Can RTX 3060 12GB run Whisper Large v3?

Yes — runs locally

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

Your VRAM

12 GB

Model size

1.55B

Best quant

Q8_0

VRAM needed

3.4 GB

The verdict

The RTX 3060 12GB (12 GB VRAM) handles Whisper Large v3 comfortably using the Q8_0 quantization, which fits in 3.4 GB. Expected throughput is around 84 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 3060 12GB

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

TL;DR

Run Whisper Large v3 on an NVIDIA GeForce RTX 3060 12GB for Grade S performance at ~198 tok/sec using the Q8_0 quantization.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 470.82.01 or later, and CUDA 11.2 or later installed.

Expected performance

With the recommended settings, you can expect ~198 tok/sec performance, with 3.4GB VRAM in use, leaving 8.6GB of VRAM for context. This allows for a practical context window of several minutes of audio, depending on the specific requirements.

1. Install runtimeOllama

curl -fsSL https://ollama.com/install.sh | bash
ollama setup

2. Download the model

Download the Q8_0 quantized version of Whisper Large v3 (2.9GB) from Hugging Face.

ollama pull ggerganov/whisper.cpp:ggml-large-v3.bin

3. Run it

ollama run ggerganov/whisper.cpp:ggml-large-v3.bin --device cuda --n-gpu-layers 32 --flash-attn
ollama interactive ggerganov/whisper.cpp:ggml-large-v3.bin

4. Optimize for RTX 3060 12GB

For optimal performance on the NVIDIA GeForce RTX 3060 12GB, set --n-gpu-layers to 32 to utilize the 12GB VRAM efficiently. Enable --flash-attn to speed up attention computations. With these settings, you should achieve ~198 tok/sec while keeping the VRAM usage around 3.4GB, leaving 8.6GB for context.

Troubleshooting

Out of memory error during inference

Reduce --n-gpu-layers to 24 or 16 to lower VRAM usage.

Slow inference speed

Ensure CUDA is properly installed and enabled with --device cuda. Also, enable --flash-attn for faster attention calculations.

Model not loading

Verify that the model file is correctly downloaded and not corrupted. Re-run the download command if necessary.

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is a good choice for a more user-friendly interface, while llama.cpp offers more customization options. Jan is suitable for cloud deployments. For the NVIDIA GeForce RTX 3060 12GB, Ollama provides a balanced combination of ease of use and performance.

Full Whisper Large v3 details →

Other models that run great on RTX 3060 12GB

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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