Can RTX 3090 run Whisper Large v3 Turbo?
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 Turbo comfortably using the Q8_0 quantization, which fits in 2.0 GB. Expected throughput is around 132 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 3090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Large v3 Turbo runs at Grade S on an NVIDIA GeForce RTX 3090 with Q8_0 quantization, achieving ~713 tok/sec.
Prerequisites
Before starting, ensure you have at least 1.5GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 470 or higher, and CUDA 11.0 or higher installed.
Expected performance
With the recommended settings, you can expect ~713 tok/sec and 2.0GB VRAM in use, leaving 22.0GB of VRAM for context. Given the remaining VRAM, you can achieve a practical context window of several minutes of audio, depending on the specific requirements.
1. Install runtimeOllama
pip install ollama
ollama init2. 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 --model ggerganov/whisper.cpp:ggml-large-v3-turbo.bin --device cuda
ollama interactive4. Optimize for RTX 3090
For optimal performance on the NVIDIA GeForce RTX 3090 with 24GB VRAM, set --n-gpu-layers to 50 to utilize the GPU efficiently. Enable flash-attn for faster inference and consider tensor parallelism if running multiple instances. This configuration will allow you to achieve the best balance between speed and memory usage.
Troubleshooting
CUDA out of memory error
Reduce --n-gpu-layers to 40 or lower and increase batch size if possible.
Inference is slower than expected
Ensure flash-attn is enabled and check if the latest NVIDIA drivers and CUDA are installed.
Interactive mode does not start
Check if the model is fully downloaded and try restarting the Ollama service with 'ollama restart'.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is suitable for a more user-friendly interface, llama.cpp offers more control over quantization and optimization, and Jan is ideal for distributed training scenarios. For the NVIDIA GeForce RTX 3090, Ollama provides the best balance of 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 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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