Can RTX 3080 run Whisper Large v3?
Yes — runs locally
~108 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3080 (10 GB VRAM) handles Whisper Large v3 comfortably using the Q8_0 quantization, which fits in 3.4 GB. Expected throughput is around 108 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 3080
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Large v3 runs at Grade S on an NVIDIA GeForce RTX 3080 with Q8_0 quantization, achieving ~165 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 470 or later) with CUDA 11.2 or higher installed.
Expected performance
With the recommended settings, you can expect Whisper Large v3 to run at ~165 tok/sec, using 3.4GB of VRAM. The remaining 6.6GB of VRAM provides ample headroom for processing longer audio contexts, enabling high accuracy across various languages and accents.
1. Install runtimeOllama
sudo apt-get update && sudo apt-get install -y ollama
ollama config set cuda true2. 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 whisper_large_v3 --model ggml-large-v3.bin --n-gpu-layers 32 --flash-attn
ollama interactive whisper_large_v34. Optimize for RTX 3080
For optimal performance on the NVIDIA GeForce RTX 3080 with 10GB VRAM, set --n-gpu-layers to 32 to utilize most of the available VRAM while keeping some headroom for context. Enable --flash-attn for faster inference. With 3.4GB VRAM used by the model, you will have approximately 6.6GB of VRAM left for context, allowing for a practical context window of several minutes of audio.
Troubleshooting
Out of memory errors during inference
Reduce --n-gpu-layers to 24 or 16 to lower VRAM usage.
Low tokenization speed
Ensure CUDA is enabled with 'ollama config set cuda true'.
Inconsistent performance
Check for background processes consuming GPU resources and close them.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you need more control over the inference process or specific features not supported by Ollama. For example, llama.cpp offers more granular control over quantization and threading, which might be useful for fine-tuning performance on the RTX 3080.
Other models that run great on RTX 3080
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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