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Movements, leaders, victories and the continuing fight for equality.
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Wilmington, North Carolina once had a thriving Black middle class and an elected interracial government. In 1898 white supremacists used violence to overthrow that government, kill Black residents and drive many others from the city.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
| LM Studio | |
|---|---|
 | |
 A user asking about Wikipedia using Qwen 3.5 9B | |
| Developer | Element Labs |
| Release | May 2023 |
| Operating system | Windows, macOS, Linux |
| Type | LLM inference software |
| License | Proprietary |
| Website | lmstudio |
| Repository | github |
LM Studio is large language model inference software for running and using large language models locally on personal computers. It provides tools for downloading and managing models, interacting with them through a chatbot interface, and serving locally running models to other applications through an application programming interface (API).[1][2]
LM Studio uses llama.cpp as an inference engine for models in the GGUF format and also supports Apple's MLX framework on Apple silicon.[3] In addition to its graphical interface, the platform includes command-line tools, software development kits for Python and TypeScript, and a local inference server with native, OpenAI-compatible, and Anthropic-compatible APIs.[4][3]
LM Studio was launched in May 2023.[5] It was initially developed as a desktop environment combining local model inference with model discovery, a chat interface, local model management, and an API server.[6]
Support for the MLX inference framework on Apple silicon was introduced in October 2024, alongside its existing llama.cpp based inference system.[7] Subsequent releases expanded the software's developer-oriented capabilities, including headless operation, on-demand model loading, command-line model downloading, tool calling, and speculative decoding.[8][9]
In April 2026, Element Labs, the developer of LM Studio, acquired Locally AI,[10] whose developer joined the company to work on native AI applications across Apple devices.[10]
In June 2026, LM Studio introduced Locally, a mobile application for iPhone and iPad that allowed users to access models running on their computers through LM Link.[11]
LM Studio provides an integrated interface for finding and downloading AI models, loading models into memory, configuring inference settings, and interacting with models through a chat interface. Models can be obtained through integrations with repositories such as Hugging Face.[1] The application can also use retrieval-augmented generation to provide local documents as context to language models.[1]
The software can operate as a local inference server, allowing applications to access loaded models through LM Studio's REST API or compatibility interfaces modeled on the OpenAI and Anthropic APIs.[4] Its Python and TypeScript software development kits provide programmatic model loading, text generation, embeddings, and agentic tool use.[3]
LM Studio can also operate without its graphical interface. Its headless component, llmster, packages the core LM Studio runtime for use as a background service on local computers, servers, and GPU systems.[12]
LM Studio has expanded beyond its original desktop application to include additional software and services. LM Link allows models running on another computer to be accessed remotely,[12] while the company's mobile software grew from its acquisition of Locally AI in 2026.[10]
The company also develops Bionic, an AI agent for document creation, computer programming, workflow automation, and computer interaction that can use locally running models through the LM Studio runtime.[13]
PCWorld included LM Studio among applications for running language models locally, highlighting its model selection, configuration controls, developer interface, and support for retrieval-augmented generation, while describing it as oriented more toward advanced users than beginners.[1]
In a comparison with Ollama, Windows Central highlighted LM Studio's graphical interface and its support for GPU acceleration on systems using integrated AMD and Intel graphics, describing it as a unified environment for discovering, installing, and interacting with local language models.[2]
LM Studio has also been studied in research on local AI software. A 2026 digital-forensics study compared LM Studio with Ollama and llama.cpp, examining artifacts left by the applications including model caches, configuration data, prompt histories, logs, and network activity.[14]
Large language models (LLMs) | |
|---|---|
| Concepts |
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| Training, prompting, and alignment | |
| Models | |
| Chatbots and assistants | |
| Agents, coding, and applications | |
| Software | |
| Hardware and infrastructure | |
| Benchmarks, evaluation, and detection | |
| Datasets and data | |
| Organizations | |
| People | |
| Social, economic, and governance | |
Source: Wikipedia. Article content is retrieved live through the MediaWiki API.
LM Studio is large language model inference software for running and using large language models locally on personal computers. It provides tools for downloading and managing models, interacting with them through a chatbot interface, and serving locally running models to other applications through an application programming interface (API). LM Studio uses llama.cpp as an inference engine for models in the GGUF format and also supports Apple's MLX framework on Apple silicon. In addition to its graphical interface, the platform includes command-line tools, software development kits for Python and TypeScript, and a local inference server with native, OpenAI-compatible, and Anthropic-compatible APIs.
llama.cpp is an open-source software library that performs inference on various large language models such as Meta's Llama model. It is co-developed alongside the GGML project, a general-purpose tensor library. Command-line tools are included with the library, alongside a server with a simple web interface. llama.cpp has been considered as the de facto standard as the core of almost all local inference tools, including Ollama and LM Studio.
The GGUF (GGML Universal File) file format is a binary file format that stores both tensors and metadata in a single file and is designed for fast saving and loading of model data. It was introduced in August 2023 by the llama.cpp project to better maintain backward compatibility as support was added for other model architectures. It superseded previous formats used by the project such as GGML and is typically produced by converting models developed with a different machine-learning library such as PyTorch. GGUF has become the standard format for distributing quantized large language models for local inference and is natively supported by tools such as llama.cpp, Ollama, LM Studio, GPT4All, Jan, and koboldcpp. As of 2026, tens of thousands of GGUF checkpoints are hosted on Hugging Face, which provides first-class integration, including a metadata viewer, an inference endpoint service, and a JavaScript parser library.
Cline is an open-source AI coding agent that operates through IDE extensions and a command-line interface. It can inspect and modify source code, execute terminal commands, run tests, and perform other software development tasks from natural-language instructions. Cline supports models from multiple providers, including Anthropic, OpenAI, and Google, as well as locally hosted models through Ollama, LM Studio, and Atomic Chat.
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →The Greenwood District.
