Can RTX 5060 Ti run Whisper Large v3?
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 comfortably using the Q8_0 quantization, which fits in 3.4 GB. Expected throughput is around 156 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Largest Whisper model. Best accuracy across all languages and accents.
Setup tutorial: Whisper Large v3 on RTX 5060 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Whisper Large v3 on an NVIDIA GeForce RTX 5060 Ti with Grade S performance at ~264 tok/sec using the Q8_0 quantization. Requires 3.4GB VRAM.
Prerequisites
Before starting, ensure you have at least 3GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.11 or later) with CUDA 11.8 installed.
Expected performance
With the Q8_0 quantization, you can expect ~264 tok/sec performance, utilizing 3.4GB of VRAM. This leaves 12.6GB of VRAM available for context, allowing for a practical context window of several minutes of audio depending on the input.
1. Install runtimeOllama
curl -s https://ollama.com/install.sh | bash
ollama setup2. Download the model
Download the Q8_0 quantized version of Whisper Large v3 (2.9GB file) from Hugging Face.
ollama pull ggerganov/whisper.cpp:ggml-large-v3.bin3. Run it
ollama run whisper_large_v3 --model-path ggml-large-v3.bin --device cuda
ollama interact whisper_large_v34. Optimize for RTX 5060 Ti
For optimal performance on the NVIDIA GeForce RTX 5060 Ti with 16GB VRAM, use the --n-gpu-layers parameter to offload layers to the GPU. Set --n-gpu-layers to 48 to utilize the 16GB VRAM effectively. Enable flash attention (--flash-attn) to further optimize memory usage and speed. Tensor parallelism is not necessary for this model and GPU combination.
Troubleshooting
Out of memory error during inference
Reduce the --n-gpu-layers value to 32 or lower to free up more VRAM.
Slow token generation
Ensure that the latest NVIDIA drivers and CUDA are installed. Check for any background processes consuming GPU resources.
Model fails to load
Verify the integrity of the downloaded model file using md5sum or sha256sum. Re-download if necessary.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more specialized needs. LM Studio offers a graphical interface and is suitable for users who prefer a GUI. llama.cpp provides more fine-grained control over quantization and optimization settings, ideal for advanced users. Jan is lightweight and efficient, suitable for environments with limited resources. For the NVIDIA GeForce RTX 5060 Ti, Ollama is generally the most straightforward and performant option.
Other models that run great on RTX 5060 Ti
FAQ (20)
What GPU do I need to run Whisper Large v3?
To run Whisper Large v3, you need a GPU with at least 3.4 GB of VRAM. NVIDIA GPUs like the RTX 2060 or higher are recommended for optimal performance.
Is Whisper Large v3 good for coding?
Whisper Large v3 is primarily designed for speech recognition and not for coding tasks. It excels in transcribing audio and handling multilingual content.
Whisper Large v3 vs Llama 3.1 8B?
Whisper Large v3 has 1.55B parameters and is optimized for speech recognition, while Llama 3.1 8B has 8B parameters and is more suited for text generation and language understanding tasks.
Can I run Whisper Large v3 on a Mac?
Yes, you can run Whisper Large v3 on a Mac, but ensure your Mac has a compatible GPU with at least 3.4 GB of VRAM for smooth operation.
How much VRAM does Whisper Large v3 need?
Whisper Large v3 requires 3.4 GB of VRAM, regardless of quantization level, to run efficiently.
Is Whisper Large v3 censored?
Whisper Large v3 is not censored. It is designed to handle a wide range of audio inputs and transcribe them accurately without restrictions.
Is Whisper Large v3 commercial-use allowed?
Yes, Whisper Large v3 is licensed under the MIT license, which allows for both commercial and non-commercial use.
Whisper Large v3 context length?
The context length for Whisper Large v3 is not explicitly defined, but it is designed to handle long audio segments effectively.
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