Can RTX 5060 Ti run Distil-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 Distil-Whisper Large v3 comfortably using the Q8_0 quantization, which fits in 1.9 GB. Expected throughput is around 156 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Distilled Whisper. 6x faster than large-v3 with 1% accuracy loss.
Setup tutorial: Distil-Whisper Large v3 on RTX 5060 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Distil-Whisper Large v3 on an NVIDIA GeForce RTX 5060 Ti with Q8_0 quantization for Grade S performance at ~497 tok/sec.
Prerequisites
Before starting, ensure you have at least 2GB of free disk space, a 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 525.60 or later) with CUDA 11.7 installed.
Expected performance
With the Q8_0 quantization, you can expect the model to run at approximately 497 tokens per second, using around 1.9GB of VRAM. This leaves about 14.1GB of VRAM for context, allowing for a practical context window of several minutes of audio.
1. Install runtimeOllama
pip install ollama
ollama config set cuda2. Download the model
Download the Q8_0 quantized version of Distil-Whisper Large v3 (1.4GB file) from HuggingFace.
ollama pull distil-whisper/distil-large-v3-ggml:Q8_03. Run it
ollama run distil-whisper/distil-large-v3-ggml:Q8_0 --device cuda
ollama interactive4. Optimize for RTX 5060 Ti
For optimal performance on the NVIDIA GeForce RTX 5060 Ti with 16GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. Given the 16GB VRAM, you can set --n-gpu-layers to 32 to utilize the GPU efficiently while maintaining a practical context window.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers with --n-gpu-layers or enable flash attention with --flash-attn.
Slow inference speed
Ensure CUDA is properly configured and try increasing the batch size with --batch-size.
Model not loading
Check if the model file is corrupted and re-download it using the 'ollama pull' command.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is suitable for a more user-friendly interface, llama.cpp offers fine-grained control over optimizations, and Jan is ideal for lightweight deployments. However, Ollama provides a balanced approach with good performance and ease of use, making it the recommended choice for this GPU.
Other models that run great on RTX 5060 Ti
FAQ (20)
What GPU do I need to run Distil-Whisper Large v3?
To run Distil-Whisper Large v3, you need a GPU with at least 1.9 GB of VRAM. NVIDIA GPUs such as the GTX 1060 or higher are recommended.
Is Distil-Whisper Large v3 good for coding?
Distil-Whisper Large v3 is primarily designed for speech recognition tasks and may not be optimized for coding-specific tasks. For coding, models like Codex or CodeLlama are more suitable.
Distil-Whisper Large v3 vs Llama 3.1 8B?
Distil-Whisper Large v3 has 0.76B parameters and is optimized for speech recognition, while Llama 3.1 8B is a larger, more versatile model with 8B parameters, better suited for a wider range of NLP tasks.
Can I run Distil-Whisper Large v3 on a Mac?
Yes, you can run Distil-Whisper Large v3 on a Mac, but ensure your Mac has a compatible GPU with at least 1.9 GB of VRAM. M1 and later Macs with Metal support are recommended.
How much VRAM does Distil-Whisper Large v3 need?
Distil-Whisper Large v3 requires 1.9 GB of VRAM, which is consistent across different quantization levels.
Is Distil-Whisper Large v3 censored?
No, Distil-Whisper Large v3 is not censored. It is an open-source model under the MIT license, allowing for unrestricted use and modification.
Is Distil-Whisper Large v3 commercial-use allowed?
Yes, Distil-Whisper Large v3 is licensed under the MIT license, which allows for commercial use without restrictions.
Distil-Whisper Large v3 context length?
The context length for Distil-Whisper Large v3 is currently unknown. For more detailed information, refer to the model's documentation or source code.
Want personalized recommendations for your exact setup? Detect my hardware →