Can RTX 4060 run Whisper Large v3?
Yes — runs locally
~102 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4060 (8 GB VRAM) handles Whisper Large v3 comfortably using the Q8_0 quantization, which fits in 3.4 GB. Expected throughput is around 102 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 4060
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Large v3 runs at Grade S on the NVIDIA GeForce RTX 4060 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 10/11 or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) with CUDA 11.8 installed.
Expected performance
With the Q8_0 quantization, you can expect ~132 tok/sec processing speed, using 3.4GB of VRAM. The remaining 4.6GB of VRAM provides ample headroom for handling longer audio contexts, allowing for accurate transcription of extended speech segments.
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 ggerganov/whisper.cpp:ggml-large-v3.bin --device cuda --n-gpu-layers 32 --flash-attn
ollama interact ggerganov/whisper.cpp:ggml-large-v3.bin4. Optimize for RTX 4060
For optimal performance on the NVIDIA GeForce RTX 4060 with 8GB VRAM, set --n-gpu-layers to 32 to utilize the GPU efficiently. Enable --flash-attn to speed up attention computations. Given the 8GB VRAM, you can allocate 3.4GB for the model, leaving 4.6GB for context, which should support a practical context window of several minutes of audio.
Troubleshooting
CUDA out of memory error
Reduce the number of GPU layers with --n-gpu-layers 24 or lower.
Slow inference speed
Ensure --flash-attn is enabled and update your NVIDIA drivers to the latest version.
Model not found
Verify the model path and ensure it is correctly downloaded with 'ollama list'.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more specialized setups. LM Studio is ideal for GUI-based workflows, llama.cpp offers more fine-grained control over quantization and performance settings, and Jan is suitable for lightweight, low-resource environments. However, Ollama provides a balanced approach with easy installation and management, making it the recommended choice for the NVIDIA GeForce RTX 4060.
Other models that run great on RTX 4060
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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