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What is GGML?
GGML is a tensor library specifically crafted for machine learning applications. Its primary focus is to enable the execution of large models with high performance on readily available, commodity hardware. This is achieved through a low-level, cross-platform implementation and features such as integer quantization support. The library is designed to be self-contained, with no third-party dependencies and zero memory allocations during runtime, contributing to its efficiency and speed.
GGML is utilized by projects such as llama.cpp and whisper.cpp. The development of GGML emphasizes simplicity, maintaining a minimal and easy-to-understand codebase. It operates under an open-core model, with the library and associated projects freely available under the MIT license.
Features
- Low-level cross-platform implementation: Enables broad hardware compatibility.
- Integer quantization support: Optimizes model size and performance.
- Broad hardware support: Runs efficiently on commodity hardware.
- No third-party dependencies: Simplifies integration and reduces potential conflicts.
- Zero memory allocations during runtime: Enhances performance and stability.
Use Cases
- On-device inference
- Machine learning applications on edge devices
- Deployment of large language models on commodity hardware
- Development of AI applications with limited resources
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