Can RTX 3080 run Whisper Large v3 Turbo?
Yes — runs locally
~108 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3080 (10 GB VRAM) handles Whisper Large v3 Turbo comfortably using the Q8_0 quantization, which fits in 2.0 GB. Expected throughput is around 108 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Optimized large Whisper model. Near-best accuracy with faster inference.
Setup tutorial: Whisper Large v3 Turbo on RTX 3080
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Large v3 Turbo runs at Grade S on an NVIDIA GeForce RTX 3080 with Q8_0 quantization, achieving ~297 tok/sec.
Prerequisites
Before starting, ensure you have at least 1.5GB of disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 470.82 or later, and CUDA 11.4 or later installed.
Expected performance
With the recommended settings, you can expect ~297 tok/sec and 2.0GB VRAM usage, leaving 8.0GB of VRAM for context. This allows for a practical context window of several minutes of audio, depending on the complexity of the input.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Q8_0 quantized model (1.5GB) from Hugging Face.
ollama pull ggerganov/whisper.cpp:ggml-large-v3-turbo.bin3. Run it
ollama run ggerganov/whisper.cpp:ggml-large-v3-turbo.bin --model-path ggml-large-v3-turbo.bin --n-gpu-layers 20 --flash-attn --tensor-parallelism 24. Optimize for RTX 3080
For optimal performance on the NVIDIA GeForce RTX 3080 with 10GB VRAM, set --n-gpu-layers to 20 to utilize most of the VRAM while leaving some headroom. Enable --flash-attn for faster attention computations and set --tensor-parallelism to 2 to distribute the workload efficiently across the GPU cores.
Troubleshooting
Out of memory error during inference
Reduce --n-gpu-layers to 15 and try again.
Low tokenization speed
Ensure CUDA is properly installed and the GPU drivers are up to date.
Inference fails with a segmentation fault
Reinstall the Ollama runtime and verify the integrity of the downloaded model file.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or if you prefer a different interface. LM Studio is ideal for a graphical interface, llama.cpp offers more control over quantization and optimization, and Jan is suitable for distributed training and inference setups. However, Ollama provides a streamlined and easy-to-use experience for most users on the NVIDIA GeForce RTX 3080.
Other models that run great on RTX 3080
FAQ (20)
What GPU do I need to run Whisper Large v3 Turbo?
To run Whisper Large v3 Turbo, you need a GPU with at least 2.0 GB of VRAM. The exact VRAM requirement can vary slightly depending on the quantization level used.
Is Whisper Large v3 Turbo good for coding?
Whisper Large v3 Turbo is primarily designed for speech recognition tasks and may not be optimized for coding-related tasks. For coding, models like Codex or CodeLLaMa might be more suitable.
Whisper Large v3 Turbo vs Llama 3.1 8B?
Whisper Large v3 Turbo has 0.81 billion parameters and is optimized for speech recognition, while Llama 3.1 8B has 8 billion parameters and is more versatile for general language tasks. Choose based on your specific needs.
Can I run Whisper Large v3 Turbo on a Mac?
Yes, you can run Whisper Large v3 Turbo on a Mac as long as your Mac has a compatible GPU with at least 2.0 GB of VRAM. Ensure you have the necessary drivers and libraries installed.
How much VRAM does Whisper Large v3 Turbo need?
Whisper Large v3 Turbo requires at least 2.0 GB of VRAM. The exact amount can vary slightly depending on the quantization level used.
Is Whisper Large v3 Turbo censored?
Whisper Large v3 Turbo is not censored. It is an open-source model released under the MIT license, allowing for broad usage without content restrictions.
Is Whisper Large v3 Turbo commercial-use allowed?
Yes, Whisper Large v3 Turbo is licensed under the MIT license, which allows for commercial use without additional restrictions.
Whisper Large v3 Turbo context length?
The context length for Whisper Large v3 Turbo is currently unknown. Refer to the official documentation or model repository for the most accurate information.
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