Can RTX 5060 run Whisper Large v3?
Yes — runs locally
~114 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5060 (8 GB VRAM) handles Whisper Large v3 comfortably using the Q8_0 quantization, which fits in 3.4 GB. Expected throughput is around 114 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 5060
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Large v3 runs at Grade S on the NVIDIA GeForce RTX 5060 with Q8_0 quantization, achieving ~132 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 525.60.11 or later) with CUDA 11.7 installed.
Expected performance
You can expect ~132 tok/sec with 3.4GB VRAM in use, leaving 4.6GB of VRAM for context. This allows for a practical context window of several minutes of audio, depending on the complexity of the input.
1. Install runtimeOllama
pip install ollama
ollama config set runtime cuda2. 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 ggerganov/whisper.cpp:ggml-large-v3.bin --device cuda --n-gpu-layers 32 --flash-attn true4. Optimize for RTX 5060
For optimal performance on the NVIDIA GeForce RTX 5060 with 8GB VRAM, use --n-gpu-layers 32 to balance between CPU and GPU usage. Enable --flash-attn for faster inference. With 3.4GB VRAM used by the model, you have 4.6GB of VRAM left for context, allowing for a practical context window of several minutes of audio.
Troubleshooting
Out of memory error during inference
Reduce --n-gpu-layers to 24 or 16 to lower VRAM usage.
Low token generation speed
Ensure CUDA is properly installed and configured. Try enabling --flash-attn if not already set.
Model does not load
Check if the model file is corrupted. Re-download the model using the provided command.
Alternative runtimes
Alternative runtimes include LM Studio and llama.cpp. Use LM Studio for a more user-friendly interface, especially for non-technical users. Use llama.cpp for more advanced customization options, but note that it may require more manual setup. For the NVIDIA GeForce RTX 5060, Ollama provides a good balance of ease of use and performance.
Other models that run great on RTX 5060
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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