Can RTX 4080 SUPER run Whisper Large v3 Turbo?

Yes — runs locally

~156 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

16 GB

Model size

0.81B

Best quant

Q8_0

VRAM needed

2.0 GB

The verdict

The RTX 4080 SUPER (16 GB VRAM) handles Whisper Large v3 Turbo comfortably using the Q8_0 quantization, which fits in 2.0 GB. Expected throughput is around 156 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 4080 SUPER

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

TL;DR

Run Whisper Large v3 Turbo on a NVIDIA GeForce RTX 4080 SUPER with Q8_0 quantization for Grade S performance, achieving ~475 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 525.60 or later, and CUDA 11.8 or later installed.

Expected performance

With the recommended settings, you can expect to achieve ~475 tok/sec, with the model using approximately 2.0GB of VRAM. This leaves 14.0GB of VRAM available for context, allowing you to process longer audio clips or maintain a larger context window.

1. Install runtimeOllama

curl -L https://ollama.ai/install.sh | bash
ollama setup

2. Download the model

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

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

3. Run it

ollama run ggerganov/whisper.cpp:ggml-large-v3-turbo.bin --n-gpu-layers 32 --flash-attn --tensor-parallelism 2

4. Optimize for RTX 4080 SUPER

For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, use the --n-gpu-layers 32 flag to offload more layers to the GPU. Enable flash attention (--flash-attn) for faster inference, and set tensor parallelism (--tensor-parallelism 2) to utilize the GPU's parallel processing capabilities. This configuration ensures that the model runs efficiently within the 16GB VRAM limit.

Troubleshooting

Model runs out of VRAM during inference

Reduce the number of GPU layers using the --n-gpu-layers flag, e.g., --n-gpu-layers 16.

Inference is slower than expected

Ensure that flash attention is enabled (--flash-attn) and that tensor parallelism is set to 2 (--tensor-parallelism 2).

Ollama setup fails

Check your internet connection and try running the setup command again: curl -L https://ollama.ai/install.sh | bash

Alternative runtimes

For users who prefer other runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for more advanced customization options, or Jan for a lightweight alternative. Ollama is recommended for its ease of use and efficient performance on the NVIDIA GeForce RTX 4080 SUPER.

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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