Audio model · whisper
Whisper large-v3 requirements
Speech to text · 1.55B params · int8 (faster-whisper) / fp16 (whisper.cpp) · released Nov 2023. Light enough to run on CPU, no GPU required.
MIT per the OpenAI Whisper repo (the HuggingFace card labels large-v3 Apache-2.0; the repo is the source of truth).
Run it
Whisper large-v3 runs in whisper.cpp, faster-whisper, MacWhisper or WhisperX at int8. It runs CPU-only, and the smaller tiers are fast enough for real-time use on a laptop or phone.
Which devices can run Whisper large-v3?
Apple Silicon Macs
- Apple M1 (8GB) Yes
- Apple M2 (16GB) Yes
- Apple M4 (16GB) Yes
- Apple M5 (16GB) Yes
- Apple M3 Pro (18GB) Yes
- Apple M4 (24GB) Yes
- Apple M4 Pro (24GB) Yes
- Apple M5 (32GB) Yes
- Apple M4 Pro (48GB) Yes
- Apple M5 Pro (48GB) Yes
- Apple M4 Max (64GB) Yes
- Apple M4 Max (128GB) Yes
- Apple M5 Max (128GB) Yes
- Apple M3 Ultra (256GB) Yes
RAM-only laptops
iPhone & iPad
Android
NVIDIA GPUs
AMD GPUs
FAQ
How much memory does Whisper large-v3 need?
At int8 it consumes ~2.5 GB. It runs on CPU, so a GPU is optional.
Can Whisper large-v3 run on a phone or CPU?
Yes for CPU. The smaller tiers are light enough for real-time use, and on-device phone runtimes are available.
Can I use Whisper large-v3 commercially?
Yes. Whisper large-v3 is licensed MIT, which permits commercial use.
Most accurate Whisper (1.55B). Via faster-whisper int8 it peaks at ~2.5GB VRAM (2,953MB measured, beam=5); via whisper.cpp the ggml model is 2.9GB and runtime RAM ~3.9GB. Runs CPU-only and on Apple Silicon (Metal) and phones (whisper.cpp). The openai/whisper README's ~10GB figure is the original fp32 PyTorch path, not whisper.cpp/faster-whisper. License is MIT per the OpenAI repo. Sources: OpenAI Whisper repo, faster-whisper benchmark issue, whisper.cpp README.
Sources
Memory is a sourced peak-usage anchor at int8 (composed from reported sizes, not a single measurement), validated 2026-06-15. See methodology.