Can RTX 4070 Ti run Whisper Large v3 Turbo?

Yes — runs locally

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

Your VRAM

12 GB

Model size

0.81B

Best quant

Q8_0

VRAM needed

2.0 GB

The verdict

The RTX 4070 Ti (12 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 4070 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 4070 Ti with Q8_0 quantization, achieving ~356 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 ~356 tok/sec, using approximately 2.0GB of VRAM. This leaves 10.0GB of VRAM headroom for context, allowing for a practical context window of several minutes of audio 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 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 --n-gpu-layers 12 --flash-attn --tensor-parallelism 2

4. Optimize for RTX 4070 Ti

For optimal performance on the NVIDIA GeForce RTX 4070 Ti with 12GB VRAM, set --n-gpu-layers to 12 to utilize most of the GPU memory. Enable --flash-attn for faster attention computation and set --tensor-parallelism to 2 to distribute the workload efficiently across the GPU cores. This configuration will maximize the token processing speed while keeping the VRAM usage within the 12GB limit.

Troubleshooting

Out of memory error during inference

Reduce the number of --n-gpu-layers to 8 or 4 to lower VRAM usage.

Low token processing speed

Ensure that --flash-attn is enabled and --tensor-parallelism is set to 2.

Inference fails to start

Check that the NVIDIA drivers and CUDA are correctly installed and up to date.

Alternative runtimes

Alternative runtimes include LM Studio and llama.cpp. LM Studio is suitable for users who prefer a graphical interface and need more advanced model management features. llama.cpp is ideal for those who want more control over the command-line and custom configurations. Jan is another option for users who need a lightweight, easy-to-use runtime, but it may not offer the same level of performance tuning as Ollama.

Full Whisper Large v3 Turbo details →

Other models that run great on RTX 4070 Ti

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.

Want personalized recommendations for your exact setup? Detect my hardware →