Can RTX 3080 Ti run Whisper Large v3 Turbo?
Yes — runs locally
~108 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3080 Ti (12 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 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Large v3 Turbo runs at Grade S on an NVIDIA GeForce RTX 3080 Ti with Q8_0 quantization, achieving ~356 tok/sec.
Prerequisites
Before starting, ensure you have at least 1.5GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 470 or later) with CUDA 11.2 or higher installed.
Expected performance
With the Q8_0 quantization, you can expect the model to run at ~356 tok/sec, using approximately 2.0GB of VRAM. This leaves around 10.0GB of VRAM available for context, enabling a practical context window of several minutes of audio without running out of memory.
1. Install runtimeOllama
curl -L https://ollama.com/install.sh | bash
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 --device cuda
ollama interactive ggerganov/whisper.cpp:ggml-large-v3-turbo.bin --device cuda4. Optimize for RTX 3080 Ti
For optimal performance on the NVIDIA GeForce RTX 3080 Ti with 12GB VRAM, set --n-gpu-layers to 48 to fully utilize the GPU. Enable flash attention with --flash-attn to reduce memory usage and improve speed. Given the 2.0GB VRAM requirement, you will have approximately 10.0GB 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 layers on the GPU with --n-gpu-layers 32 or lower.
Slow inference speed
Ensure that flash attention is enabled with --flash-attn and that the CUDA backend is properly configured.
Model not loading
Verify that the model file is correctly downloaded and not corrupted. Try re-downloading the model with the pull command.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is suitable for a more user-friendly interface and easier model management. llama.cpp offers more fine-grained control over optimization settings and is ideal for advanced users. Jan is a lightweight option for quick testing but may lack some features available in Ollama. Choose based on your specific needs for ease of use, performance tuning, or lightweight deployment.
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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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