DATASHEET // FASTER-WHISPER
faster-whisper
Whisper, 4× faster, same accuracy. CTranslate2 backend.
OPEN SOURCE2–6 GB VRAMRuns locally
Actually FreeNo SignupOpen SourceWatermark-FreeHobbyist-OK
Visit faster-whisperUPDATED 2026-04-25 · DIRECT LINK
HARDWARE REQUIREMENTS //
Runs locally · Entry GPU (6–8 GB)
Min VRAM
2 GB
Rec. VRAM
6 GB
Min RAM
8 GB
Rec. RAM
16 GB
Disk
8 GB
GPU class
Entry GPU
11.8+Apple Silicon ✓CPU-CapableQuant: INT8, FP16
`large-v3` runs in 4-5 GB VRAM with INT8.
[ EDITORIAL PICK ]
Why we recommend faster-whisper
- Open source
Source is public — you can audit it, fork it, and you'll never lose access to your workflows if faster-whisper the company changes direction.
- Runs on 2 GB
Fits on entry-level cards (GTX 1660, RTX 3050, RTX 4060). Rare for this category.
- Apple Silicon
Native Metal / MPS support — runs on M-series Macs without CUDA gymnastics.
- Top-tier pick
Power-user score 89/100 — consistently rated highly by people who use this every day, not just benchmark chasers.
[ EVIDENCE NOTE ]
Documentation-led datasheet
This page summarizes upstream documentation, release information, and editorially reviewed catalogue fields. It is not presented as a hands-on benchmark. Verify changing requirements at the official project; report stale data through our corrections channel.
AT-A-GLANCE SIGNALS //
DERIVED FROM THIS PAGE'S DATA- Install difficultyEasy
Runs CPU-only — no CUDA / driver gymnastics required.
- Hardware comfortEntry-level
Fits on 2 GB cards — GTX 1660 / RTX 3050 territory.
- EcosystemOpen source
Source is public — auditable and forkable, no vendor lock.
- VerificationRecent
Catalogue entry last updated 82 days ago — re-verification due soon.
[ COMMUNITY GUIDES & WORKFLOWS ]
Tutorials & deep-dives for faster-whisper
Hand-picked from YouTube, Reddit, GitHub, and the wider web. Each link goes straight to the source — we don't intercept or rewrite anything.
[ MORE IN THIS NICHE ]
Other local llm runners tools we rate
Three picks across different tradeoffs — so you don't end up with three near-clones of faster-whisper.
What is faster-whisper?
faster-whisper reimplements Whisper inference on top of CTranslate2 (C++/CUDA), delivering 4× speedup over the reference PyTorch impl at the same word error rate. INT8 quantisation halves VRAM again with no measurable accuracy loss. The default Whisper backend for anyone who's measured it.
Pros & cons
✓ PROS
- 4× faster than reference Whisper at equal accuracy
- INT8 quantisation cuts VRAM in half
- Drop-in CLI compatible with the reference
– CONS
- No diarisation built in — pair with WhisperX or pyannote
- Setup involves CUDA + cuDNN library paths that occasionally fight
What's actually free?
MIT.
✓ Actually FreeNo SignupOpen SourceWatermark-Free
