Can RTX 4060 Ti 16GB run Distil-Whisper Large v3?

Yes — runs locally

~114 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 4060 Ti 16GB (16 GB VRAM) handles Distil-Whisper Large v3 comfortably using the Q8_0 quantization, which fits in 1.9 GB. Expected throughput is around 114 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 4060 Ti 16GB

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

TL;DR

The Distil-Whisper Large v3 model runs at Grade S on the NVIDIA GeForce RTX 4060 Ti 16GB, achieving ~497 tok/sec with the Q8_0 quantization. It uses 1.9GB of VRAM, leaving ample headroom for large context windows.

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 526.95 or later) with CUDA 11.7 or later installed.

Expected performance

You can expect the model to run at ~497 tok/sec with 1.9GB VRAM in use, leaving 14.1GB of VRAM available for context. This allows for a practical context window of several thousand tokens, depending on the specific input size and complexity.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q8_0 quantized version of the model (1.4GB) from the HuggingFace 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

4. Optimize for RTX 4060 Ti 16GB

For optimal performance on the NVIDIA GeForce RTX 4060 Ti 16GB, use --n-gpu-layers 32 to fully utilize the 16GB VRAM. Enable --flash-attn for faster and more efficient attention computations. With 1.9GB VRAM used by the model, you have 14.1GB available for context, allowing for large context windows without running out of memory.

Troubleshooting

Out of memory error during inference

Reduce the number of GPU layers with --n-gpu-layers 16 or increase the batch size to distribute the workload more efficiently.

Low token generation speed

Ensure that flash attention is enabled with --flash-attn and that the CUDA toolkit is up to date.

Model not loading

Verify that the model file is correctly downloaded and not corrupted. Try redownloading the model using the 'ollama pull' command.

Alternative runtimes

For users preferring a different runtime, consider LM Studio for a more user-friendly GUI, llama.cpp for fine-grained control over model parameters, or Jan for a lightweight, web-based interface. Ollama is recommended for its ease of use and robust performance on the NVIDIA GeForce RTX 4060 Ti 16GB.

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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