Whisper Large V3
Whisper · MIT
OpenAI's best open speech-to-text model. Supports 99 languages. Near-human accuracy for English. Low VRAM requirements; runs on any GPU. Useful for builders who need voice-to-code or meeting transcription.
- Parameters
- 1.55B
- Architecture
- Dense
- Context
- 448 tokens
- Released
- 2023-11-06
- Engines
- whisper.cpp, faster-whisper
Parameters
1.55B
VRAM
1.5 GB
Context
0K
Formats
3
GPUs
19
Whisper Large V3 (1.55B) requires 1.5 GB VRAM at recommended quality (Q5_K_M). At efficient quality (Q4_K_M), it fits in 1.3 GB VRAM, making it compatible with the NVIDIA RTX 4060 Laptop (40-60W). On NVIDIA Grace Blackwell Ultra GB300, expect approximately 450 tok/s at FP16. For the best experience, Starter AI Desktop ($582) is recommended.
Source: OwnRig methodology
1.5 GB
Q5_K_M
0.93 GB
0K tokens
Transcription
VRAM Requirements
| Quality | Quantization | VRAM | File Size |
|---|---|---|---|
| full | FP16 | 3.1 GB | 3.1 GB |
| recommended | Q5_K_M | 1.5 GB | 0.93 GB |
| efficient | Q4_K_M | 1.3 GB | 0.78 GB |
Compatible GPUs
19 devices| NVIDIA Grace Blackwell Ultra GB300 | FP16 | 450 tok/s | Excellent |
| NVIDIA GeForce RTX 3060 12GB | FP16 | – | Excellent |
| NVIDIA GeForce RTX 3080 10GB | Q5_K_M | – | Excellent |
| NVIDIA GeForce RTX 4060 8GB | Q5_K_M | – | Excellent |
| NVIDIA RTX 4060 Laptop (40-60W) | Q5_K_M | – | Excellent |
| NVIDIA RTX 4070 Laptop (80-115W) | Q5_K_M | – | Excellent |
| NVIDIA GeForce RTX 4070 Ti 12GB | FP16 | – | Excellent |
| NVIDIA RTX 4080 Laptop (120-150W) | FP16 | – | Excellent |
| NVIDIA GeForce RTX 4090 | FP16 | – | Excellent |
| AMD Radeon RX 7600 | Q5_K_M | – | Excellent |
| AMD Radeon RX 7900 XTX | FP16 | – | Excellent |
| AMD Radeon Pro W7900 | FP16 | – | Excellent |
| NVIDIA RTX PRO 6000 Blackwell | FP16 | – | Excellent |
| NVIDIA RTX PRO 6000 Blackwell Max-Q | FP16 | – | Excellent |
| AMD Radeon RX 9070 | FP16 | – | Excellent |
| AMD Radeon RX 9060 XT 16GB | FP16 | – | Excellent |
| AMD Radeon RX 9060 XT 8GB | FP16 | – | Excellent |
| NVIDIA GeForce RTX 5060 8GB | Q5_K_M | – | Excellent |
| Apple M3 Pro (18GB Unified) | Q5_K_M | – | Good |
Showing 19 of 19 entries
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Frequently Asked Questions
- How much VRAM does Whisper Large V3 need?
- Whisper Large V3 requires 1.5 GB VRAM at recommended quality (Q5_K_M). At lower quality settings, it can fit in as little as 1.3 GB.
- What is the best GPU for Whisper Large V3?
- The NVIDIA Grace Blackwell Ultra GB300 delivers the best performance for Whisper Large V3, achieving 450 tok/s at FP16 with an excellent rating.
- What quantization should I use for Whisper Large V3?
- For the best quality, use Q5_K_M (1.5 GB VRAM). If your GPU has limited VRAM, Q4_K_M (1.3 GB) is the most efficient option with acceptable quality.
Related Guides
Data confidence: verified. Source
VRAM requirements are calculated from model parameters and may vary by inference engine, context length, and batch size. Performance estimates are based on community benchmarks and should be verified for your specific configuration.Whisper is a trademark of its respective owner. OwnRig is not affiliated with or endorsed by the model creator.