Can RTX 5070 Ti run Whisper Large v3?
Yes — runs locally
~156 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5070 Ti (16 GB VRAM) handles Whisper Large v3 comfortably using the Q8_0 quantization, which fits in 3.4 GB. Expected throughput is around 156 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 5070 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Large v3 runs at Grade S on the NVIDIA GeForce RTX 5070 Ti with Q8_0 quantization, achieving ~264 tok/sec.
Prerequisites
Before starting, ensure you have at least 3GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) with CUDA 11.8 installed.
Expected performance
You can expect ~264 tok/sec with 3.4GB VRAM in use, leaving 12.6GB for context. This allows for a practical context window of several minutes of audio, depending on the complexity and resolution of the input.
1. Install runtimeOllama
curl -fsSL https://ollama.com/install.sh | sh
ollama install2. 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 whisper_large_v3 --model-path ggml-large-v3.bin
ollama chat --model whisper_large_v34. Optimize for RTX 5070 Ti
For optimal performance on the NVIDIA GeForce RTX 5070 Ti with 16GB VRAM, set --n-gpu-layers to 64 to fully utilize the GPU. Enable flash attention (--flash-attn) to speed up inference. With 3.4GB VRAM used by the model, you have 12.6GB of VRAM left for context, allowing for a large context window.
Troubleshooting
Out of memory error during inference
Reduce the number of layers loaded into GPU memory using --n-gpu-layers 32 or lower.
Low token generation speed
Ensure that flash attention is enabled with --flash-attn and that the latest NVIDIA drivers and CUDA are installed.
Model not found error
Verify the model path and ensure the model file is correctly downloaded and accessible.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. Use LM Studio for a more user-friendly interface, llama.cpp for fine-grained control over inference settings, and Jan for cloud-based deployment options. However, Ollama provides a balanced approach with ease of use and performance, making it suitable for most users on the NVIDIA GeForce RTX 5070 Ti.
Other models that run great on RTX 5070 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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