Local AI — on-device AI, edge AI, local LLM — has moved out of research labs and into daily work. In 2026, a MacBook, Framework Desktop or Nvidia DGX Spark runs tens-of-billions-of-parameter language models without a network connection. GDPR, the EU AI Act and post-Schrems II case law make it the only realistic option for many organisations.
What does local AI actually mean?
The model and its weights load onto your device. Prompts, documents and chat history stay there — inference runs on your CPU, GPU or NPU. Gemma 4 E4B fits in just 3 GB of VRAM, Qwen 3.5 27B runs on a single 16 GB GPU, and Viking 33B or DeepSeek V3.2 needs Mac Studio- or DGX Spark-class hardware. Nothing leaves the network unless you explicitly wire it up.
Why 2026 in particular?
Three things shifted. First, open weights caught up to closed models for everyday work — Qwen 3.5 27B scores 72.4% on SWE-Bench Verified, DeepSeek V3.2 won gold at IMO/IOI 2025. Second, consumer hardware memory has jumped: a M4 Max Mac Studio ships with 128 GB of unified memory, the DGX Spark with 128 GB and 1 PFLOP of FP4 compute. Third, the EU AI Act's GPAI obligations have been live since 2 Aug 2025 and full enforcement begins 2 Aug 2026 — a transparent, open model is dramatically easier to document.
Who is local AI for?
Leaders whose calendars, emails and strategy are confidential. Entrepreneurs who don't want contracts, salaries or client conversations leaking to a cloud. Researchers with unpublished data and a need for offline tools. Regulated sectors (health, defence, finance, public sector) where GDPR, NIS2 and DORA often rule out foreign cloud altogether.
How Sinun AI builds it for you
We map your work, your existing hardware and your threat model. We pick an open European model (Viking, Poro, Mistral, Gemma, Qwen), install it locally, define memory and guardrails, and sync your devices over a CRDT-based E2EE stack. The result: a personal AI that stays yours — no franchise chain, no offshore team, no vendor lock-in.
Frequently asked
- Do I need the network to use it?
- No. You need the network once to download the model. After that the assistant runs fully offline — on a train, at the cottage, on a plane.
- Is local AI slower than ChatGPT?
- Response time depends on hardware. Mac Studio or DGX Spark typically hits 40–100 tokens/s, matching or beating the ChatGPT experience. A mid-size open model is enough for most work.
- Can I use the same AI across devices?
- Yes. Local-first CRDT sync keeps your phone, laptop and workstation memory aligned end-to-end encrypted, with no central cloud.
Updated 2026-04-21