GGML
ggml is a machine learning tensor library written in C that provides high performance and large model support on commodity hardware. The library supports 16-bit floats, integer quantization, automatic differentiation, and built-in optimization algorithms like ADAM and L-BFGS. It is optimized for Apple Silicon, utilizes AVX/AVX2 intrinsics on x86 architectures, offers WebAssembly support, and performs zero memory allocations during runtime. Use cases include voice command detection on Raspberry Pi, running multiple instances on Apple devices, and deploying high-efficiency models on GPUs. ggml promotes simplicity, openness, and exploration while fostering community contributions and innovation.
Features
- Written in C
- 16-bit float support
- Integer quantization support (4-bit
- 5-bit
- 8-bit)
- Automatic differentiation
- Built-in optimization algorithms (ADAM
- L-BFGS)
Use Cases
- Voice recognition enthusiasts
- Apple device users
- AI researchers
- Machine learning developers
- Web developers
- Open-source contributors
- Tech companies
- Embedded system developers