Can RTX 3070 run Whisper Large v3 Turbo?

Yes — runs locally

~90 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

8 GB

Model size

0.81B

Best quant

Q8_0

VRAM needed

2.0 GB

The verdict

The RTX 3070 (8 GB VRAM) handles Whisper Large v3 Turbo comfortably using the Q8_0 quantization, which fits in 2.0 GB. Expected throughput is around 90 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 3070

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 3070 with Q8_0 quantization, achieving ~238 tok/sec.

Prerequisites

Before starting, ensure you have at least 1.5GB of free disk space, a 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 512.15 or later) installed. You also need CUDA 11.4 or later.

Expected performance

With the recommended settings, you can expect ~238 tok/sec with 2.0GB VRAM in use, leaving 6.0GB of VRAM for context. This allows for a practical context window of several minutes of audio, depending on the specific requirements.

1. Install runtimeOllama

curl -sSL https://ollama.ai/install.sh | sh
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 --model ggerganov/whisper.cpp:ggml-large-v3-turbo.bin --n-gpu-layers 20 --flash-attn
ollama interactive

4. Optimize for RTX 3070

For optimal performance on the NVIDIA GeForce RTX 3070 with 8GB VRAM, set `--n-gpu-layers` to 20 to utilize most of the available VRAM while leaving some headroom for context. Enable `--flash-attn` for faster attention computations. Tensor parallelism is not necessary for this model size and GPU configuration.

Troubleshooting

Out of memory errors during inference

Reduce the value of `--n-gpu-layers` to 15 or 10 to free up more VRAM for context.

Slow inference speed

Ensure that `--flash-attn` is enabled and that your CUDA drivers are up to date.

Model fails to load

Verify that the model file is correctly downloaded and not corrupted. Re-run the `ollama pull` command to re-download the model.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you prefer a different interface or additional features. LM Studio provides a more user-friendly GUI, llama.cpp offers more fine-grained control over quantization and performance tuning, and Jan is suitable for lightweight deployments. However, Ollama is recommended for its ease of use and robust performance on the NVIDIA GeForce RTX 3070.

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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