Offline-first AI assistant for local inference
Jan is an open-source offline-first AI assistant with 25K+ GitHub stars running LLMs locally without sending data externally. Features a ChatGPT-like interface with one-click model downloads from Hugging Face, conversation management, customizable prompts, and an OpenAI-compatible local API server. Supports GGUF models via llama.cpp with GPU acceleration on NVIDIA and Apple Silicon. Built with Electron for macOS, Windows, and Linux with full data privacy.
ChatGPT-like experience on local hardware. 25K+ stars, privacy and offline-first.
One-click model downloads, conversation management, model switching. GGUF via llama.cpp.
OpenAI-compatible local API. GPU acceleration on NVIDIA CUDA and Apple Metal.
Cross-platform Electron desktop app. All data stays local.
100% free and open-source under the AGPL-3.0 license ($0 software cost). Runs open-weight GGUF models locally on consumer hardware at $0 compute cost, with optional user-provided API keys for remote cloud model providers (OpenAI, Anthropic, Groq).
full pricing breakdown →macOS, Windows, Linux
Find which AI models actually run on your hardware in one command
llmfit is a Rust-based terminal tool that matches over 200 LLM models from 30+ providers against your exact hardware specs. The interactive TUI scores each model on fit, speed, VRAM usage, and context length, helping you avoid downloading models that won't run on your machine. It supports Ollama, llama.cpp, MLX, Docker Model Runner, and LM Studio backends.
Run open-source LLMs on your phone, fully offline and private
Google AI Edge Gallery is an open-source mobile app that lets you download and run large language models like Gemma directly on Android and iOS devices with zero cloud dependency. Built on MediaPipe and LiteRT, it features AI chat with reasoning mode, multimodal image analysis, real-time audio transcription, and autonomous agent skills—all running entirely on-device for complete privacy. A reference implementation for developers building offline-first AI experiences.
Container-native local AI model serving with Podman
RamaLama is an open-source tool that containerizes AI model inference using Podman or Docker, eliminating host system configuration complexity. It auto-detects GPUs (NVIDIA, AMD, Intel, Apple Silicon), pulls models from HuggingFace, Ollama, and OCI registries, and runs them in isolated rootless containers with read-only mounts and network isolation. Developed under the Containers project (Red Hat ecosystem), it brings familiar container workflows to local LLM serving.
Running LLMs on your own hardware used to mean fighting Python environments and CUDA toolkits. In 2026, three desktop-class tools dominate that workflow: Ollama, LM Studio, and Jan. All three let you download a model and chat with it offline within minutes, but the philosophies differ. Ollama is a CLI-first engine with a thriving ecosystem and an OpenAI-compatible server. LM Studio is a polished GUI with the best model discovery experience. Jan is open-source and privacy-first with native MCP support. This comparison covers interface, ecosystem, performance, and license — and gives clear signals for which fits which developer.
Verification dates are editorial checks. Routine CMS saves and automatic updatedAt timestamps do not advance them.
Jan is an open-source offline-first AI assistant with 25K+ GitHub stars running LLMs locally without sending data externally. Features a ChatGPT-like interface with one-click model downloads from Hugging Face, conversation management, customizable prompts, and an OpenAI-compatible local API server. Supports GGUF models via llama.cpp with GPU acceleration on NVIDIA and Apple Silicon. Built with Electron for macOS, Windows, and Linux with full data privacy.
Yes — Jan is open source and free to use. 100% free and open-source under the AGPL-3.0 license ($0 software cost). Runs open-weight GGUF models locally on consumer hardware at $0 compute cost, with optional user-provided API keys for remote cloud model providers (OpenAI, Anthropic, Groq).
Yes — Jan is open source.
Yes — Jan is active. Its listing was last verified on September 6, 2026.
The first editor-selected Jan alternatives are llmfit, Google AI Edge Gallery, RamaLama.
computed discovery: shared active categories
Anthropic's frontier AI assistant
Anthropic's AI assistant known for strong reasoning, nuanced writing, and extended context up to 200K tokens. Available in Opus (most capable), Sonnet (balanced), and Haiku (fast) tiers. Features web search, deep research, file analysis, code execution, artifacts, and Projects for organized workflows. Claude Code provides terminal-based agentic coding. API supports tool use, batch processing, and prompt caching. Available via claude.ai, mobile apps, and developer API.
Wafer-scale inference at thousands of tokens per second
Cerebras Inference serves open-weight LLMs like Llama, Qwen, and GPT-OSS on wafer-scale CS-3 chips through an OpenAI-compatible API, benchmarking between 1,800 and 2,600 output tokens per second on Llama 3.1 8B and several hundred on 70B models. A free tier offers one million tokens per day with no credit card, while paid pay-per-token pricing starts at $0.04 per million tokens for the smaller Llama models.
OpenAI’s flagship conversational AI for chat, research, coding help, and team workspaces
ChatGPT is OpenAI’s consumer and business assistant for everyday Q&A, writing, coding help, image tools, deep research, and workspace collaboration across web and apps. Plans span Free, Go, Plus, Pro, Business, and Enterprise on chatgpt.com.
Ultra-fast LPU inference for open-weight models
Groq is an AI inference provider built around custom Language Processing Unit (LPU) hardware for low-latency open-weight model serving. GroqCloud exposes an OpenAI-compatible API for Llama, GPT-OSS, Qwen, Kimi, DeepSeek, Gemma, Whisper, and related models, with high token-throughput positioning, model-specific rate limits, and usage-based pricing.
High-throughput LLM serving engine
vLLM is an Apache-2.0 LLM inference and serving engine focused on high-throughput self-hosted model APIs. It combines PagedAttention, continuous batching, prefix caching, quantization options, OpenAI-compatible serving, structured outputs, metrics, Docker/Kubernetes deployment guidance and integrations with agent and LLM frameworks.
Direct API access to Claude models with tool use
Official API for Claude models including Opus, Sonnet, and Haiku. Supports tool use, computer use, extended thinking, and batch processing. Features prompt caching, streaming, and Messages API with vision capabilities. Known for strong performance on complex reasoning tasks, nuanced instruction following, and safety-conscious design that makes it trusted for enterprise and production applications.
