Griptape
Enterprise-grade Python framework for building secure, modular AI agents and multi-step workflows.
Discover, download, and run any local LLM on your machine with total privacy and hardware acceleration.
LM Studio is a premier desktop application built for professional AI developers and privacy-conscious enterprises to run Large Language Models (LLMs) locally on macOS, Windows, and Linux. Architected on the llama.cpp framework with an Electron-based GUI, it provides a sophisticated abstraction layer for hardware-accelerated inference using Apple Metal (M1/M2/M3), NVIDIA CUDA, and AMD ROCm. By 2026, LM Studio has positioned itself as the industry standard for local LLM orchestration, bridging the gap between raw model weights on Hugging Face and production-ready local endpoints. It supports a wide array of model architectures including Llama 3, Mistral, and Phi-3, specifically focusing on the GGUF format for efficient 4-bit and 8-bit quantization. The platform's technical core is its Local Inference Server, which provides an OpenAI-compatible API, allowing developers to swap cloud-based models for local ones with a single line of code. Its 2026 market position is defined by 'LM Studio for Business,' offering centralized management for teams, while remaining the go-to tool for individual researchers seeking to bypass the latency, costs, and data sovereignty risks associated with cloud AI providers.
Allows users to specify the exact number of layers to offload to the GPU, optimizing for hybrid CPU/GPU memory architectures.
Enterprise-grade Python framework for building secure, modular AI agents and multi-step workflows.
State-of-the-art neural audio coding for high-fidelity speech tokenization and reconstruction.
The enterprise-grade autonomous refactoring engine for legacy modernization and multi-agent SDLC orchestration.
Transform raw codebases into high-fidelity synthetic instruction datasets for LLM fine-tuning.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Exposes a local REST API that mirrors OpenAI’s /v1/chat/completions schema.
Direct integration with the Hugging Face Hub API to filter models by compatibility, architecture, and popularity.
Forces the model to adhere to a specific JSON schema or regex pattern during generation.
Supports Metal (Mac), CUDA (NVIDIA), and ROCm (AMD) natively without complex environment setup.
Ability to load and switch between multiple models in memory simultaneously if VRAM allows.
Native support for multimodal LLMs (like LLaVA) allowing for local image analysis.
Law firms or healthcare providers cannot upload sensitive PII to cloud providers due to compliance.
Registry Updated:2/7/2026
Export results.
Developers want AI-assisted coding without their proprietary source code being used for training.
High API costs when generating millions of rows of synthetic training data.