Can RTX 4060 Ti run Whisper Large v3?
Yes — runs locally
~114 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4060 Ti (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 4060 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Whisper Large v3 on an NVIDIA GeForce RTX 4060 Ti with Ollama using the Q8_0 quantization. Expect Grade S performance at ~132 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.60.13 or later), and CUDA 11.7 or later installed.
Expected performance
With the Q8_0 quantization, expect the model to run at approximately 132 tokens per second, consuming around 3.4GB of VRAM. This leaves 4.6GB of VRAM for context, allowing for a practical context window of several minutes of audio input.
1. Install runtimeOllama
curl -L https://ollama.com/install.sh | bash
ollama install2. Download the model
Download the Q8_0 quantized version of Whisper Large v3 (2.9GB) from Hugging Face.
ollama pull ggerganov/whisper.cpp:ggml-large-v3.bin3. Run it
ollama run whisper_large_v3 --model ggml-large-v3.bin --device cuda
ollama chat whisper_large_v34. Optimize for RTX 4060 Ti
For optimal performance on the NVIDIA GeForce RTX 4060 Ti with 8GB VRAM, use the --n-gpu-layers parameter to offload some layers to the CPU if necessary. Setting --n-gpu-layers to 32 should balance performance and memory usage. Additionally, enable flash attention (--flash-attn) to speed up inference. Tensor parallelism is not typically needed for this model and GPU combination.
Troubleshooting
Out of memory errors during inference
Reduce the number of GPU layers by setting --n-gpu-layers to a lower value, such as 24 or 16.
Slow inference times
Ensure that CUDA and the NVIDIA drivers are up to date. Enable flash attention with --flash-attn.
Model fails to load
Verify that the model file has been downloaded correctly and is not corrupted. Try re-downloading the model.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is a good choice for a more user-friendly interface, while llama.cpp offers more fine-grained control over model execution. Jan is suitable for cloud deployments. For the NVIDIA GeForce RTX 4060 Ti, Ollama provides a balanced approach between ease of use and performance.
Other models that run great on RTX 4060 Ti
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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