Can RTX 5070 Ti run Distil-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 Distil-Whisper Large v3 comfortably using the Q8_0 quantization, which fits in 1.9 GB. Expected throughput is around 156 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Distilled Whisper. 6x faster than large-v3 with 1% accuracy loss.
Setup tutorial: Distil-Whisper Large v3 on RTX 5070 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Distil-Whisper Large v3 on an NVIDIA GeForce RTX 5070 Ti with Ollama using the Q8_0 quantization. Expect Grade S performance at ~497 tok/sec.
Prerequisites
Before starting, ensure you have at least 1.4GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.60.13 or later), and CUDA 11.8 installed.
Expected performance
Expect a throughput of approximately 497 tokens per second with 1.9GB of VRAM in use. Given the remaining 14.1GB of VRAM, you can achieve a practical context window of several thousand tokens, depending on the specific input size and complexity.
1. Install runtimeOllama
curl -L https://ollama.com/install.sh | bash
ollama install2. Download the model
Download the Q8_0 quantized version of Distil-Whisper Large v3 (1.4GB file) from the Hugging Face repository.
ollama pull distil-whisper/distil-large-v3-ggml:Q8_03. Run it
ollama run distil-whisper/distil-large-v3-ggml:Q8_0 --n-gpu-layers 32 --flash-attn
ollama chat distil-whisper/distil-large-v3-ggml:Q8_04. Optimize for RTX 5070 Ti
For optimal performance on the NVIDIA GeForce RTX 5070 Ti with 16GB VRAM, set --n-gpu-layers to 32 to utilize the GPU effectively. Enable --flash-attn for faster inference. With 1.9GB VRAM usage, you have 14.1GB of headroom for larger context windows.
Troubleshooting
Out of memory error during inference
Reduce the number of --n-gpu-layers or increase the batch size to fit within the 16GB VRAM limit.
Low token generation speed
Ensure that --flash-attn is enabled and that the latest NVIDIA drivers and CUDA are installed.
Model fails to load
Verify that the model file (ggml-distil-large-v3.bin) is correctly downloaded and not corrupted.
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 quantization and performance settings, and Jan for cloud-based deployment options. Ollama is recommended for its ease of use and efficient performance 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 Distil-Whisper Large v3?
To run Distil-Whisper Large v3, you need a GPU with at least 1.9 GB of VRAM. NVIDIA GPUs such as the GTX 1060 or higher are recommended.
Is Distil-Whisper Large v3 good for coding?
Distil-Whisper Large v3 is primarily designed for speech recognition tasks and may not be optimized for coding-specific tasks. For coding, models like Codex or CodeLlama are more suitable.
Distil-Whisper Large v3 vs Llama 3.1 8B?
Distil-Whisper Large v3 has 0.76B parameters and is optimized for speech recognition, while Llama 3.1 8B is a larger, more versatile model with 8B parameters, better suited for a wider range of NLP tasks.
Can I run Distil-Whisper Large v3 on a Mac?
Yes, you can run Distil-Whisper Large v3 on a Mac, but ensure your Mac has a compatible GPU with at least 1.9 GB of VRAM. M1 and later Macs with Metal support are recommended.
How much VRAM does Distil-Whisper Large v3 need?
Distil-Whisper Large v3 requires 1.9 GB of VRAM, which is consistent across different quantization levels.
Is Distil-Whisper Large v3 censored?
No, Distil-Whisper Large v3 is not censored. It is an open-source model under the MIT license, allowing for unrestricted use and modification.
Is Distil-Whisper Large v3 commercial-use allowed?
Yes, Distil-Whisper Large v3 is licensed under the MIT license, which allows for commercial use without restrictions.
Distil-Whisper Large v3 context length?
The context length for Distil-Whisper Large v3 is currently unknown. For more detailed information, refer to the model's documentation or source code.
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