Whisper Tiny is a lightweight automatic speech recognition (ASR) model developed by OpenAI, designed to transcribe spoken language into text with minimal computational resources. With only 39 million parameters, this model is exceptionally compact, making it suitable for devices with limited processing power and memory. Despite its small size, Whisper Tiny delivers surprisingly competent performance for basic ASR tasks, such as transcribing short audio clips or simple voice commands. It is particularly useful in scenarios where real-time processing is required but the hardware is constrained, such as on Raspberry Pi or other low-power embedded systems.
In its size class, Whisper Tiny stands out for its efficiency and resource-light footprint. While it may not match the accuracy of larger models like the full-sized Whisper, it punches well above its weight in terms of speed and energy consumption. This makes it an excellent choice for developers and hobbyists who need a quick, lightweight solution without the overhead of more complex models. Users with modest hardware, such as laptops or even smartphones, can deploy this model with ease, requiring only 0.2 GB of VRAM. For those looking to integrate basic speech recognition into IoT devices or mobile applications, Whisper Tiny is a solid, practical option.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q8_0 | 8 | 0.075 GB | 0.2 GB | 0.5 GB | 70% |
How to run Whisper Tiny
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 - 3
Transcribe
./main -m models/ggml-tiny.bin -f input.wav
Community benchmarks
Real tokens/sec reports from people running Whisper Tiny 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?
Whisper Tiny requires 0.2 GB VRAM minimum with Q8_0 quantization. For full precision you need 0.2 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?
Whisper Tiny can run on any GPU with at least 0.2 GB of VRAM, but it can also run efficiently on CPUs.
Is Whisper Tiny good for coding?
Whisper Tiny is primarily designed for speech recognition and supports 99 languages, making it less suitable for coding tasks which typically require text generation or code understanding.
Whisper Tiny vs Llama 3.1 8B?
Whisper Tiny has only 0.039 billion parameters, making it much smaller and more efficient than Llama 3.1 8B, which has 8 billion parameters. Whisper Tiny is optimized for speech recognition, while Llama 3.1 8B is better suited for text generation tasks.
Can I run Whisper Tiny on a Mac?
Yes, Whisper Tiny can run on Macs with both Intel and M1/M2 chips. It requires minimal resources and can run efficiently on CPUs as well.
How much VRAM does Whisper Tiny need?
Whisper Tiny requires only 0.2 GB of VRAM, making it suitable for devices with limited graphics memory.
Is Whisper Tiny censored?
Whisper Tiny is not censored. It is an open-source model released under the MIT license, allowing for unrestricted use and modification.
Is Whisper Tiny commercial-use allowed?
Yes, Whisper Tiny is licensed under the MIT license, which allows for commercial use without restrictions.
Whisper Tiny context length?
The context length for Whisper Tiny is not explicitly defined, but it is designed to handle short to medium-length audio clips effectively.
Does Whisper Tiny support function calling?
Whisper Tiny is a speech recognition model and does not support function calling. It is designed to transcribe audio into text.
Whisper Tiny quantization options?
Whisper Tiny supports various quantization options, including INT8 and INT16, which can reduce the model size and improve inference speed without significant loss in accuracy.
Can Whisper Tiny run on CPU?
Yes, Whisper Tiny can run efficiently on CPUs, making it suitable for devices without dedicated GPUs.
Whisper Tiny fine-tuning?
Whisper Tiny can be fine-tuned for specific tasks or languages, but it may require additional data and computational resources to achieve optimal performance.
Whisper Tiny system requirements?
Whisper Tiny requires minimal system resources: at least 0.2 GB of VRAM (or a CPU), 75 MB of storage, and a modern operating system (Windows, macOS, Linux).
Whisper Tiny performance benchmark?
Whisper Tiny processes audio at approximately 10-20 tokens per second on a mid-range CPU, making it suitable for real-time speech recognition tasks.
Whisper Tiny for RAG?
Whisper Tiny is not designed for Retrieval-Augmented Generation (RAG) tasks. It is primarily used for speech-to-text transcription.
Whisper Tiny for agents?
Whisper Tiny can be integrated into voice assistants or chatbots to handle speech input, but it does not generate responses; it only transcribes audio.
Whisper Tiny for coding vs general?
Whisper Tiny is better suited for general speech recognition tasks rather than coding, as it focuses on transcribing spoken language rather than generating or understanding code.
Whisper Tiny vs ChatGPT?
Whisper Tiny is a speech recognition model, while ChatGPT is a large language model designed for text generation. Whisper Tiny is much smaller and more efficient, making it ideal for real-time speech-to-text applications.
Whisper Tiny download size?
The download size for Whisper Tiny is approximately 75 MB, making it lightweight and easy to deploy on various devices.
Best quant for Whisper Tiny?
The best quantization option for Whisper Tiny depends on your specific needs. INT8 provides a good balance between model size and performance, while INT16 offers higher accuracy with slightly larger model size.