Can RTX 5070 Ti 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 5070 Ti (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 5070 Ti

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 5070 Ti 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), the latest NVIDIA drivers (version 525.60.13 or later), and CUDA 11.8 installed.

Expected performance

With the Q8_0 quantization, you can expect ~475 tok/sec, using 2.0GB of VRAM. The remaining 14.0GB of VRAM provides ample headroom for handling longer audio contexts, making it suitable for real-time transcription tasks.

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

4. Optimize for RTX 5070 Ti

For optimal performance on the NVIDIA GeForce RTX 5070 Ti with 16GB VRAM, set --n-gpu-layers to 48 to utilize the GPU efficiently. Enable flash attention with --flash-attn to speed up inference. Given the 16GB VRAM, you can allocate 2.0GB for the model, leaving 14.0GB for context, which allows for a practical context window of several minutes of audio.

Troubleshooting

CUDA out of memory error

Reduce the number of GPU layers with --n-gpu-layers 32 or lower.

Slow inference speed

Ensure flash attention is enabled with --flash-attn.

Model not found

Verify the model path and ensure it is correctly downloaded with 'ollama list'.

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is ideal for a more user-friendly interface, while llama.cpp offers more customization options. Jan is suitable for lightweight deployments. For the NVIDIA GeForce RTX 5070 Ti, 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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