The Whisper Tiny English (Quantized) model by OpenAI is a lightweight automatic speech recognition (ASR) model designed for efficient local deployment. With only 0.039 billion parameters, this quantized version of the Whisper architecture is optimized for minimal resource usage while maintaining reasonable accuracy for English speech recognition tasks. It is particularly well-suited for applications where computational resources are limited, such as on edge devices or low-end computers. The model's small size and low VRAM requirement (0.1–0.1 GB) make it highly efficient, allowing it to run smoothly even on hardware with very limited memory.
In its size class, the Whisper Tiny English (Quantized) model punches above its weight. While it may not match the accuracy of larger, more resource-intensive ASR models, it offers a compelling balance between performance and efficiency. This makes it an excellent choice for real-time speech-to-text applications, such as voice commands, transcription of short audio clips, or basic dictation tasks. Users who prioritize low latency and minimal power consumption will find this model particularly useful. Ideal hardware for running this model includes Raspberry Pi, low-end laptops, or any device with limited processing power and memory.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q5_1 | 5 | 0.032 GB | 0.1 GB | 0.2 GB | 65% |
How to run Whisper Tiny English (Quantized)
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
Pure-C reimplementation. CoreML/Metal/CUDA. 1-line setup.
whisper.cpp home →- 1
Build
git clone https://github.com/ggerganov/whisper.cpp && cd whisper.cpp && make - 2
Get the model
bash ./models/download-ggml-model.sh tiny.en - 3
Transcribe
./main -m models/ggml-tiny.en.bin -f input.wav
Community benchmarks
Real tokens/sec reports from people running Whisper Tiny English (Quantized) on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
how much VRAM do I need to run Whisper Tiny English (Quantized)?
Whisper Tiny English (Quantized) requires 0.1 GB VRAM minimum with Q5_1 quantization. For full precision you need 0.1 GB.
which quant should I pick?
Q4_K_M is the best quality/VRAM balance — ~92% of FP16 quality at ~25% the footprint. Q8_0 is near-lossless if you have the headroom.
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.
Does Whisper Tiny English (Quantized) support function calling?
Whisper Tiny English (Quantized) does not support function calling as it is a speech recognition model and not a language model designed for interactive functions.
Whisper Tiny English (Quantized) quantization options?
Whisper Tiny English (Quantized) supports various quantization options, including INT8 and FP16, which help reduce model size and improve inference speed.
Can Whisper Tiny English (Quantized) run on CPU?
Yes, Whisper Tiny English (Quantized) can run on CPU. Its small size makes it efficient even on low-power CPUs.
Whisper Tiny English (Quantized) fine-tuning?
Whisper Tiny English (Quantized) can be fine-tuned for specific speech recognition tasks, but its small size may limit the extent of improvements you can achieve.
Whisper Tiny English (Quantized) system requirements?
Whisper Tiny English (Quantized) requires minimal system resources: 0.1 GB VRAM, 32MB storage, and a modern CPU or GPU. It is compatible with most devices, including smartphones and low-end computers.
Whisper Tiny English (Quantized) performance benchmark?
Whisper Tiny English (Quantized) processes speech at a rate of approximately 100 tokens per second on a mid-range GPU, making it highly efficient for real-time applications.
Whisper Tiny English (Quantized) for RAG?
Whisper Tiny English (Quantized) is not designed for Retrieval-Augmented Generation (RAG) tasks. It is primarily used for speech recognition and converting audio to text.
Whisper Tiny English (Quantized) for agents?
Whisper Tiny English (Quantized) can be used in agent-based systems for voice commands and speech-to-text conversion, but it is not suitable for generating responses or complex interactions.
Whisper Tiny English (Quantized) for coding vs general?
Whisper Tiny English (Quantized) is better suited for general speech recognition tasks due to its small size and efficiency. For coding-specific tasks, more specialized models may be more appropriate.
Whisper Tiny English (Quantized) vs ChatGPT?
Whisper Tiny English (Quantized) is a speech recognition model, while ChatGPT is a language model designed for text generation. They serve different purposes and are not directly comparable.
Whisper Tiny English (Quantized) download size?
The download size for Whisper Tiny English (Quantized) is approximately 32MB, making it very lightweight and easy to deploy on various devices.
Best quant for Whisper Tiny English (Quantized)?
The best quantization for Whisper Tiny English (Quantized) depends on your specific needs. INT8 provides a good balance between size and performance, while FP16 offers higher precision for critical applications.