whisper-large-v3-turbo — MLX 8-bit
Whisper large-v3-turbo in the mlx-whisper layout, quantized to 8-bit for on-device speech-to-text on Apple silicon.
How it was made
From mlx-community/whisper-large-v3-turbo
(fp16): every Linear and Embedding layer (233 modules) quantized with mlx.nn.quantize —
affine, 8-bit, group size 64 — the recipe of mlx-examples' whisper/convert.py.
config.json is the mlx-whisper "dims" config with a "quantization": {"group_size": 64, "bits": 8} block.
Files
weights.safetensors— quantized weights (864 MB)config.json
The tokenizer is not included: use the standard openai/whisper-large-v3 tokenizer.
Why MLX
It loads straight onto the Metal GPU with no Neural Engine compile — about half a second from disk, where a Core ML Whisper spends minutes specializing on first launch and after every app install or update.
Accuracy
Measured on 1,000 Russian clips (500 FLEURS, 500 GOLOS), language auto-detected, greedy decode, on an M4 Mac:
| build | download | FLEURS WER | GOLOS WER | speed |
|---|---|---|---|---|
8-bit (transcriber-app/whisper-large-v3-turbo-mlx-8bit) | 864 MB | 4.60 % | 17.64 % | 6.7× |
| 6-bit (not published) | 664 MB | 4.70 % | 18.10 % | 6.8× |
5-bit (transcriber-app/whisper-large-v3-turbo-mlx-5bit) | 564 MB | 4.71 % | 17.64 % | 7.0× |
| 4-bit (not published) | 463 MB | 5.23 % | 17.39 % | 6.9× |
| Core ML, Argmax's 4-bit palettized build (for reference) | 633 MB | 4.85 % | 18.80 % | 11.8× |
8-bit is the most accurate build. The 5-bit build gives up about a tenth of a FLEURS point for a third less download; below 5 bits accuracy drops clearly.