Can RTX 4060 Ti 16GB run Whisper Tiny English (Quantized)?
Yes — runs locally
~114 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4060 Ti 16GB (16 GB VRAM) handles Whisper Tiny English (Quantized) comfortably using the Q5_1 quantization, which fits in 0.1 GB. Expected throughput is around 114 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 4060 Ti 16GB
AI-generated, GPU-specific. Verified commands for your exact hardware.
Whisper Tiny English (Quantized) runs at Grade S on the NVIDIA GeForce RTX 4060 Ti 16GB with Q5_1 quantization, achieving ~955 tok/sec.
Prerequisites
Before starting, ensure you have at least 32MB of disk space, a compatible OS (Windows or Linux), and the latest NVIDIA drivers (version 528.49 or later) installed along with CUDA 11.8.
Expected performance
With the Q5_1 quantization, you can expect ~955 tok/sec performance, using only 0.1GB of VRAM. This leaves 15.9GB of VRAM available for context, allowing for a practical context window of several minutes of audio.
1. Install runtimeOllama
curl -s https://ollama.com/install.sh | bash
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 chat ggerganov/whisper.cpp:ggml-tiny.en-q5_1.bin4. Optimize for RTX 4060 Ti 16GB
For optimal performance on the NVIDIA GeForce RTX 4060 Ti 16GB, use the --n-gpu-layers flag to offload layers to the GPU. Given the 16GB VRAM, you can set --n-gpu-layers to a high value like 128. Additionally, enable flash attention (--flash-attn) to further optimize speed and memory usage. Tensor parallelism is not necessary for this small model.
Troubleshooting
Model does not load due to insufficient VRAM.
Ensure you are using the Q5_1 quantization and that your GPU drivers and CUDA are up to date.
Performance is lower than expected.
Check that you have enabled flash attention (--flash-attn) and set --n-gpu-layers to a high value like 128.
Audio input is not recognized correctly.
Verify that the audio input is in a supported format and that the sample rate matches the model's requirements.
Alternative runtimes
Alternative runtimes include LM Studio and llama.cpp. LM Studio is useful for a more graphical interface and easier model management, while llama.cpp offers more fine-grained control over model execution. For the NVIDIA GeForce RTX 4060 Ti 16GB, Ollama provides a balanced approach with ease of use and good performance.
Other models that run great on RTX 4060 Ti 16GB
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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