Can RTX 4060 Ti 16GB run Whisper Large v3 Turbo?
Yes — runs locally
~114 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4060 Ti 16GB (16 GB VRAM) handles Whisper Large v3 Turbo comfortably using the Q8_0 quantization, which fits in 2.0 GB. Expected throughput is around 114 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 4060 Ti 16GB
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Large v3 Turbo runs at Grade S on the NVIDIA GeForce RTX 4060 Ti 16GB 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
With the Q8_0 quantization, you can expect Whisper Large v3 Turbo to run at ~475 tok/sec, using approximately 2.0GB of VRAM. This leaves 14.0GB 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 version of Whisper Large v3 Turbo (1.5GB file) from the Hugging Face repository.
ollama pull ggerganov/whisper.cpp:ggml-large-v3-turbo3. Run it
ollama run ggerganov/whisper.cpp:ggml-large-v3-turbo --device cuda
ollama interact ggerganov/whisper.cpp:ggml-large-v3-turbo4. Optimize for RTX 4060 Ti 16GB
For optimal performance on the NVIDIA GeForce RTX 4060 Ti 16GB, use the --n-gpu-layers flag to offload layers to the GPU, enable flash attention with --flash-attn, and consider tensor parallelism with --tensor-parallel-size. Given the 16GB VRAM, you can set --n-gpu-layers to a high value (e.g., 60) to maximize GPU utilization while maintaining a reasonable context window.
Troubleshooting
Out of memory errors during inference
Reduce the --n-gpu-layers value to lower the VRAM usage.
Slow inference speed
Ensure that CUDA is properly installed and that the GPU is being utilized by checking with nvidia-smi.
Inference crashes or hangs
Update your NVIDIA drivers to the latest version and reinstall CUDA if necessary.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you need more control over the execution environment or specific features. LM Studio is suitable for a graphical interface, llama.cpp offers more customization options, and Jan is ideal for lightweight, embedded systems. However, Ollama provides a streamlined and user-friendly experience for most users on the NVIDIA GeForce RTX 4060 Ti 16GB.
Other models that run great on RTX 4060 Ti 16GB
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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