Can RTX 4070 SUPER run Whisper Large v3?
Yes — runs locally
~132 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4070 SUPER (12 GB VRAM) handles Whisper Large v3 comfortably using the Q8_0 quantization, which fits in 3.4 GB. Expected throughput is around 132 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 4070 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Large v3 runs at Grade S on the NVIDIA GeForce RTX 4070 SUPER with Q8_0 quantization, achieving ~198 tok/sec.
Prerequisites
Before starting, ensure you have at least 3GB of free disk space, a 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 525.60 or later) installed along with CUDA 11.8 or later.
Expected performance
With the Q8_0 quantization, you can expect ~198 tok/sec, utilizing approximately 3.4GB of VRAM. This leaves about 8.6GB of VRAM for context, allowing for a practical context window of several minutes of audio without running out of memory.
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 the Hugging Face repository.
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 4070 SUPER
For optimal performance on the NVIDIA GeForce RTX 4070 SUPER with 12GB VRAM, use the --n-gpu-layers flag to offload some layers to the CPU if needed. Enable flash attention (--flash-attn) for better efficiency. Given the 12GB VRAM, you can set --n-gpu-layers to 32 or higher, leaving around 8.6GB of VRAM for context, which should support a practical context window of several minutes of audio.
Troubleshooting
Out of memory errors during inference
Reduce the number of GPU layers using --n-gpu-layers <N> where <N> is a lower value, or enable flash attention with --flash-attn.
Slow inference speed
Ensure that CUDA is properly installed and that the model is running on the GPU with --device cuda. Also, check if the latest NVIDIA drivers are installed.
Inference fails to start
Verify that the model file is correctly downloaded and not corrupted. Re-run the download command if necessary.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio provides a more user-friendly interface and is suitable for users who prefer a GUI. llama.cpp is highly customizable and can be used for fine-tuning specific parameters. Jan is lightweight and efficient but may lack some features compared to Ollama. Choose an alternative based on your specific needs for customization, performance, or ease of use.
Other models that run great on RTX 4070 SUPER
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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