Audio

Check exact model cardOpen weights where releasedUpdated August 2026

Whisper Large V3 Turbo

Whisper Large V3 Turbo is a Whisper-family option for speech recognition, transcription, subtitles, meeting notes, and multilingual audio workflows.

OpenAI · Whisper

Editorial review

Reviewed byOpenSourcesAI EditorialLast updatedAugust 2026SourcesHugging Face model card (openai/whisper-large-v3-turbo)

Model checkpoints, context windows, provider support, local runtime compatibility, and license terms can change quickly. Verify the exact model card before production or commercial use.

Best for

Fast transcription and multilingual audio workflows

Who should use it

  • Fast transcription and multilingual audio workflows
  • Builders who want local or self-hosted testing options.

Common workflows

  • Fast transcription and multilingual audio workflows
  • audio workflows
  • transcription workflows
  • speech recognition workflows
  • local workflows

Deployment and hardware notes

Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.

License and usage notes

Check exact model card. Open weights where released. Verify the exact model card and license terms for the checkpoint or hosted provider you use.

Strengths

  • Open weights where released model option for Whisper workflows.
  • Fast transcription and multilingual audio workflows
  • Use the exact checkpoint and quantization that matches your hardware and latency target.

Limitations

  • Verify license, deployment requirements, runtime support, and fit on your own workload before production use.
  • Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.
  • Context window and limits: Fixed-length audio windows, not a token context.
  • Verify the exact model card, provider docs, license, and serving support before production use.

Local workflow notes

Use the exact checkpoint and quantization that matches your hardware and latency target.

Local runtimes: Ollama where supported, LM Studio where supported, llama.cpp where supported, Transformers

Platforms: Windows, macOS, Linux

Sources to verify

Hugging Face model card (openai/whisper-large-v3-turbo)

Related resources

Continue with model source notes, local tools, and implementation guides related to this model.

Whisper family hubAI tools directoryModel source notesModels directoryGuides library
Hardware~1.6 GB in fp16 (809M parameters)RuntimeOllama or LM Studio where supported, llama.cpp, Transformers, vLLMContextFixed-length audio windows, not a token contextLast updated2026
Hugging Face model card (openai/whisper-large-v3-turbo) →

Model ecosystem connections

Use these next-step links to move from this profile into related tools, comparisons, guides, stacks, and curated shortlists.

Recommended runtimes and tools

Ollama →LM Studio →llama.cpp →Transformers →vLLM →

Setup and deployment

Check if this model fits your GPU →What can 12GB VRAM run? →What is quantization? GGUF, Q4, Q8 explained →What is VRAM and how much do you need? →

Related model pages

Whisper family hub →Whisper Medium →Whisper Small →Whisper Tiny →Distil-Whisper Large V3 →Faster-Whisper Large V3 →Whisper Base →

Guides, stacks, and comparisons

All model comparisons →

Related models

Whisper MediumBuilders balancing local transcription quality and runtime cost for meetings, media, and batch audio processing.Whisper SmallLocal transcription setups that need a lighter model for captions, notes, and general speech-to-text tasks.Whisper TinyFast prototypes, edge-style experiments, and low-memory ASR tests where accuracy tradeoffs are acceptable.Distil-Whisper Large V3Builders who want strong multilingual transcription quality with a lighter checkpoint for production-style speech pipelines.Faster-Whisper Large V3Teams optimizing Whisper-style batch or local transcription pipelines where runtime efficiency and deployment control matter.Whisper BaseDevelopers who want a practical starting point for local captioning, voice notes, and CPU-leaning ASR experiments.

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