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DATASHEET // WHISPER

OpenAI Whisper

The reference open-source speech-to-text model.

OPEN SOURCE2–10 GB VRAMRuns locally
Actually FreeNo SignupOpen SourceWatermark-FreeHobbyist-OKAPI
Visit OpenAI WhisperUPDATED 2025-11-15 · DIRECT LINK
github.com/openai/whisper
OpenAI Whisper — preview image

HARDWARE REQUIREMENTS //

Runs locally · Entry GPU (6–8 GB)

2–10 GB VRAM
Min VRAM
2 GB
Rec. VRAM
10 GB
Min RAM
8 GB
Rec. RAM
16 GB
Disk
10 GB
GPU class
Entry GPU
11.3+Apple Silicon ✓CPU-CapableQuant: FP16

`tiny` runs on CPU; `large-v3` needs ~10 GB VRAM.

[ EDITORIAL PICK ]

Why we recommend OpenAI Whisper

DERIVED FROM METADATA — NOT SPONSORED
  • Open source

    Source is public — you can audit it, fork it, and you'll never lose access to your workflows if OpenAI 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 90/100 — 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.

Memory guide →

AT-A-GLANCE SIGNALS //

DERIVED FROM THIS PAGE'S DATA
  • Install difficulty
    Easy

    Runs CPU-only — no CUDA / driver gymnastics required.

  • Hardware comfort
    Entry-level

    Fits on 2 GB cards — GTX 1660 / RTX 3050 territory.

  • Ecosystem
    Strong devkit

    Open-source AND ships an API — easy to integrate, possible to host yourself.

  • Verification
    Stale

    243 days since the last refresh — treat hardware numbers as a floor, not a ceiling.

[ COMMUNITY GUIDES & WORKFLOWS ]

Tutorials & deep-dives for OpenAI 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 OpenAI Whisper.

LIGHTEST HARDWARE //

Transformers

The library every LLM ships against first.

OPEN SOURCE2–24 GB VRAM
BEST FREE OPTION //

llama.cpp

The C++ inference engine powering most local LLMs.

OPEN SOURCECPU-CAPABLE
TOP QUALITY //

Ollama

One-command local LLM runtime.

OPEN SOURCECPU-CAPABLE

What is OpenAI Whisper?

Whisper is the open-weight transcription model that reset expectations for ASR. Five sizes (tiny → large-v3), 99 languages, robust to noise and accents, runs fully offline. The reference implementation is slow; in practice you'll want faster-whisper, whisper.cpp, or WhisperX. Listed here as the canonical entry.

Pros & cons

✓ PROS

  • Multilingual ASR that genuinely works across 99 languages
  • Five size points let you trade speed for accuracy
  • Active fork ecosystem (faster-whisper, WhisperX, whisper.cpp)

– CONS

  • Reference impl in PyTorch is 4-6× slower than the optimised forks
  • Hallucinates plausible text during long silences

What's actually free?

MIT, weights freely downloadable from HuggingFace.

✓ Actually FreeNo SignupOpen SourceWatermark-Free

Alternatives

faster-whisper

Whisper, 4× faster, same accuracy. CTranslate2 backend.

OPEN SOURCE2–6 GB VRAM
VRAM fit2–6 GB

WhisperX

Whisper + speaker diarisation + word-level timestamps.

OPEN SOURCE4–8 GB VRAM
VRAM fit4–8 GB