Can RTX 4090 run Whisper Large v3?
Yes — runs locally
~192 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4090 (24 GB VRAM) handles Whisper Large v3 comfortably using the Q8_0 quantization, which fits in 3.4 GB. Expected throughput is around 192 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 4090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Whisper Large v3 on an NVIDIA GeForce RTX 4090 with Ollama using the Q8_0 quantization. Expect Grade S performance at ~396 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) with CUDA 11.8 installed.
Expected performance
With the Q8_0 quantization, you can expect ~396 tok/sec performance, utilizing 3.4GB of VRAM. The remaining 20.6GB of VRAM allows for a practical context window of several minutes of audio, depending on the specific requirements.
1. Install runtimeOllama
curl -s https://ollama.com/install.sh | bash
ollama install2. Download the model
Download the Q8_0 quantized version of Whisper Large v3 (2.9GB file) from the Hugging Face repository.
ollama pull ggerganov/whisper.cpp:ggml-large-v3.bin3. Run it
ollama run whisper_large_v3 --model ggml-large-v3.bin --n-gpu-layers 128 --flash-attn
ollama interactive whisper_large_v34. Optimize for RTX 4090
For optimal performance on the NVIDIA GeForce RTX 4090 with 24GB VRAM, set --n-gpu-layers to 128 to utilize the GPU's memory efficiently. Enable --flash-attn to speed up attention computations. With 3.4GB VRAM used by the model, you have 20.6GB of VRAM left for context, allowing for longer audio processing without running out of memory.
Troubleshooting
Out of memory errors during long audio processing
Reduce the --n-gpu-layers parameter to 64 or lower to free up more VRAM for context.
Slow performance
Ensure that the latest NVIDIA drivers and CUDA are installed. Also, check if the --flash-attn flag is enabled.
Model not loading
Verify the integrity of the downloaded model file and try re-downloading it using the 'ollama pull' command.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for specific needs. LM Studio offers a user-friendly GUI and is suitable for users who prefer a visual interface. llama.cpp provides more fine-grained control over model parameters and is ideal for advanced users. Jan is lightweight and can be a good choice for environments with limited resources. However, Ollama is recommended for its ease of use and performance on the NVIDIA GeForce RTX 4090.
Other models that run great on RTX 4090
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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