Voxtral Mini 4B Realtime Q4 GGUF

Q4_0 quantized weights for Voxtral Mini 4B Realtime (ASR) in GGUF format. For use with voxtral-mini-realtime-rs.

Try the browser demo — runs entirely client-side via WASM + WebGPU.

Files

Model Details

• Base model: mistralai/Voxtral-Mini-4B-Realtime-2602
• Quantization: Q4_0 (4-bit, 18 bytes per 32 elements)
• File size: ~2.5 GB (vs ~9 GB BF16 original)
• Format: GGUF v3
• Inference: Burn ML framework with custom WGSL compute shaders
• WER: 8.49% on FLEURS English (647 utterances), vs. Mistral's reported 4.90% at f32

Benchmarks

NVIDIA DGX Spark (GB10, LPDDR5x), 16s test audio:

Q4 decode is 4.2x faster than BF16. Custom WGSL shaders with shared-memory tiled kernel for decode, naive kernel for encode.

Usage

Native CLI

# Download
uv run --with huggingface_hub \
  hf download TrevorJS/voxtral-mini-realtime-gguf --local-dir models

# Transcribe (unified voxtral CLI)
cargo run --release --features "wgpu,cli,hub" --bin voxtral -- \
  transcribe --audio audio.wav --gguf models/voxtral-q4.gguf

Browser (WASM + WebGPU)

Shards are pre-split for browser loading. The ASR demo loads them automatically.

For local dev:

wasm-pack build --target web --no-default-features --features wasm
bun serve.mjs  # serves shards from models/voxtral-q4-shards/

Architecture

Audio (16kHz) → Mel [B, 128, T] → Encoder [B, T/4, 1280]
  → Reshape [B, T/16, 5120] → Adapter [B, T/16, 3072]
    → Decoder (autoregressive, 26 layers, GQA 32Q/8KV)
      → Token IDs → Text

WASM Constraints Solved

1. 2 GB allocation limit — ShardedCursor over multiple Vec
2. 4 GB address space — Two-phase loading (parse → drop reader → finalize)
3. 1.5 GiB embedding table — Q4 on GPU + CPU bytes for row lookups
4. No sync GPU readback — into_data_async().await throughout
5. 256 workgroup limit — Patched cubecl-wgpu to cap reduce kernels

Related

• Code: TrevorS/voxtral-mini-realtime-rs
• TTS Model: TrevorJS/voxtral-tts-q4-gguf
• ASR Demo: TrevorJS/voxtral-mini-realtime
• TTS Demo: TrevorJS/voxtral-4b-tts