Can RTX 5070 run Whisper Large v3 Turbo?
Yes — runs locally
~132 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5070 (12 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 5070
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Large v3 Turbo runs at Grade S on the NVIDIA GeForce RTX 5070 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 525.60.13 or later) with CUDA 11.8 installed.
Expected performance
With the recommended settings, you can expect the model to run at ~356 tok/sec, using approximately 2.0GB of VRAM. This leaves 10.0GB of VRAM for context, allowing for a practical context window of several minutes of audio 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 --model ggerganov/whisper.cpp:ggml-large-v3-turbo.bin --device cuda
ollama interact --model ggerganov/whisper.cpp:ggml-large-v3-turbo.bin4. Optimize for RTX 5070
For optimal performance on the NVIDIA GeForce RTX 5070 with 12GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU. Set --n-gpu-layers to 64 to balance between speed and memory usage. Enable flash attention with --flash-attn to further improve efficiency. Given the 12GB VRAM, you can achieve a practical context window of several minutes of audio without running out of memory.
Troubleshooting
Out of memory error during inference
Reduce the --n-gpu-layers value to 32 or lower to decrease VRAM usage.
Slow inference times
Ensure that CUDA is properly installed and that the --device cuda flag is used. Also, enable flash attention with --flash-attn.
Model not found error
Verify that the model was successfully downloaded by checking the model directory and ensure the correct model name is used in the run commands.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is a good choice for a graphical interface and easy model management. llama.cpp offers more control over quantization and optimization but requires more manual setup. Jan is suitable for lightweight deployments with minimal dependencies. For the NVIDIA GeForce RTX 5070, Ollama provides a balanced approach with ease of use and good performance.
Other models that run great on RTX 5070
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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