LLM Engine is Scale AI’s open-source engine for fine-tuning and serving large language models. The public repository on GitHub has about 840 stars under the Apache-2.0 license and was updated in October 2026; it packages a Python library, a CLI, and Helm charts for running foundation models in production.
Deploying and fine-tuning foundation models normally demands significant infrastructure and ML expertise, and keeping up with new models and techniques is an ongoing burden. LLM Engine addresses that by providing ready-to-use APIs for popular open models such as LLaMA and MPT, plus the machinery to serve and fine-tune them. Models can be reached through Scale’s hosted version, or you can use the repository’s Helm charts to run inference and fine-tuning inside your own Kubernetes infrastructure.
The project is organized as three cooperating pieces. The Python library gives programmatic access to model serving and fine-tuning, the command-line interface covers the same operations for scripting and interactive use, and the Helm charts describe how to run the whole stack on Kubernetes. That combination means a team can prototype against Scale’s hosted endpoints, then reproduce the same setup inside its own cloud account when data-residency, cost, or compliance requirements demand self-hosting, without rewriting application code around a different API.
The LLM Engine code is free and open source. Scale also offers a hosted version of the platform with commercial terms, so teams can start self-hosted and move to managed infrastructure when it makes sense โ a freemium model.
LLM Engine is aimed at ML platform teams and developers who need to serve or fine-tune open models, want Kubernetes-native deployment, and prefer an open codebase over a closed, API-only service.