Can RTX 5080 run 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 Whisper Large v3 comfortably using the Q8_0 quantization, which fits in 3.4 GB. Expected throughput is around 156 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Largest Whisper model. Best accuracy across all languages and accents.
Setup tutorial: Whisper Large v3 on RTX 5080
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Whisper Large v3 on an NVIDIA GeForce RTX 5080 with Q8_0 quantization for Grade S performance at ~264 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (Windows or Linux), the latest NVIDIA drivers (version 525.60.13 or later), and CUDA 11.7 installed.
Expected performance
With the Q8_0 quantization, you can expect ~264 tok/sec performance, using approximately 3.4GB of VRAM. This leaves 12.6GB of VRAM for context, allowing for a practical context window of up to 1024 tokens.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Q8_0 quantized version of Whisper Large v3 (2.9GB file) from Hugging Face.
ollama pull ggerganov/whisper.cpp:ggml-large-v3.bin3. Run it
ollama run --model ggerganov/whisper.cpp:ggml-large-v3.bin --device cuda
ollama interactive --model ggerganov/whisper.cpp:ggml-large-v3.bin --device cuda4. Optimize for RTX 5080
For optimal performance on the NVIDIA GeForce RTX 5080 with 16GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU. Set --n-gpu-layers to 48 to balance between speed and memory usage. Enable flash attention (--flash-attn) to reduce memory consumption and improve inference speed. With 16GB VRAM, you can achieve a practical context window of up to 1024 tokens while maintaining ~264 tok/sec.
Troubleshooting
CUDA out of memory error
Reduce the number of GPU layers by setting --n-gpu-layers to a lower value, such as 32.
Slow inference speed
Enable flash attention by adding the --flash-attn flag to your run command.
Model not found
Ensure the model is correctly downloaded and the path is correct. Re-run the 'ollama pull' command if necessary.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. Use LM Studio for a more user-friendly interface, llama.cpp for fine-grained control over optimizations, and Jan for a lightweight, easy-to-deploy solution. Choose based on your specific needs for performance, ease of use, and deployment flexibility.
Other models that run great on RTX 5080
FAQ (20)
What GPU do I need to run Whisper Large v3?
To run Whisper Large v3, you need a GPU with at least 3.4 GB of VRAM. NVIDIA GPUs like the RTX 2060 or higher are recommended for optimal performance.
Is Whisper Large v3 good for coding?
Whisper Large v3 is primarily designed for speech recognition and not for coding tasks. It excels in transcribing audio and handling multilingual content.
Whisper Large v3 vs Llama 3.1 8B?
Whisper Large v3 has 1.55B parameters and is optimized for speech recognition, while Llama 3.1 8B has 8B parameters and is more suited for text generation and language understanding tasks.
Can I run Whisper Large v3 on a Mac?
Yes, you can run Whisper Large v3 on a Mac, but ensure your Mac has a compatible GPU with at least 3.4 GB of VRAM for smooth operation.
How much VRAM does Whisper Large v3 need?
Whisper Large v3 requires 3.4 GB of VRAM, regardless of quantization level, to run efficiently.
Is Whisper Large v3 censored?
Whisper Large v3 is not censored. It is designed to handle a wide range of audio inputs and transcribe them accurately without restrictions.
Is Whisper Large v3 commercial-use allowed?
Yes, Whisper Large v3 is licensed under the MIT license, which allows for both commercial and non-commercial use.
Whisper Large v3 context length?
The context length for Whisper Large v3 is not explicitly defined, but it is designed to handle long audio segments effectively.
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