Can RTX 4060 Ti 16GB run Whisper Large v3?
Yes — runs locally
~114 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4060 Ti 16GB (16 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 4060 Ti 16GB
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Large v3 runs at Grade S on the NVIDIA GeForce RTX 4060 Ti 16GB with Q8_0 quantization, achieving ~264 tok/sec.
Prerequisites
Before starting, ensure you have at least 3.4GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 510.47 or later, and CUDA 11.6 or later installed.
Expected performance
With the Q8_0 quantization, you can expect ~264 tok/sec performance, utilizing 3.4GB of VRAM. This leaves 12.6GB of VRAM for context, allowing for a practical context window of several minutes of audio.
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 interactive4. Optimize for RTX 4060 Ti 16GB
For optimal performance on the NVIDIA GeForce RTX 4060 Ti 16GB, set --n-gpu-layers to 128 to fully utilize the 16GB VRAM. Enable flash-attn for faster inference and consider using tensor parallelism if running multiple instances. The 12.6GB VRAM headroom allows for large context windows.
Troubleshooting
Out of memory error during inference
Reduce --n-gpu-layers or increase batch size to fit within the 16GB VRAM limit.
Slow inference speed
Ensure CUDA is properly installed and enabled in Ollama. Check for any background processes consuming GPU resources.
Model not found
Verify the model path and ensure the model is correctly downloaded and accessible.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. Use LM Studio for a more user-friendly interface, llama.cpp for advanced customization options, and Jan for lightweight, embedded systems. However, Ollama provides the best balance of ease of use and performance for the NVIDIA GeForce RTX 4060 Ti 16GB.
Other models that run great on RTX 4060 Ti 16GB
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.
Want personalized recommendations for your exact setup? Detect my hardware →