Can RTX 3060 12GB run Distil-Whisper Large v3?

Yes — runs locally

~84 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

12 GB

Model size

0.76B

Best quant

Q8_0

VRAM needed

1.9 GB

The verdict

The RTX 3060 12GB (12 GB VRAM) handles Distil-Whisper Large v3 comfortably using the Q8_0 quantization, which fits in 1.9 GB. Expected throughput is around 84 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 3060 12GB

AI-generated, GPU-specific. Verified commands for your exact hardware.

TL;DR

Run Distil-Whisper Large v3 on an NVIDIA GeForce RTX 3060 12GB for Grade S performance at ~373 tok/sec using the Q8_0 quantization.

Prerequisites

Before starting, ensure you have at least 5GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60 or later) with CUDA 11.8 installed.

Expected performance

With the Q8_0 quantization, you can expect ~373 tok/sec performance, utilizing approximately 1.9GB of VRAM. This leaves about 10.1GB of VRAM for context, allowing for a practical context window of several minutes of audio input.

1. Install runtimeOllama

pip install ollama
ollama config set runtime cuda

2. Download the model

Download the Q8_0 quantized model (1.4GB) from the Hugging Face repository.

ollama pull distil-whisper/distil-large-v3-ggml:Q8_0

3. Run it

ollama run distil-whisper/distil-large-v3-ggml:Q8_0
ollama chat --model distil-whisper/distil-large-v3-ggml:Q8_0

4. Optimize for RTX 3060 12GB

For optimal performance on the NVIDIA GeForce RTX 3060 12GB, use the --n-gpu-layers flag to offload layers to the GPU. Set --n-gpu-layers to 32 to utilize the 12GB VRAM effectively. Enable flash attention (--flash-attn) to further optimize memory usage and speed. Tensor parallelism is not necessary for this model size and GPU configuration.

Troubleshooting

Out of Memory (OOM) errors during inference

Reduce the number of GPU layers by setting --n-gpu-layers to a lower value, such as 24 or 16.

Low tokenization speed

Ensure that the CUDA backend is correctly configured and that the latest NVIDIA drivers are installed.

Inference fails to start

Check that the model file (ggml-distil-large-v3.bin) is correctly downloaded and accessible.

Alternative runtimes

For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for advanced customization options, or Jan for lightweight deployment. Ollama is recommended for its ease of use and CUDA backend support, making it ideal for the NVIDIA GeForce RTX 3060 12GB.

Full Distil-Whisper Large v3 details →

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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.

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