Can RTX 3090 Ti run Whisper Large v3 Turbo?

Yes — runs locally

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

Your VRAM

24 GB

Model size

0.81B

Best quant

Q8_0

VRAM needed

2.0 GB

The verdict

The RTX 3090 Ti (24 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 3090 Ti

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 3090 Ti with Q8_0 quantization, achieving ~713 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 526.47 or later) with CUDA 11.8 installed.

Expected performance

With the recommended settings, you can expect Whisper Large v3 Turbo to run at approximately 713 tokens per second, using around 2.0GB of VRAM. This leaves 22.0GB of VRAM available for context, allowing for a practical context window of several minutes of audio.

1. Install runtimeOllama

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

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 whisper_large_v3_turbo --model ggml-large-v3-turbo.bin
ollama serve

4. Optimize for RTX 3090 Ti

For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, use the --n-gpu-layers parameter to offload layers to the GPU. Set --n-gpu-layers to 48 to utilize the available VRAM efficiently. Additionally, enable flash attention (--flash-attn) to further speed up inference. Given the 24GB VRAM, you can achieve a practical context window of several minutes without running out of memory.

Troubleshooting

Out of memory errors during inference

Reduce the --n-gpu-layers value to 32 or lower to decrease VRAM usage.

Slow inference speeds

Ensure that flash attention (--flash-attn) is enabled and that your CUDA installation is up to date.

Inference fails with segmentation faults

Try reducing the batch size or disabling flash attention.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can also be used to run Whisper Large v3 Turbo. LM Studio is suitable for users who prefer a graphical interface, while llama.cpp offers more fine-grained control over model parameters. Jan is a lightweight alternative for quick testing but may not offer the same level of performance optimization as Ollama.

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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