Can RTX 3070 Ti run Whisper Large v3?
Yes — runs locally
~90 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3070 Ti (8 GB VRAM) handles Whisper Large v3 comfortably using the Q8_0 quantization, which fits in 3.4 GB. Expected throughput is around 90 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 3070 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Whisper Large v3 on an NVIDIA GeForce RTX 3070 Ti with Q8_0 quantization for 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, and the latest NVIDIA drivers (version 512.15 or later) installed along with CUDA 11.4 or higher.
Expected performance
With the Q8_0 quantization, you can expect ~132 tok/sec processing speed, using 3.4GB of VRAM, leaving 4.6GB for context. This should allow for a practical context window of several minutes of audio without running out of VRAM.
1. Install runtimeOllama
curl -O https://ollama.com/install.sh
bash install.sh2. 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 --device cuda
ollama interact whisper_large_v34. Optimize for RTX 3070 Ti
For optimal performance on the NVIDIA GeForce RTX 3070 Ti with 8GB VRAM, use the --n-gpu-layers flag to offload some layers to CPU if necessary. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. 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 the number of GPU layers using --n-gpu-layers <N> where <N> is the number of layers to keep on the GPU. For example: --n-gpu-layers 24
Slow inference speed
Enable flash attention by adding the --flash-attn flag to your run command.
Model not loading
Ensure that the model file is correctly downloaded and the path is correct. Try re-downloading the model using the 'ollama pull' command.
Alternative runtimes
For users preferring different runtimes, consider LM Studio for a more graphical interface, llama.cpp for more advanced quantization options, or Jan for a lightweight, easy-to-use CLI. Each has its own strengths, but Ollama provides a balanced approach with good performance and ease of use on the RTX 3070 Ti.
Other models that run great on RTX 3070 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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