Can RTX 4070 run Whisper Large v3 Turbo?

Yes — runs locally

~132 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

12 GB

Model size

0.81B

Best quant

Q8_0

VRAM needed

2.0 GB

The verdict

The RTX 4070 (12 GB VRAM) handles Whisper Large v3 Turbo comfortably using the Q8_0 quantization, which fits in 2.0 GB. Expected throughput is around 132 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 4070

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

TL;DR

Whisper Large v3 Turbo runs at Grade S on an NVIDIA GeForce RTX 4070 with Q8_0 quantization, achieving ~356 tok/sec.

Prerequisites

Before starting, ensure you have at least 1.5GB of free disk space, a compatible operating system (Windows or Linux), the latest NVIDIA drivers (version 525.60.11 or later), and CUDA 11.8 installed.

Expected performance

With the recommended settings, you can expect Whisper Large v3 Turbo to run at ~356 tok/sec, using 2.0GB of VRAM, leaving 10.0GB of VRAM for context. This provides ample headroom for processing longer audio segments without running out of memory.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q8_0 quantized version of Whisper Large v3 Turbo (1.5GB file) from Hugging Face.

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

3. Run it

ollama run ggerganov/whisper.cpp:ggml-large-v3-turbo.bin --device cuda
ollama interactive ggerganov/whisper.cpp:ggml-large-v3-turbo.bin --device cuda

4. Optimize for RTX 4070

For optimal performance on the NVIDIA GeForce RTX 4070 with 12GB VRAM, set --n-gpu-layers to 32 to utilize the GPU efficiently. Enable flash attention (--flash-attn) to speed up inference. With 2.0GB VRAM used by the model, you have 10.0GB of VRAM left for context, allowing for a practical context window of several minutes of audio.

Troubleshooting

CUDA out of memory error

Reduce --n-gpu-layers to 24 or lower and decrease batch size.

Slow inference speed

Ensure flash attention is enabled with --flash-attn and check that CUDA is properly installed and configured.

Model not loading

Verify that the model file is correctly downloaded and not corrupted. Try re-downloading the model.

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is suitable for a more user-friendly interface, while llama.cpp offers more control over quantization and optimization. Jan is a lightweight option for quick testing but may lack advanced features. Choose based on your specific needs and comfort level with configuration.

Full Whisper Large v3 Turbo details →

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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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