Can RTX 3070 Ti run Whisper Tiny English (Quantized)?

Yes — runs locally

~90 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

8 GB

Model size

0.039B

Best quant

Q5_1

VRAM needed

0.1 GB

The verdict

The RTX 3070 Ti (8 GB VRAM) handles Whisper Tiny English (Quantized) comfortably using the Q5_1 quantization, which fits in 0.1 GB. Expected throughput is around 90 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 3070 Ti

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

TL;DR

The Whisper Tiny English (Quantized) model runs at Grade S on an NVIDIA GeForce RTX 3070 Ti with Q5_1 quantization, achieving ~477 tok/sec.

Prerequisites

Before starting, ensure you have at least 32MB of disk space available. The system should be running Windows or Linux with the latest NVIDIA drivers (version 470.82.01 or later) and CUDA 11.4 or later installed.

Expected performance

With the Q5_1 quantization, you can expect the model to run at approximately 477 tok/sec, using only 0.1GB of VRAM. This leaves 7.9GB of VRAM available for context, allowing for a practical context window of several minutes of audio.

1. Install runtimeOllama

pip install ollama
ollama config set runtime cuda

2. Download the model

Download the Q5_1 quantized model from Hugging Face, which is only 0.0GB in size.

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

3. Run it

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

4. Optimize for RTX 3070 Ti

For optimal performance on the NVIDIA GeForce RTX 3070 Ti with 8GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU. Set --n-gpu-layers to 12 to utilize the 8GB VRAM effectively. Additionally, enable flash attention (--flash-attn) to speed up inference. Tensor parallelism is not necessary for this small model.

Troubleshooting

Error: CUDA out of memory

Reduce the number of GPU layers with --n-gpu-layers 6

Slow inference speed

Ensure flash attention is enabled with --flash-attn

Model not found

Verify the model path and ensure it is correctly downloaded with 'ollama list'

Alternative runtimes

While Ollama is recommended for its ease of use and CUDA support, you can also consider LM Studio for a more graphical interface or llama.cpp for advanced customization. Jan is another option but may require more manual setup. Choose based on your specific needs for customization or ease of deployment.

Full Whisper Tiny English (Quantized) details →

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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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