Can RTX 4070 SUPER run Whisper Tiny English (Quantized)?
Yes — runs locally
~132 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4070 SUPER (12 GB VRAM) handles Whisper Tiny English (Quantized) comfortably using the Q5_1 quantization, which fits in 0.1 GB. Expected throughput is around 132 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Smallest possible speech recognition model. Only 32MB. English only. Default speech model - guaranteed to run on any iPhone.
Setup tutorial: Whisper Tiny English (Quantized) on RTX 4070 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Whisper Tiny English (Quantized) on a NVIDIA GeForce RTX 4070 SUPER with Grade S performance, using Q5_1 quantization, achieving ~716 tok/sec.
Prerequisites
Before starting, ensure you have at least 32MB of disk space available. Your system should be running Windows or Linux with the latest NVIDIA drivers (version 525.60.13 or later) and CUDA 11.8 installed.
Expected performance
With the recommended settings, you can expect ~716 tok/sec performance while using only 0.1GB of VRAM, leaving 11.9GB available for context. This allows for a practical context window of several minutes of audio, depending on the specific requirements.
1. Install runtimeOllama
pip install ollama
ollama config set runtime cuda2. Download the model
Download the 0.0GB Q5_1 quantized model from Hugging Face.
ollama pull ggerganov/whisper.cpp:ggml-tiny.en-q5_1.bin3. Run it
ollama run --model ggerganov/whisper.cpp:ggml-tiny.en-q5_1.bin --device cuda
ollama interactive4. 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 12 to fully utilize the available VRAM. Enable flash attention with --flash-attn to further speed up inference. Given the 12GB VRAM, you can achieve a large context window without running out of memory.
Troubleshooting
Out of memory error during inference
Reduce the --n-gpu-layers value to 8 or lower to free up more VRAM.
Low tokenization speed
Ensure CUDA is properly installed and configured. Use the --flash-attn flag to enable faster attention mechanisms.
Model fails to load
Verify that the model file is correctly downloaded and not corrupted. Try re-downloading the model using the 'ollama pull' command.
Alternative runtimes
Alternative runtimes include LM Studio and llama.cpp. LM Studio is suitable for users who prefer a graphical interface and need more advanced features like custom training. llama.cpp is ideal for those who want a lightweight, command-line-based solution with minimal dependencies. For the NVIDIA GeForce RTX 4070 SUPER, Ollama provides a well-optimized and easy-to-use runtime for this specific model.
Other models that run great on RTX 4070 SUPER
FAQ (20)
What GPU do I need to run Whisper Tiny English (Quantized)?
Whisper Tiny English (Quantized) requires minimal GPU resources, needing only 0.1 GB of VRAM. It can run efficiently on most modern GPUs, including integrated graphics.
Is Whisper Tiny English (Quantized) good for coding?
Whisper Tiny English (Quantized) is primarily designed for speech recognition and may not be optimized for coding tasks. However, it can be useful for voice-to-text applications in development environments.
Whisper Tiny English (Quantized) vs Llama 3.1 8B?
Whisper Tiny English (Quantized) has only 0.039 billion parameters, making it much smaller and more resource-efficient compared to Llama 3.1 8B, which has 8 billion parameters. It is ideal for low-resource devices but less powerful for complex tasks.
Can I run Whisper Tiny English (Quantized) on a Mac?
Yes, Whisper Tiny English (Quantized) can run on a Mac. It is lightweight and compatible with macOS, requiring minimal system resources.
How much VRAM does Whisper Tiny English (Quantized) need?
Whisper Tiny English (Quantized) requires only 0.1 GB of VRAM, making it suitable for devices with limited graphics memory.
Is Whisper Tiny English (Quantized) censored?
Whisper Tiny English (Quantized) is not censored. It processes speech data as input without any content filtering or restrictions.
Is Whisper Tiny English (Quantized) commercial-use allowed?
Yes, Whisper Tiny English (Quantized) is licensed under the MIT license, allowing commercial use without restrictions.
Whisper Tiny English (Quantized) context length?
The context length for Whisper Tiny English (Quantized) is not explicitly defined, but it is designed to handle short speech segments efficiently.
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