Local AI

AnythingLLM is an open-source, self-hosted AI platform that bundles RAG document chat, multi-agent task automation, and multi-user workspace management into a single deployable package — with zero data leaving your infrastructure. As of early 2026, it has accumulated over 57,000 GitHub stars and remains MIT licensed. What Is AnythingLLM? Core Architecture and 2026 Positioning AnythingLLM is a full-stack AI application layer, not an inference engine. It sits between your documents and your LLM provider, handling embedding, vector storage, retrieval, and conversation context so you don’t have to wire these together yourself. The project is maintained by Mintplex Labs and has crossed 57,000 GitHub stars as of early 2026 — making it one of the most-starred self-hosted RAG projects in existence. The architecture is built around the concept of workspaces: isolated knowledge bases, each with its own document pool, embedding index, and conversation history. One workspace handles your engineering runbooks; another handles customer contracts; a third handles sales collateral — none of them bleed into each other. Under the hood, AnythingLLM delegates model inference entirely to external providers. It ships with LanceDB as its default on-instance vector store, which means embeddings persist locally without requiring a separate Postgres or Pinecone subscription. This design decision — orchestration without inference — is the reason AnythingLLM can support 30+ LLM backends without rewriting its core logic: Ollama, LM Studio, OpenAI, Anthropic, Azure, AWS Bedrock, Groq, Together, Mistral, and DeepSeek all plug in via a provider abstraction layer. ...

Qwen3’s original open-weight release arrived on April 29, 2025. It contains six dense models and two mixture-of-experts (MoE) models. This guide covers that release; later Qwen3 updates and Qwen3.x families must be checked against their own model cards. Source: Qwen’s release announcement. Correction — October 7, 2026: The previous version gave the wrong release year, dense-model count and size range. It also mixed advertised context limits with runtime memory requirements. Those claims have been corrected; unsupported benchmark rankings and hardware-speed promises have been removed. ...

Devstral Small 2 is a 24B-parameter coding model from Mistral AI that scores 68% on SWE-bench Verified and runs on a single 24GB GPU or a Mac M-series with 32GB unified memory — making it the first cloud-grade coding agent most developers can realistically self-host. This guide covers three setup paths: Ollama for beginners, vLLM for production teams, and llama.cpp for CPU-only or low-VRAM machines. What Is Devstral Small 2? Devstral Small 2 is Mistral AI’s open-weight coding specialist, released December 10, 2025 under the Apache 2.0 license. With 24 billion parameters and a 256K-token context window, it achieves 68.0% on SWE-bench Verified — a real-world benchmark measuring a model’s ability to resolve open GitHub issues autonomously. That puts it on par with models up to five times its parameter count, including closed-source proprietary systems. Because it ships under Apache 2.0, you can run it locally with no API fees, no data leaving your machine, and no usage restrictions — even in commercial projects. The model is fine-tuned specifically on agentic coding workflows: reading multi-file codebases, writing patches, running tool calls, and self-correcting from test failures. Devstral Small 2 outperforms Qwen 3 Coder Flash (30B) despite being a smaller model, and its larger sibling Devstral 2 (123B) hits 72.2%, compared to Claude Sonnet 4.5’s 77.2% — at up to 7x lower cost per coding task. For teams or individuals who need a capable coding agent without cloud dependency, Devstral Small 2 is the most practical choice available today. ...

The best Ollama model for coding depends on the work you need it to perform, the memory available to it and the tools around it. This guide identifies model options and deployment checks. It is a documentation-based comparison, not a set of locally measured rankings. Correction — October 7, 2026: The previous version described RTX 4090, RTX 3090 and M3 Max tests, 50+ runs and per-language pass rates without supporting first-party run records in the reviewed material. Those claims and the resulting rankings have been removed. HumanEval, MMLU, LiveCodeBench and SWE-bench results are no longer mixed into one score table. ...

You do not need to pay for cloud AI APIs anymore. Ollama and LM Studio let you run powerful language models entirely on your own hardware — for free, with full privacy, and with zero per-request cost. Ollama is the developer’s tool: a CLI that deploys models in one command and serves them via an OpenAI-compatible API. LM Studio is the explorer’s tool: a polished desktop app with a built-in model browser, chat interface, and visual performance monitoring. Both use llama.cpp under the hood, so raw inference speed is nearly identical. Most power users in 2026 run both — LM Studio for experimenting with new models, Ollama for production integration. ...