Can RTX 4080 run Whisper Large v3?
Yes — runs locally
~156 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4080 (16 GB VRAM) handles Whisper Large v3 comfortably using the Q8_0 quantization, which fits in 3.4 GB. Expected throughput is around 156 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Largest Whisper model. Best accuracy across all languages and accents.
Setup tutorial: Whisper Large v3 on RTX 4080
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Whisper Large v3 on an NVIDIA GeForce RTX 4080 with Q8_0 quantization for Grade S performance at ~264 tok/sec.
Prerequisites
Before starting, ensure you have at least 3GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 510.47 or later, and CUDA 11.4 or later installed.
Expected performance
You can expect ~264 tok/sec performance with 3.4GB VRAM in use, leaving 12.6GB of VRAM for context. This allows for a practical context window of several minutes of audio, depending on the complexity and length of the input.
1. Install runtimeOllama
curl -sSL https://ollama.com/install.sh | sh
ollama install2. Download the model
Download the Q8_0 quantized version of Whisper Large v3 (2.9GB file) from Hugging Face.
ollama pull ggerganov/whisper.cpp:ggml-large-v3.bin3. Run it
ollama run --model ggerganov/whisper.cpp:ggml-large-v3.bin --device cuda
ollama interactive4. Optimize for RTX 4080
For optimal performance on the NVIDIA GeForce RTX 4080 with 16GB VRAM, set --n-gpu-layers to 50 to utilize the GPU efficiently. Enable flash attention (--flash-attn) to speed up inference and reduce memory usage. With 3.4GB VRAM used by the model, you have 12.6GB of VRAM left for context, allowing for a practical context window of several minutes of audio.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers using --n-gpu-layers or decrease the batch size.
Low token generation speed
Ensure flash attention is enabled with --flash-attn and check your CUDA installation.
Model not loading
Verify the model file integrity and try re-downloading it using the ollama pull command.
Alternative runtimes
For users preferring a different runtime, consider LM Studio for a more graphical interface, llama.cpp for more advanced quantization options, or Jan for a lightweight alternative. Ollama is recommended for its ease of use and robust performance on the NVIDIA GeForce RTX 4080.
Other models that run great on RTX 4080
FAQ (20)
What GPU do I need to run Whisper Large v3?
To run Whisper Large v3, you need a GPU with at least 3.4 GB of VRAM. NVIDIA GPUs like the RTX 2060 or higher are recommended for optimal performance.
Is Whisper Large v3 good for coding?
Whisper Large v3 is primarily designed for speech recognition and not for coding tasks. It excels in transcribing audio and handling multilingual content.
Whisper Large v3 vs Llama 3.1 8B?
Whisper Large v3 has 1.55B parameters and is optimized for speech recognition, while Llama 3.1 8B has 8B parameters and is more suited for text generation and language understanding tasks.
Can I run Whisper Large v3 on a Mac?
Yes, you can run Whisper Large v3 on a Mac, but ensure your Mac has a compatible GPU with at least 3.4 GB of VRAM for smooth operation.
How much VRAM does Whisper Large v3 need?
Whisper Large v3 requires 3.4 GB of VRAM, regardless of quantization level, to run efficiently.
Is Whisper Large v3 censored?
Whisper Large v3 is not censored. It is designed to handle a wide range of audio inputs and transcribe them accurately without restrictions.
Is Whisper Large v3 commercial-use allowed?
Yes, Whisper Large v3 is licensed under the MIT license, which allows for both commercial and non-commercial use.
Whisper Large v3 context length?
The context length for Whisper Large v3 is not explicitly defined, but it is designed to handle long audio segments effectively.
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