Can RTX 5080 run Distil-Whisper Large v3?
Yes — runs locally
~156 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5080 (16 GB VRAM) handles Distil-Whisper Large v3 comfortably using the Q8_0 quantization, which fits in 1.9 GB. Expected throughput is around 156 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 5080
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Distil-Whisper Large v3 on your NVIDIA GeForce RTX 5080 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 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 525.60 or later) installed along with CUDA 11.8 or higher.
Expected performance
With the Q8_0 quantization, you can expect a performance of ~497 tok/sec, consuming around 1.9GB of VRAM. This leaves you with 14.1GB of VRAM for context, enabling you to process longer audio segments without running out of memory.
1. Install runtimeOllama
curl -s https://ollama.com/install.sh | bash
ollama install2. Download the model
Download the Q8_0 quantized version of Distil-Whisper Large v3 (1.4GB file) from the HuggingFace repository.
ollama pull distil-whisper/distil-large-v3-ggml:Q8_03. Run it
ollama run distil-whisper/distil-large-v3-ggml:Q8_0 --n-gpu-layers 128 --flash-attn
ollama chat --model distil-whisper/distil-large-v3-ggml:Q8_04. Optimize for RTX 5080
For optimal performance on your NVIDIA GeForce RTX 5080 with 16GB VRAM, set --n-gpu-layers to 128 to utilize the GPU effectively. Enable --flash-attn to speed up attention computations. Given the 1.9GB VRAM usage, you will have approximately 14.1GB of VRAM left for context, allowing for a practical context window of several minutes of audio.
Troubleshooting
Out of memory errors during inference
Reduce the number of --n-gpu-layers or increase the batch size to fit within the available VRAM.
Slow inference times
Ensure that --flash-attn is enabled and that your CUDA installation is up-to-date.
Model not loading
Verify that the model file has been downloaded correctly and that the Ollama runtime is properly installed.
Alternative runtimes
For users preferring different runtimes, consider LM Studio for a more graphical interface, llama.cpp for lightweight deployment, or Jan for advanced customization options. Ollama is recommended for its ease of use and performance optimization on NVIDIA GPUs like the RTX 5080.
Other models that run great on RTX 5080
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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