Can RTX 5090 run Whisper Large v3 Turbo?

Yes — runs locally

~216 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

32 GB

Model size

0.81B

Best quant

Q8_0

VRAM needed

2.0 GB

The verdict

The RTX 5090 (32 GB VRAM) handles Whisper Large v3 Turbo comfortably using the Q8_0 quantization, which fits in 2.0 GB. Expected throughput is around 216 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 5090

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 5090 with Q8_0 quantization, achieving ~950 tok/sec.

Prerequisites

Before starting, ensure you have at least 2GB 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 achieve ~950 tok/sec, using approximately 2.0GB of VRAM. This leaves 30.0GB of VRAM for context, allowing for a practical context window of several minutes of audio input.

1. Install runtimeOllama

pip install ollama
ollama init

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

4. Optimize for RTX 5090

For optimal performance on the NVIDIA GeForce RTX 5090 with 32GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU, enable flash attention with --flash-attn, and consider tensor parallelism with --tensor-parallel-size 2. This configuration will maximize the utilization of the 32GB VRAM, ensuring that the model runs efficiently while maintaining a high token throughput.

Troubleshooting

Out of memory error during inference

Reduce the number of GPU layers with --n-gpu-layers or decrease the batch size.

Low token throughput

Ensure that flash attention is enabled with --flash-attn and that the CUDA backend is properly configured.

Model fails to load

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

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or different use cases. LM Studio is ideal for GUI-based workflows, llama.cpp offers more fine-grained control over model execution, and Jan is suitable for distributed training scenarios. However, for simplicity and ease of use, Ollama remains the best choice for running Whisper Large v3 Turbo on the NVIDIA GeForce RTX 5090.

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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