Can RTX 5070 run Whisper Large v3?
Yes — runs locally
~132 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5070 (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 5070
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Large v3 runs at Grade S on the NVIDIA GeForce RTX 5070 with Q8_0 quantization, achieving ~198 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.12 or later) with CUDA 11.8 installed.
Expected performance
With the recommended settings, you can expect ~198 tok/sec performance, using 3.4GB of VRAM. The remaining 8.6GB of VRAM provides ample headroom for handling longer audio contexts, making it suitable for real-time transcription tasks.
1. Install runtimeOllama
curl -O https://ollama.io/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.cpp:ggml-large-v3.bin --model-path ./ggml-large-v3.bin --n-gpu-layers 32 --flash-attn4. Optimize for RTX 5070
For optimal performance on the NVIDIA GeForce RTX 5070 with 12GB VRAM, set --n-gpu-layers to 32 to utilize the GPU effectively. Enable --flash-attn to speed up attention computations. With 3.4GB VRAM used by the model, you will have approximately 8.6GB 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 --n-gpu-layers to 24 or 16 to lower VRAM usage.
Low token generation speed
Ensure that --flash-attn is enabled and that your CUDA drivers are up to date.
Model fails to load
Verify the integrity of the downloaded model file and try re-downloading it.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or if you prefer a different interface. LM Studio is ideal for a GUI-based workflow, while llama.cpp offers more control over quantization and optimization. Jan is a lightweight option for quick prototyping. However, Ollama provides a balanced approach with ease of use and good performance on the NVIDIA GeForce RTX 5070.
Other models that run great on RTX 5070
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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