Can RTX 3090 Ti run Whisper Tiny English (Quantized)?
Yes — runs locally
~132 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3090 Ti (24 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 3090 Ti
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 3090 Ti with Q5_1 quantization, achieving ~1432 tok/sec.
Prerequisites
Before starting, ensure you have at least 32MB of disk space available. This setup is compatible with Windows or Linux, and requires the NVIDIA driver version 470.82.01 or later, along with CUDA 11.4 or later installed.
Expected performance
You can expect the model to run at approximately ~1432 tok/sec with only 0.1GB of VRAM in use, leaving 23.9GB of VRAM available for context. This allows for a practical context window of several minutes of audio, depending on the complexity of the input.
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 --model ggerganov/whisper.cpp:ggml-tiny.en-q5_1.bin4. Optimize for RTX 3090 Ti
For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, set --n-gpu-layers to 12 to fully utilize the GPU. Enable flash attention (--flash-attn) for faster inference and consider using tensor parallelism (--tensor-parallel-size 2) to further speed up processing. With 23.9GB of VRAM left after loading the model, you can handle large context windows without running out of memory.
Troubleshooting
If you encounter a CUDA initialization error, it may be due to an outdated NVIDIA driver.
Update your NVIDIA driver to version 470.82.01 or later.
If the model runs but the token generation rate is significantly lower than expected, check if the --flash-attn flag is enabled.
Add the --flash-attn flag to your run command: ollama run ggerganov/whisper.cpp:ggml-tiny.en-q5_1.bin --device cuda --flash-attn
If you experience out-of-memory errors, reduce the number of GPU layers or disable tensor parallelism.
Try reducing --n-gpu-layers to 6 or disabling tensor parallelism: ollama run ggerganov/whisper.cpp:ggml-tiny.en-q5_1.bin --device cuda --n-gpu-layers 6
Alternative runtimes
Alternatively, you can use LM Studio for a more user-friendly interface, llama.cpp for more advanced customization options, or the Jan runtime for lightweight deployment. Choose Ollama for its ease of use and robust community support, especially suitable for the NVIDIA GeForce RTX 3090 Ti.
Other models that run great on RTX 3090 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.
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