Can RTX 4070 SUPER run Distil-Whisper Large v3?
Yes — runs locally
~132 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4070 SUPER (12 GB VRAM) handles Distil-Whisper Large v3 comfortably using the Q8_0 quantization, which fits in 1.9 GB. Expected throughput is around 132 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 4070 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Distil-Whisper Large v3 on an NVIDIA GeForce RTX 4070 SUPER with Ollama using the Q8_0 quantization. Expect Grade S performance at ~373 tok/sec.
Prerequisites
Before starting, ensure you have at least 1.5GB 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
With the Q8_0 quantization, you can expect ~373 tok/sec performance while using approximately 1.9GB of VRAM. This leaves about 10.1GB of VRAM available for context, allowing for a practical context window of several minutes of audio.
1. Install runtimeOllama
curl -fsSL https://ollama.com/install.sh | sh
ollama config set cuda2. Download the model
Download the Q8_0 quantized version of Distil-Whisper Large v3 (1.4GB) from HuggingFace.
ollama pull distil-whisper/distil-large-v3-ggml:Q8_03. Run it
ollama run distil-whisper/distil-large-v3-ggml:Q8_0
ollama chat --model distil-whisper/distil-large-v3-ggml:Q8_04. Optimize for RTX 4070 SUPER
For optimal performance on the NVIDIA GeForce RTX 4070 SUPER with 12GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU. Set --n-gpu-layers to 32 to utilize the 12GB VRAM effectively. Enable flash attention (--flash-attn) to speed up inference. With these settings, you should achieve ~373 tok/sec.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers using --n-gpu-layers <num_layers> to a lower value, such as 24.
Low token generation speed
Ensure that flash attention is enabled with --flash-attn. If not, add this flag to your run command.
Model fails to load
Verify that the model file is correctly downloaded and not corrupted. Re-run the download command if necessary.
Alternative runtimes
For users preferring other runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for fine-grained control over quantization and performance, or Jan for web-based access. Ollama is recommended for its ease of use and CUDA backend support on the NVIDIA GeForce RTX 4070 SUPER.
Other models that run great on RTX 4070 SUPER
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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