Optimate is an open-source collection of libraries from Nebuly for optimizing AI model performance, published on GitHub with around 8,300 stars. Rather than a single tool, it gathers the company’s open-source work on making models run faster and cheaper into one repository.
Serving and training large models is expensive, and small inefficiency multiplies across millions of requests. The libraries collected in Optimate tackle that problem from several angles: analyzing where inference time and memory go, applying optimization techniques such as compilation and quantization-friendly transformations, and benchmarking models so improvements can be measured rather than guessed. The code grew out of Nebuly’s work on an open platform for LLM user analytics and model optimization, and the repository became a reference point for practitioners interested in squeezing more performance out of existing hardware instead of simply buying bigger GPUs. The main development period ran through 2024, and the codebase remains available as a mature, readable collection of optimization utilities and experiments that engineers can adopt piecemeal.
Optimate is free and open source under the Apache-2.0 license. The libraries in the GitHub repository can be used at no cost; Nebuly’s separate commercial analytics platform is a different product and is not required.
Optimate is for ML engineers and platform teams who serve models in production and want practical, open code for profiling and optimizing inference performance without rewriting their whole stack.