Can RTX 5080 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 5080 (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 5080

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

TL;DR

Whisper Large v3 Turbo runs at Grade S on the NVIDIA GeForce RTX 5080 with Q8_0 quantization, achieving ~475 tok/sec.

Prerequisites

Before starting, ensure you have at least 1.5GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) with CUDA 11.8 installed.

Expected performance

With the recommended settings, you can expect the model to run at approximately 475 tokens per second, using 2.0GB of VRAM. The remaining 14.0GB of VRAM provides ample headroom for a practical context window, allowing for longer audio processing without running out of memory.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q8_0 quantized model (1.5GB) from the Hugging Face repository.

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

3. Run it

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

4. Optimize for RTX 5080

For optimal performance on the NVIDIA GeForce RTX 5080 with 16GB VRAM, set --n-gpu-layers to 32 to utilize the GPU effectively. Enable --flash-attn for faster attention computations and set --tensor-parallelism to 2 to leverage the multi-core architecture. This configuration ensures that the model runs efficiently within the 2.0GB VRAM requirement, leaving 14.0GB of VRAM for context and other tasks.

Troubleshooting

Insufficient VRAM error

Reduce the number of --n-gpu-layers to 16 or disable --tensor-parallelism.

Slow inference speed

Ensure that --flash-attn is enabled and check your CUDA installation for any issues.

Model not loading

Verify the integrity of the downloaded model file and try re-downloading it.

Alternative runtimes

For users preferring different runtimes, consider LM Studio for a more graphical interface, llama.cpp for advanced customization, or Jan for a lightweight alternative. Each runtime has its own strengths; LM Studio is ideal for beginners, llama.cpp offers deep customization options, and Jan is suitable for resource-constrained environments.

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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