Can RTX 4080 run Whisper Large v3 Turbo?
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 Turbo comfortably using the Q8_0 quantization, which fits in 2.0 GB. Expected throughput is around 156 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 4080
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Large v3 Turbo runs at Grade S on an NVIDIA GeForce RTX 4080 with Q8_0 quantization, achieving ~475 tok/sec.
Prerequisites
Before starting, ensure you have at least 1.5GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.60 or later), and CUDA 11.8 installed.
Expected performance
With the Q8_0 quantization, you can expect ~475 tok/sec performance, using approximately 2.0GB of VRAM. The remaining 14.0GB of VRAM provides ample headroom for processing longer audio contexts, making it suitable for extended speech recognition tasks.
1. Install runtimeOllama
sudo apt-get update && sudo apt-get install -y ollama
ollama version2. Download the model
Download the Q8_0 quantized model (1.5GB) from the Hugging Face repository.
ollama pull ggerganov/whisper.cpp:ggml-large-v3-turbo.bin3. Run it
ollama run ggerganov/whisper.cpp:ggml-large-v3-turbo.bin --model ggml-large-v3-turbo.bin --n-gpu-layers 48 --flash-attn4. Optimize for RTX 4080
For optimal performance on the NVIDIA GeForce RTX 4080 with 16GB VRAM, set --n-gpu-layers to 48 to utilize the GPU efficiently. Enable flash-attn for faster inference. With 2.0GB VRAM used by the model, you have 14.0GB of VRAM headroom for context, allowing for longer audio processing.
Troubleshooting
Low performance or high CPU usage
Ensure that the --n-gpu-layers parameter is set to 48 and that flash-attn is enabled. This will maximize GPU utilization and reduce CPU load.
Out of memory errors
Reduce the --n-gpu-layers value to 32 or 24 to lower VRAM usage. Alternatively, increase the batch size to process more data in parallel.
Inference is too slow
Enable flash-attn and ensure that the --n-gpu-layers parameter is set to 48. If still slow, consider increasing the batch size.
Alternative runtimes
For users preferring different runtimes, LM Studio offers a GUI-based interface and is suitable for those who prefer a visual setup. llama.cpp is a lightweight alternative that can be compiled and run directly from the command line, ideal for minimal setups. Jan is another runtime that supports a wide range of models but may require additional configuration for optimal performance. Choose based on your comfort with command-line tools and the need for a graphical interface.
Other models that run great on RTX 4080
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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