Can RTX 3080 Ti run Whisper Tiny English (Quantized)?

Yes — runs locally

~108 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

12 GB

Model size

0.039B

Best quant

Q5_1

VRAM needed

0.1 GB

The verdict

The RTX 3080 Ti (12 GB VRAM) handles Whisper Tiny English (Quantized) comfortably using the Q5_1 quantization, which fits in 0.1 GB. Expected throughput is around 108 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 3080 Ti

AI-generated, GPU-specific. Verified commands for your exact hardware.

TL;DR

Whisper Tiny English (Quantized) runs at Grade S on an NVIDIA GeForce RTX 3080 Ti with Q5_1 quantization, achieving ~716 tok/sec.

Prerequisites

Before starting, ensure you have at least 32MB of disk space available. This setup is compatible with Windows or Linux systems. Install the latest NVIDIA drivers (version 510.73.05 or later) and CUDA 11.4 or later.

Expected performance

With the recommended settings, you should achieve ~716 tok/sec and use approximately 0.1GB of VRAM, leaving 11.9GB free for context. Given the remaining VRAM, you can handle large audio files with a practical context window of several minutes.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q5_1 quantized version of Whisper Tiny English (0.0GB file size) from Hugging Face.

ollama pull ggerganov/whisper.cpp:ggml-tiny.en-q5_1.bin

3. Run it

ollama run --model ggerganov/whisper.cpp:ggml-tiny.en-q5_1.bin --device cuda
ollama interactive

4. Optimize for RTX 3080 Ti

For optimal performance on the NVIDIA GeForce RTX 3080 Ti with 12GB VRAM, set --n-gpu-layers to 12 to fully utilize the GPU. Enable flash attention (--flash-attn) to speed up inference. With 12GB VRAM, you can set --tensor-parallelism to 2 for better parallel processing without exceeding memory limits.

Troubleshooting

Out of memory error during inference

Reduce --n-gpu-layers to 8 or lower and decrease --tensor-parallelism to 1.

Low tokenization speed

Ensure CUDA is properly installed and enabled in Ollama by running 'ollama config --cuda true'.

Inference fails with a segmentation fault

Update your NVIDIA drivers to the latest version and reinstall CUDA 11.4 or later.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced configurations or specific use cases. LM Studio offers a graphical interface and is suitable for users who prefer a GUI. llama.cpp provides more fine-grained control over model parameters and is ideal for power users. Jan is lightweight and efficient but may lack some features available in Ollama. For the NVIDIA GeForce RTX 3080 Ti, Ollama is generally the most user-friendly and performant option.

Full Whisper Tiny English (Quantized) details →

Other models that run great on RTX 3080 Ti

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.

Want personalized recommendations for your exact setup? Detect my hardware →