Can RTX 4080 SUPER run Whisper Tiny English (Quantized)?
Yes — runs locally
~156 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4080 SUPER (16 GB VRAM) handles Whisper Tiny English (Quantized) comfortably using the Q5_1 quantization, which fits in 0.1 GB. Expected throughput is around 156 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 4080 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
The Whisper Tiny English (Quantized) model runs at Grade S on an NVIDIA GeForce RTX 4080 SUPER with Q5_1 quantization, achieving ~955 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
You can expect the model to run at ~955 tok/sec with 0.1GB VRAM in use, leaving 15.9GB of VRAM available for context. This headroom allows for a significant practical context window, enabling longer audio clips to be processed efficiently.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Q5_1 quantized version of the Whisper Tiny English model, which is a 0.0GB file.
ollama pull ggerganov/whisper.cpp:ggml-tiny.en-q5_1.bin3. Run it
ollama run ggerganov/whisper.cpp:ggml-tiny.en-q5_1.bin --device cuda
ollama chat ggerganov/whisper.cpp:ggml-tiny.en-q5_1.bin4. Optimize for RTX 4080 SUPER
For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, set --n-gpu-layers to 12 to fully utilize the GPU. Enable flash-attn for faster inference and consider using tensor parallelism if running multiple instances. With 0.1GB VRAM usage, you will have 15.9GB of VRAM available for context, allowing for a large practical context window.
Troubleshooting
Low token throughput
Increase --n-gpu-layers to 12 and enable flash-attn.
Out of memory errors
Reduce the batch size or context length to fit within the 15.9GB available VRAM.
Inference is slow
Ensure CUDA is properly installed and the correct device is selected with --device cuda.
Alternative runtimes
Alternative runtimes include LM Studio and llama.cpp. LM Studio is useful for a more graphical interface, while llama.cpp offers more control over low-level optimizations. Jan is another option for those who prefer a different API, but Ollama provides a simpler and more streamlined experience for most users on this GPU.
Other models that run great on RTX 4080 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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