Can RTX 4070 Ti SUPER run Whisper Large v3?
Yes — runs locally
~144 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4070 Ti SUPER (16 GB VRAM) handles Whisper Large v3 comfortably using the Q8_0 quantization, which fits in 3.4 GB. Expected throughput is around 144 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 Ti SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Large v3 runs at Grade S on the NVIDIA GeForce RTX 4070 Ti SUPER with Q8_0 quantization, achieving ~264 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows 10/11 or Linux, NVIDIA driver version 510.47.03 or later, and CUDA 11.7 installed.
Expected performance
With the recommended settings, you should expect ~264 tok/sec, using approximately 3.4GB of VRAM. The remaining 12.6GB of VRAM provides ample headroom for handling large context windows, enabling the processing of longer audio clips without performance degradation.
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 interact4. Optimize for RTX 4070 Ti SUPER
For optimal performance on the NVIDIA GeForce RTX 4070 Ti SUPER with 16GB VRAM, set --n-gpu-layers to 100 to fully utilize the GPU. Enable flash attention (--flash-attn) for faster inference. Given the 16GB VRAM, you can allocate up to 3.4GB for the model, leaving 12.6GB for context, which allows for longer audio inputs without running out of memory.
Troubleshooting
Out of memory errors during inference
Reduce the number of layers allocated to the GPU using --n-gpu-layers <number>. For example, try --n-gpu-layers 50.
Slow inference speed
Ensure that flash attention is enabled with --flash-attn. If still slow, check your CUDA installation and driver versions.
Model fails to load
Verify the integrity of the downloaded model file and try downloading it again using the same command.
Alternative runtimes
Alternative runtimes include LM Studio and llama.cpp. LM Studio offers a more user-friendly interface and is suitable for users who prefer a graphical environment. llama.cpp is a lightweight option for those who need minimal dependencies. Jan is another runtime that supports advanced features like tensor parallelism, useful for multi-GPU setups. For the NVIDIA GeForce RTX 4070 Ti SUPER, Ollama is generally the most straightforward and efficient choice.
Other models that run great on RTX 4070 Ti 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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