Can RTX 4070 Ti SUPER run Distil-Whisper Large v3?

Yes — runs locally

~144 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

16 GB

Model size

0.76B

Best quant

Q8_0

VRAM needed

1.9 GB

The verdict

The RTX 4070 Ti SUPER (16 GB VRAM) handles Distil-Whisper Large v3 comfortably using the Q8_0 quantization, which fits in 1.9 GB. Expected throughput is around 144 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Distilled Whisper. 6x faster than large-v3 with 1% accuracy loss.

Setup tutorial: Distil-Whisper Large v3 on RTX 4070 Ti SUPER

AI-generated, GPU-specific. Verified commands for your exact hardware.

TL;DR

Run Distil-Whisper Large v3 on an NVIDIA GeForce RTX 4070 Ti SUPER with Ollama using the Q8_0 quantization. Expect Grade S performance at ~497 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 525.60.12 or later) with CUDA 11.8 installed.

Expected performance

Expect the model to run at approximately 497 tokens per second with 1.9GB VRAM in use, leaving 14.1GB of VRAM for context. This allows for a practical context window of several minutes of audio without running out of VRAM.

1. Install runtimeOllama

pip install ollama
ollama config set device cuda

2. Download the model

Download the Q8_0 quantized version of Distil-Whisper Large v3 (1.4GB file) from the Hugging Face repository.

ollama pull distil-whisper/distil-large-v3-ggml:Q8_0

3. Run it

ollama run distil-whisper/distil-large-v3-ggml:Q8_0 --n-gpu-layers 32 --flash-attn
ollama interactive distil-whisper/distil-large-v3-ggml:Q8_0

4. Optimize for RTX 4070 Ti SUPER

For optimal performance on the NVIDIA GeForce RTX 4070 Ti SUPER with 16GB VRAM, use --n-gpu-layers 32 to offload layers to the GPU. Enable flash attention with --flash-attn for faster inference. With 1.9GB VRAM usage, you have 14.1GB of headroom for larger context windows.

Troubleshooting

Out of memory error during inference

Reduce --n-gpu-layers to 16 or 8 to decrease VRAM usage.

Slow inference speed

Ensure CUDA is properly installed and enabled with 'ollama config set device cuda'. Also, check if flash attention is enabled with '--flash-attn'.

Model not found

Verify that the model was successfully downloaded with 'ollama list' and use the correct model name in the run command.

Alternative runtimes

Alternative runtimes include LM Studio and llama.cpp. Use LM Studio for a more user-friendly interface with visual tools. Use llama.cpp for more fine-grained control over model parameters and optimizations, especially useful for research or custom applications. Jan is another option for lightweight deployment, but it may not offer the same level of performance tuning as Ollama.

Full Distil-Whisper Large v3 details →

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FAQ (20)

What GPU do I need to run Distil-Whisper Large v3?

To run Distil-Whisper Large v3, you need a GPU with at least 1.9 GB of VRAM. NVIDIA GPUs such as the GTX 1060 or higher are recommended.

Is Distil-Whisper Large v3 good for coding?

Distil-Whisper Large v3 is primarily designed for speech recognition tasks and may not be optimized for coding-specific tasks. For coding, models like Codex or CodeLlama are more suitable.

Distil-Whisper Large v3 vs Llama 3.1 8B?

Distil-Whisper Large v3 has 0.76B parameters and is optimized for speech recognition, while Llama 3.1 8B is a larger, more versatile model with 8B parameters, better suited for a wider range of NLP tasks.

Can I run Distil-Whisper Large v3 on a Mac?

Yes, you can run Distil-Whisper Large v3 on a Mac, but ensure your Mac has a compatible GPU with at least 1.9 GB of VRAM. M1 and later Macs with Metal support are recommended.

How much VRAM does Distil-Whisper Large v3 need?

Distil-Whisper Large v3 requires 1.9 GB of VRAM, which is consistent across different quantization levels.

Is Distil-Whisper Large v3 censored?

No, Distil-Whisper Large v3 is not censored. It is an open-source model under the MIT license, allowing for unrestricted use and modification.

Is Distil-Whisper Large v3 commercial-use allowed?

Yes, Distil-Whisper Large v3 is licensed under the MIT license, which allows for commercial use without restrictions.

Distil-Whisper Large v3 context length?

The context length for Distil-Whisper Large v3 is currently unknown. For more detailed information, refer to the model's documentation or source code.

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