Can RTX 4090 run Whisper Tiny English (Quantized)?
Yes — runs locally
~192 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4090 (24 GB VRAM) handles Whisper Tiny English (Quantized) comfortably using the Q5_1 quantization, which fits in 0.1 GB. Expected throughput is around 192 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 4090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Whisper Tiny English (Quantized) on an NVIDIA GeForce RTX 4090 with Ollama. Grade S performance, using Q5_1 quantization, achieving ~1432 tok/sec.
Prerequisites
Before starting, ensure you have at least 32MB of disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) with CUDA 11.8 installed.
Expected performance
With the Q5_1 quantization, you can expect ~1432 tok/sec, using only 0.1GB of VRAM. This leaves 23.9GB of VRAM free, allowing for a practical context window of several thousand tokens, depending on the specific input size.
1. Install runtimeOllama
curl -fsSL https://ollama.com/install.sh | sh
ollama install2. Download the model
Download the 32MB Q5_1 quantized model from Hugging Face.
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 interact ggerganov/whisper.cpp:ggml-tiny.en-q5_1.bin4. Optimize for RTX 4090
For optimal performance on the NVIDIA GeForce RTX 4090 with 24GB VRAM, set --n-gpu-layers to 12 to fully utilize the GPU. Enable flash attention (--flash-attn) for faster inference. With 23.9GB of VRAM remaining, you can handle large context windows without running out of memory.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers (--n-gpu-layers) or enable flash attention (--flash-attn) to optimize memory usage.
Low token generation speed
Ensure CUDA is properly installed and the model is running on the GPU (--device cuda). Adjust the batch size or enable flash attention (--flash-attn) for better performance.
Model fails to load
Verify that the model file is correctly downloaded and not corrupted. Re-run the download command if necessary.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio offers a graphical interface and is suitable for users who prefer a visual setup. llama.cpp provides more control over low-level optimizations and is ideal for advanced users. Jan is a lightweight runtime that can be used for quick prototyping. For the NVIDIA GeForce RTX 4090, Ollama is recommended for its ease of use and high performance.
Other models that run great on RTX 4090
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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