Whisper Small is a compact automatic speech recognition model developed by OpenAI, boasting 0.24 billion parameters. This model excels in transcribing audio to text with a balance between accuracy and computational efficiency. It is particularly well-suited for real-time applications and scenarios where resources are limited, such as on devices with lower VRAM and processing power. The model’s ability to handle a wide range of audio inputs, from clear recordings to more challenging environments, makes it a versatile choice for various speech recognition tasks.
In its size class, Whisper Small punches well above its weight. Despite having fewer parameters compared to larger models like Whisper Medium or Large, it maintains a high level of accuracy, making it an efficient option for those who need reliable speech-to-text capabilities without the overhead of more resource-intensive models. The Q8_0 quantization further enhances its efficiency, allowing it to run smoothly on hardware with as little as 0.9 GB of VRAM. This makes it an excellent choice for developers and users working with budget-friendly or older hardware, such as low-end GPUs or even some high-end CPUs. Ideal use cases include live transcription, voice assistants, and content creation tools where real-time performance is crucial.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q8_0 | 8 | 0.454 GB | 0.95 GB | 1.45 GB | 85% |
How to run Whisper Small
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 small - 3
Transcribe
./main -m models/ggml-small.bin -f input.wav
Community benchmarks
Real tokens/sec reports from people running Whisper Small 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 Small?
Whisper Small requires 0.95 GB VRAM minimum with Q8_0 quantization. For full precision you need 0.95 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 Small?
To run Whisper Small, you need a GPU with at least 0.9 GB of VRAM. NVIDIA GPUs like the GTX 1050 Ti or better are recommended.
Is Whisper Small good for coding?
Whisper Small is primarily designed for speech-to-text tasks and may not be suitable for coding-specific tasks. For coding, consider models specifically trained on code datasets.
Whisper Small vs Llama 3.1 8B?
Whisper Small has 0.24 billion parameters and is optimized for speech-to-text, while Llama 3.1 8B has 8 billion parameters and is more versatile for general NLP tasks.
Can I run Whisper Small on a Mac?
Yes, you can run Whisper Small on a Mac with an M1 or later chip, which provides sufficient computational power and VRAM.
How much VRAM does Whisper Small need?
Whisper Small requires 0.9 GB of VRAM, which is consistent across different quantization levels.
Is Whisper Small censored?
Whisper Small is not inherently censored, but it adheres to the MIT license, which allows for open use and modification.
Is Whisper Small commercial-use allowed?
Yes, Whisper Small is released under the MIT license, which permits commercial use without restriction.
Whisper Small context length?
The context length for Whisper Small is not explicitly specified, but it generally handles sequences of up to several minutes of audio effectively.
Does Whisper Small support function calling?
Whisper Small does not support function calling as it is primarily designed for speech-to-text transcription.
Whisper Small quantization options?
Whisper Small supports various quantization options, including INT8 and FP16, which can reduce memory usage and improve inference speed.
Can Whisper Small run on CPU?
Yes, Whisper Small can run on a CPU, but it will be significantly slower compared to running on a GPU.
Whisper Small fine-tuning?
Whisper Small can be fine-tuned on specific datasets to improve its performance on particular tasks, such as transcribing domain-specific audio.
Whisper Small system requirements?
To run Whisper Small, you need a system with at least 0.9 GB of VRAM, 4 GB of RAM, and a modern CPU. A GPU is recommended for faster performance.
Whisper Small performance benchmark?
Whisper Small typically processes around 10-15 tokens per second on a mid-range GPU, making it efficient for real-time transcription tasks.
Whisper Small for RAG?
Whisper Small is not designed for Retrieval-Augmented Generation (RAG) tasks; it is primarily used for speech-to-text transcription.
Whisper Small for agents?
Whisper Small can be integrated into voice assistants or chatbots to handle speech input, but it is not a conversational model.
Whisper Small for coding vs general?
Whisper Small is better suited for general speech-to-text tasks rather than coding-specific tasks, which require specialized models.
Whisper Small vs ChatGPT?
Whisper Small is designed for speech-to-text, while ChatGPT is a large language model for text generation and conversation. They serve different purposes.
Whisper Small download size?
The download size for Whisper Small is approximately 500 MB, depending on the quantization level.
Best quant for Whisper Small?
The best quantization for Whisper Small depends on your use case. INT8 offers a good balance between performance and resource efficiency, while FP16 provides higher accuracy.