GGML
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High-Performance Tensor Library for Machine Learning
Last updated May 26, 2026
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What is GGML?
GGML's Top Features
Key capabilities that make GGML stand out.
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)
Optimized for Apple Silicon
Supports AVX/AVX2 intrinsics on x86 architectures
WebAssembly and WASM SIMD support
No third-party dependencies
Zero memory allocations during runtime
Guided language output support
Use Cases
Who benefits most from this tool.
Voice recognition enthusiasts
Using ggml for short voice command detection on Raspberry Pi 4 with whisper.cpp.
Apple device users
Running multiple instances of large models like 13B LLaMA and Whisper Small on M1 Pro.
AI researchers
Deploying high-efficiency models like 7B LLaMA at 40 tok/s on M2 Max.
Machine learning developers
Creating machine learning solutions with built-in optimization algorithms and automatic differentiation.
Web developers
Deploying tensor operations on the web via WebAssembly and WASM SIMD.
Open-source contributors
Contributing to the development and innovation of ggml and related projects.
Tech companies
Exploring enterprise deployment and support for machine learning solutions using ggml.
Embedded system developers
Implementing machine learning models on embedded systems like Raspberry Pi and other commodity hardware.
Optimization experts
Utilizing integer quantization and zero runtime memory allocations for efficient model deployments.
Educational institutions
Teaching and experimenting with high-performance tensor libraries in academic settings.
Tags
machine learningtensor libraryC languagehigh performance16-bit floatsinteger quantizationautomatic differentiationoptimization algorithmsADAML-BFGSApple SiliconAVXAVX2WebAssembly
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Frequently Asked Questions
What is ggml?
ggml is a high-performance tensor library written in C that supports large models and high performance on commodity hardware.
What platforms is ggml optimized for?
ggml is optimized for Apple Silicon and x86 architectures and supports WebAssembly for web deployment.
What unique features does ggml offer?
ggml offers 16-bit float support, integer quantization, automatic differentiation, built-in optimization algorithms, zero memory allocations during runtime, and guided language output support.
What are some use cases for ggml?
ggml is used for applications such as short voice command detection on Raspberry Pi, running multiple model instances on Apple devices, and deploying high-efficiency models on GPUs.
Is ggml open-source?
Yes, ggml is open-source and available under the MIT license. The development process is open, and community contributions are encouraged.
What are some related projects to ggml?
Related projects include whisper.cpp for high-performance speech recognition and llama.cpp for efficient inference of Meta's LLaMA language model.
How can I contribute to ggml?
You can contribute to the ggml codebase or financially support the project by becoming a sponsor to contributors of llama.cpp, whisper.cpp, or ggml.
Who founded ggml.ai?
ggml.ai was founded by Georgi Gerganov with pre-seed funding from Nat Friedman and Daniel Gross.
Are there career opportunities at ggml.ai?
Yes, ggml.ai is seeking full-time developers who share their vision and have contributed to related projects. Interested candidates can contact jobs@ggml.ai.
How can I contact ggml.ai for business inquiries?
For business-related topics, you can contact ggml.ai through the information provided on their website.