* The bundled voxtral_tts.yaml hardcodes gpu_memory_utilization: 0.8
  on the language_model stage — overrides the CLI flag. Mounted a
  patched copy (0.4) at /etc/voxtral/voxtral_tts.yaml and pointed
  --stage-configs-path there.
* With Kyutai stopped to free 5 GB on the 3090, both stages fit
  (target 9.4 + 2.4 GB ≈ 11.8 GB; 17 GB free post-kyutai-stop).
* Voxtral now healthy on GPU 0 — bench: 1.9-2.7 s TTFB, real WAV.

Fish s2-pro optimization (per-request sweep, no model swap):
* `streaming: true` in request body drops TTFB from 7.7 s → 0.026 s
  (300×). Total time goes up ~1 s (chunked HTTP overhead) but
  perceived latency = TTFB. Use stream:true for any interactive use.
* `latency: "balanced"` actually slower than default — bad name; skip.
* `use_memory_cache: "on"` no measurable benefit.
* `chunk_length: 100` (default 200) no TTFB benefit non-streaming.
* Server-side `--half` (fp16 inference) added via compose `command`
  override — passes through start_server.sh's $@ unchanged into
  api_server.py. Should reduce total time too. Validation pending
  the post-restart bench.

Kyutai stopped to free GPU 0 budget — the bench numbers earlier
(3.4 s avg) were unimpressive vs Voxtral's 2.3 s in the same
multilingual slot. Kept the stack files for future re-deploy if
needed; just the running container is gone.
2026-04-28 00:35:55 -07:00
..

Voxtral TTS

mistralai/Voxtral-4B-TTS-2603 — Mistral AI's 4B open-weight streaming TTS, served via the vLLM-Omni production serving stack (Mistral co-developed). Released March 28, 2026.

⚠️ License

CC BY-NC. Personal use, research, and internal tooling are fine. Don't ship Voxtral output in any commercial product without re-licensing from Mistral. The other TTS in this fleet (Kokoro, Chatterbox, Fish S2-Pro, IndexTTS-2, Qwen3-TTS, CosyVoice) are all open-licensed and clean for commercial work.

Why this stack exists

Multilingual streaming with serious speed:

use case
Voxtral multilingual EN/FR/DE/ES/IT/PT/NL/HI streaming, 70 ms model latency
Kokoro low-latency English, fixed voice library
Chatterbox Turbo low-latency English w/ cloning + 9 paralinguistic tags
Fish Audio S2-Pro richest paralinguistic English (15k+ tags)
IndexTTS-2 English voice cloning + emotion vector / text control
Qwen3-TTS-1.7B English voice cloning (slow on official backend)
CosyVoice 3 multilingual (Chinese-leaning)
VibeVoice 1.5B long-form / multi-speaker dialogue

Voxtral fills the multilingual + low-latency + cloning slot that's been weak in the fleet (CosyVoice is multilingual but slow on English; nothing else is multilingual at all).

Headline numbers

  • 70 ms model latency for a typical 10 s sample (500-char input)
  • 9.7× realtime factor
  • 68.4% blind A/B win rate vs ElevenLabs Flash v2.5 in voice cloning evaluations
  • 8 languages: EN, FR, DE, ES, IT, PT, NL, HI

API

vLLM-Omni serves an OpenAI-compatible API at http://10.100.79.3:8197/v1:

# Single-shot synthesis.
curl -fsS -X POST http://10.100.79.3:8197/v1/audio/speech \
  -H 'Content-Type: application/json' \
  -d '{"model":"mistralai/Voxtral-4B-TTS-2603","input":"Hello there.","voice":"alloy","response_format":"wav"}' \
  > out.wav

# Streaming.
curl -fsS -X POST http://10.100.79.3:8197/v1/audio/speech \
  -H 'Content-Type: application/json' \
  -d '{"model":"mistralai/Voxtral-4B-TTS-2603","input":"long passage…","voice":"alloy","stream":true}' \
  | mpv --no-cache -

# vLLM-Omni standard endpoints.
curl http://10.100.79.3:8197/v1/models    # confirms model loaded
curl http://10.100.79.3:8197/v1/audio/voices  # built-in + cloned voices

Deploy

scripts/elway irv-ml1 --playbook playbooks/deploy-voxtral.yaml

First boot pulls Voxtral-4B (~8 GB BF16) into the HF cache + warms vLLM. Both are cached afterwards.

Hardware footprint

  • VRAM: ~16 GB practical (8 GB weights + KV + activation). Pinned to GPU 1 (RTX A6000) by default — comfortable headroom. The 3090's 24 GB CAN fit but it's tight for long streaming sessions.
  • Disk: ~8 GB for the Voxtral checkpoint + HF cache.

Voice library

Drop reference WAV / FLAC into /worktank/voxtral/voices/ on the host. The wrapper scans on request — no restart needed.