Run AI on your own hardware.

• 6 July 2026

  Mistral releases the Mistral 3 family under Apache 2.0

  Mistral has released the Mistral 3 family, spanning small dense models and a large mixture-of-experts, all under the Apache 2.0 licence. The licensing is as notable as the models: it removes the commercial conditions of Mistral's earlier releases.

• 2 July 2026

  Mistral open-sources Leanstral, a model built for formal verification

  Mistral has released Leanstral 1.5, an Apache 2.0 model specialised for Lean 4, the proof assistant used in formal software verification. It is a reminder that local models are not only general chatbots, but increasingly tools for specific domains.

• 29 June 2026

  EU finalises Digital Omnibus, delaying the AI Act's high-risk rules to 2027

  The Council of the EU has given final approval to the Digital Omnibus, a package that simplifies and defers parts of the AI Act. The main high-risk obligations move from August 2026 to December 2027, while the prohibitions and model rules stay live.

• 18 May 2026

  Apple trims high-memory Mac Studio options amid a DRAM squeeze

  A tightening memory market has led Apple to narrow the memory configurations on its top Mac Studio, removing some of the very high-capacity options that made it a favourite for running large models. If you were counting on a specific config, check current availability.

• Gemma 3 27B

  Google · 27B

  Google's 27-billion-parameter open model, and the largest of the Gemma 3 family. It is multimodal, handling images as well as text, supports over 140 languages, and is designed to run on a single 24GB card at 4-bit.

  Permitted with conditions

• Llama 3.3 70B Instruct

  Meta · 70B

  Meta's 70-billion-parameter instruction-tuned model, delivering performance close to their much larger 405B model at a fraction of the hardware cost. A strong general-purpose choice if you have the VRAM for it.

  Permitted with conditions

• Qwen2.5-Coder 7B Instruct

  Alibaba · 7B

  Alibaba's code-specialised 7B model, strong at code completion and generation well beyond what its size would suggest. A practical choice for a local coding assistant on a mid-range GPU, and permissively licensed.

  Commercial use permitted

• Qwen3 30B-A3B

  Alibaba · MoE 30B-A3B

  A mixture-of-experts model with 30 billion total parameters but only 3 billion active per token, so it generates far faster than a dense 30B while keeping much of the quality. Apache 2.0, with an optional thinking mode.

  Commercial use permitted

• Qwen3 32B

  Alibaba · 32B

  The largest dense Qwen3 model, and a strong single-card flagship: at Q4 it fits a 24GB card with short context. Apache 2.0 licensed, with an optional thinking mode that makes it a credible reasoning model.

  Commercial use permitted

• Qwen3 8B

  Alibaba · 8B

  Alibaba's 8-billion-parameter model from the Qwen3 family, with a hybrid thinking mode you can switch on for harder problems. Apache 2.0 licensed and strong for its size, it is one of the most capable models that runs comfortably on a 12GB card.

  Commercial use permitted

• Qwen3-Coder 30B-A3B

  Alibaba · MoE 30B-A3B

  A code-specialised mixture-of-experts model with 30 billion total parameters and 3 billion active, built for agentic coding: exploring codebases, editing across files, and driving coding agents. Apache 2.0, with a very long 256k context window.

  Commercial use permitted

Learning paths

• 🚀 Getting Started What local AI is, why it matters, and how to run your first model.
• 🖥️ Hardware GPUs, unified memory, VRAM, and what you actually need for what you want to do.
• ⚙️ Running Models Quantisation, inference engines, model formats, and getting good performance.
• 💻 Local AI Coding Code assistants, autocomplete, and agentic coding tools that run on your machine.
• 📚 RAG & Knowledge Systems Building retrieval systems over your own documents, entirely locally.
• 🤖 Agentic AI & Harnesses Tool use, agent frameworks, and orchestration with local models.
• 🎯 Fine-Tuning Adapting models to your domain: LoRA, QLoRA, full fine-tunes, and when each is worth it.
• 🎨 Beyond LLMs Image generation, speech recognition, text-to-speech, and other local models.
• 🏢 Server & Enterprise Running local AI at organisational scale: serving, scaling, and multi-user access.
• 🛡️ AI Regulation & Sovereignty AI-specific regulation like the EU AI Act, alongside data residency, GDPR, and the practical case for keeping data in-house.