monatis/clip.cpp

CLIP inference in plain C/C++ with no extra dependencies

What it solves

clip.cpp is a lightweight, dependency-free C/C++ implementation of OpenAI's CLIP (Contrastive Language-Image Pre-training) model. It allows users to run CLIP inference on devices with limited computation or memory, such as serverless environments where fast startup times are critical to avoid cold starts.

How it works

Built on the GGML library, the project provides a high-performance inference engine that supports models from both OpenAI and LAION in GGUF format. It implements quantization (4-bit, 5-bit, and 8-bit) to drastically reduce model size—for example, a 4-bit quantized model can be as small as 85.6 MB. The system supports three model variants: text-only, vision-only, and the standard two-tower architecture.

Who it’s for

Developers building applications for memory-constrained devices, serverless deployments, and those who need a fast, lightweight alternative to large ML frameworks like PyTorch or TensorFlow.

Highlights

  • Zero Dependencies: Pure C/C++ implementation with no requirement for heavy ML libraries.
  • Quantization Support: Reduces memory footprint via 4, 5, and 8-bit quantization.
  • Python Bindings: Provides Python access via ctypes without requiring NumPy or other third-party packages.
  • Versatile Use Cases: Includes built-in examples for zero-shot image labeling and semantic image search.
  • Fast Startup: Optimized for low-latency cold starts in serverless environments.

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