# lmstudio-ai/lms

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4,214 stars · 333 forks · TypeScript · mit

## Links

- GitHub: https://github.com/lmstudio-ai/lms
- Homepage: https://lms.dev
- awesome-repositories: https://awesome-repositories.com/repository/lmstudio-ai-lms.md

## Topics

`llm` `lmstudio` `nodejs` `typescript`

## Description

This project is a headless large language model inference engine and server manager designed for local deployments. It provides a developer toolkit and API gateway that allows for the management of model lifecycles and inference tasks without a graphical user interface.

The system enables the deployment of model engines across different operating systems, cloud environments, or CI pipelines. It includes a command-line interface for bootstrapping development projects and automating the orchestration of loading and unloading model binaries based on specific workflow needs.

The toolset covers infrastructure monitoring through real-time state-streaming logs and application status checks. It further provides a standardized network interface to expose inference capabilities to external software development kits.

## Tags

### Artificial Intelligence & ML

- [Headless Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-deployment-servers/llm-inference-servers/headless-implementations.md) — Runs large language models locally without a GUI, exposing them via an HTTP API for integration.
- [Local Inference Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/serving-and-runtime/large-language-model-optimization/local-inference-engines/local-inference-engines.md) — Runs large language models entirely on the local machine without any cloud dependency or internet connection.
- [Server Managers](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-deployment-servers/llm-inference-servers/server-managers.md) — Starts, stops, and monitors local inference servers, loading and unloading models for headless deployments.
- [CLI Toolkits](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-development-toolkits/cli-toolkits.md) — Provides a command-line toolkit for scaffolding projects, managing model lifecycles, and automating inference tasks.
- [Local Inference Runtimes](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/inference-engines/local-inference-runtimes.md) — Runs large language models entirely on the local machine with no cloud connectivity or external service dependencies.
- [Model Loading](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/data-and-checkpointing/model-loading.md) — Loads a specified model into GPU or system memory so it is ready to handle inference requests. ([source](https://cdn.jsdelivr.net/gh/lmstudio-ai/lms@main/README.md))
- [Model Unloading Policies](https://awesome-repositories.com/f/artificial-intelligence-ml/memory-optimization/model-unloading-policies.md) — Removes a loaded model from memory to free resources, optionally unloading all models at once. ([source](https://cdn.jsdelivr.net/gh/lmstudio-ai/lms@main/README.md))
- [Model API Gateways](https://awesome-repositories.com/f/artificial-intelligence-ml/model-api-gateways.md) — Exposes a standardized HTTP interface for local LLM inference, enabling integration with external SDKs.
- [Multi-Language SDK Wrappers](https://awesome-repositories.com/f/artificial-intelligence-ml/python-sdk-embeddings/multi-language-sdk-wrappers.md) — Provides JavaScript and Python SDKs that wrap the HTTP API for building custom applications against the local inference engine.
- [LLM Development Toolkits](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-development-toolkits.md) — Scaffolds new projects and provides SDKs to build custom applications that interact with a local inference engine.

### Part of an Awesome List

- [Model Lifecycle Management](https://awesome-repositories.com/f/awesome-lists/ai/model-repositories/model-lifecycle-management.md) — Provides CLI commands to load, unload, and list models for controlling memory and inference availability.
- [CLI-Driven Lifecycles](https://awesome-repositories.com/f/awesome-lists/ai/model-repositories/model-lifecycle-management/cli-driven-lifecycles.md) — Controls model loading and unloading entirely through command-line commands with GPU resource management.
- [CLI Model Management](https://awesome-repositories.com/f/awesome-lists/devops/model-deployment-and-management/cli-model-management.md) — Starts and stops local inference servers, loads and unloads models, and lists available models from the command line. ([source](https://lms.dev/docs))
- [Local Inference SDKs](https://awesome-repositories.com/f/awesome-lists/devtools/sdk-integration/local-inference-sdks.md) — Provides JavaScript and Python SDKs for building custom applications against a local inference engine. ([source](https://lms.dev/docs))
- [Model Serving & Deployment](https://awesome-repositories.com/f/awesome-lists/ai/model-serving-deployment.md) — Deploys LLMs locally on consumer machines.

### Content Management & Publishing

- [Inference API Gateways](https://awesome-repositories.com/f/content-management-publishing/headless-api-gateways/inference-api-gateways.md) — Exposes all inference and management functionality through a RESTful HTTP interface without a GUI.

### DevOps & Infrastructure

- [Headless Deployments](https://awesome-repositories.com/f/devops-infrastructure/cross-platform-deployments/headless-deployments.md) — Ships precompiled binaries for headless installation on Linux, macOS, and Windows servers. ([source](https://lms.dev/docs))
- [Headless Server Deployments](https://awesome-repositories.com/f/devops-infrastructure/headless-server-deployments.md) — Installs and runs the core inference engine on Linux or Windows servers without a graphical interface. ([source](https://lms.dev/docs))
- [Inference Server Architectures](https://awesome-repositories.com/f/devops-infrastructure/headless-server-management/inference-server-architectures.md) — Operates without a graphical user interface, exposing all functionality through CLI and HTTP API.

### Networking & Communication

- [Inference API Gateways](https://awesome-repositories.com/f/networking-communication/http-gateways/inference-api-gateways.md) — Provides a uniform network interface for external SDKs to interact with the inference engine over HTTP.

### Software Engineering & Architecture

- [Inference Engines](https://awesome-repositories.com/f/software-engineering-architecture/headless-runtimes/inference-engines.md) — Runs large language models locally without a GUI, controlled entirely through a CLI and HTTP API.

### Business & Productivity Software

- [Cross-Platform Binary Distribution](https://awesome-repositories.com/f/business-productivity-software/cross-platform-binary-distribution.md) — Distributes precompiled inference engine binaries for Linux, macOS, and Windows without system dependencies.

### System Administration & Monitoring

- [Real-time Status Streaming](https://awesome-repositories.com/f/system-administration-monitoring/real-time-status-streaming.md) — Streams live log output and server status updates to clients for monitoring and debugging purposes.
- [Server Health Checks](https://awesome-repositories.com/f/system-administration-monitoring/server-health-checks.md) — Provides a command to check whether the local API server is running and report its state. ([source](https://cdn.jsdelivr.net/gh/lmstudio-ai/lms@main/README.md))