Can RTX 5060 Ti run Whisper Large v3 Turbo?
Yes — runs locally
~156 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5060 Ti (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 5060 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Large v3 Turbo runs at Grade S on the NVIDIA GeForce RTX 5060 Ti with Q8_0 quantization, achieving ~475 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
You can expect the model to run at approximately 475 tokens per second, utilizing 2.0GB of VRAM. The remaining 14.0GB of VRAM provides ample headroom for handling larger context windows, enabling the processing of extended audio clips without performance degradation.
1. Install runtimeOllama
curl -O https://ollama.com/install.sh
bash install.sh2. 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-path /path/to/ggml-large-v3-turbo.bin
ollama chat --model ggerganov/whisper.cpp:ggml-large-v3-turbo.bin4. Optimize for RTX 5060 Ti
For optimal performance on the NVIDIA GeForce RTX 5060 Ti with 16GB VRAM, set --n-gpu-layers to 32 to utilize the GPU efficiently. Enable flash-attn for faster inference and consider using tensor parallelism if you are running multiple instances. With 2.0GB VRAM used by the model, you will have 14.0GB of VRAM available for context, allowing for longer audio segments.
Troubleshooting
The model does not load due to insufficient VRAM.
Reduce the number of GPU layers by setting --n-gpu-layers to a lower value, such as 24.
Inference is slower than expected.
Ensure that flash-attn is enabled and that your CUDA drivers are up to date. You can also try increasing the batch size if your VRAM allows it.
The model crashes during long audio processing.
Increase the swap space or reduce the context window size to fit within the available VRAM.
Alternative runtimes
Alternative runtimes like LM Studio and llama.cpp can be used for more fine-grained control over the model execution. LM Studio is ideal for users who prefer a graphical interface, while llama.cpp offers advanced customization options. Jan is another lightweight option suitable for resource-constrained environments. Choose an alternative runtime based on your specific needs for control, performance, and ease of use.
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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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