Can RTX 3090 run Whisper Large v3?
Yes — runs locally
~132 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3090 (24 GB VRAM) handles Whisper Large v3 comfortably using the Q8_0 quantization, which fits in 3.4 GB. Expected throughput is around 132 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 3090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Whisper Large v3 on an NVIDIA GeForce RTX 3090 with Q8_0 quantization for Grade S performance at ~396 tok/sec.
Prerequisites
Before starting, ensure you have at least 3GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60 or later) with CUDA 11.7 or higher installed.
Expected performance
With the recommended settings, you can expect to achieve ~396 tok/sec, with 3.4GB VRAM in use and 20.6GB of VRAM available for context. This allows for a practical context window of several minutes of audio, depending on the specific requirements.
1. Install runtimeOllama
sudo apt-get update && sudo apt-get install -y ollama
ollama --version2. 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 interact --model ggerganov/whisper.cpp:ggml-large-v3.bin4. Optimize for RTX 3090
For optimal performance on the NVIDIA GeForce RTX 3090 with 24GB VRAM, set --n-gpu-layers to 48 to fully utilize the GPU's memory. Enable flash attention (--flash-attn) to speed up inference. With 3.4GB VRAM used by the model, you will have 20.6GB of VRAM left for context, allowing for a large practical context window.
Troubleshooting
Insufficient VRAM during inference
Reduce the number of layers loaded onto the GPU using --n-gpu-layers <num_layers> or enable CPU offloading with --offload-dir <path>
Slow inference speed
Ensure that flash attention is enabled with --flash-attn and that the CUDA toolkit is correctly installed and up-to-date.
Model fails to load
Verify that the model file has been downloaded correctly and that the Ollama runtime is installed and configured properly.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio offers a user-friendly interface and is suitable for users who prefer a graphical environment. llama.cpp provides more control over low-level optimizations and is ideal for advanced users. Jan is a lightweight runtime that can be used for quick prototyping. For the NVIDIA GeForce RTX 3090, Ollama is recommended for its ease of use and performance.
Other models that run great on RTX 3090
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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