Can RTX 4070 Ti run Whisper Large v3?
Yes — runs locally
~132 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4070 Ti (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 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Large v3 runs at Grade S on the NVIDIA GeForce RTX 4070 Ti with Q8_0 quantization, achieving ~198 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, and the latest NVIDIA drivers (version 525.60.12 or later) with CUDA 11.8 installed.
Expected performance
With the Q8_0 quantization, Whisper Large v3 should run at ~198 tok/sec, using approximately 3.4GB of VRAM, leaving 8.6GB of VRAM available for context. This headroom allows for a practical context window of several minutes of speech, depending on the complexity and resolution of the input.
1. Install runtimeOllama
pip install ollama
ollama setup2. Download the model
Download the Q8_0 quantized version of Whisper Large v3 (2.9GB) 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 --quant Q8_0
ollama interactive4. Optimize for RTX 4070 Ti
For optimal performance on the NVIDIA GeForce RTX 4070 Ti with 12GB VRAM, use the --n-gpu-layers flag to offload some layers to the GPU. Setting --n-gpu-layers 32 should balance performance and VRAM usage. Additionally, enabling flash-attn can further improve speed. Tensor parallelism is not necessary for this model size and GPU configuration.
Troubleshooting
Insufficient VRAM: 'CUDA out of memory'
Reduce the number of GPU layers with --n-gpu-layers 16 or lower.
Performance is slower than expected
Ensure flash-attn is enabled and the latest NVIDIA drivers and CUDA are installed.
Model fails to load
Verify the model file integrity and try re-downloading with 'ollama pull ggerganov/whisper.cpp:ggml-large-v3.bin'.
Alternative runtimes
For users preferring different runtimes, LM Studio offers a more user-friendly GUI and is suitable for those who need a visual interface. llama.cpp is a lightweight alternative for command-line users and can be more flexible for custom configurations. Jan is another runtime that provides advanced features and is ideal for users who need extensive customization options. However, Ollama is recommended for its ease of use and performance on the NVIDIA GeForce RTX 4070 Ti.
Other models that run great on RTX 4070 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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