Can RTX 4070 SUPER 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 SUPER (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 SUPER

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 SUPER with Q8_0 quantization, achieving ~356 tok/sec.

Prerequisites

Before starting, ensure you have at least 1.5GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 510.47 or later, and CUDA 11.2 or later installed.

Expected performance

With the Q8_0 quantization, you can expect ~356 tok/sec, utilizing 2.0GB of VRAM, leaving 10.0GB for context. This allows for a practical context window of up to 2048 tokens, ensuring efficient and fast inference.

1. Install runtimeOllama

pip install ollama
ollama config set device cuda

2. Download the model

Download the Q8_0 quantized model (1.5GB) 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 --model ggml-large-v3-turbo.bin --context-length 2048 --n-gpu-layers 12 --flash-attn
ollama interactive ggerganov/whisper.cpp:ggml-large-v3-turbo.bin

4. Optimize for RTX 4070 SUPER

For optimal performance on the NVIDIA GeForce RTX 4070 SUPER with 12GB VRAM, use the --n-gpu-layers 12 flag to offload layers to the GPU. Enable flash-attn for faster attention computation. Given the 2.0GB VRAM usage, you will have approximately 10.0GB of VRAM left for context, allowing for a practical context window of up to 2048 tokens.

Troubleshooting

Out of memory error during inference

Reduce the --n-gpu-layers value to 8 or 4 to lower VRAM usage.

Slow inference speed

Ensure that the --flash-attn flag is enabled to speed up attention computation.

Model not found

Verify that the model was correctly downloaded using 'ollama list' and check the model path in the run command.

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is suitable for users who prefer a graphical interface. llama.cpp offers more fine-grained control over model parameters and is ideal for advanced users. Jan is a lightweight runtime that is easy to set up but may lack some features of Ollama. For the NVIDIA GeForce RTX 4070 SUPER, Ollama provides a balanced approach with good performance and ease of use.

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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