Local LLMs
Local LLMs are language models that run directly on your own hardware — a laptop, a PC with a GPU or a home server — with no cloud, no subscriptions and no data leaving your machine. Ollama, llama.cpp and LM Studio, plus quantized GGUF builds of Llama, Qwen and Gemma, make it possible even on a decade-old graphics card. Here is all our coverage of running AI locally: guides, hardware tests, quantization and privacy.
Stable Diffusion XL at Home: A Guide for Those Who Thought It Was Too Complicated
Publisher BHV has released a practical guide to Stable Diffusion XL — for those who want to generate images without subscriptions, locally and privately.
Google Introduces TurboQuant: 6x KV-cache Compression for LLMs Without Accuracy Loss
Google Research unveiled TurboQuant — an algorithm that compresses the KV-cache of large language models by at least six times and accelerates attention computation…
Doka launches local AI-agent for Russia without VPN, subscription or cloud
A developer bundled a custom AI-agent into the Doka desktop app: the service runs locally, uses the Qwen3 model, and requires no cloud, VPN, or monthly fees.
Habr AI Author Proposes Deterministic AI on MacBook Air Over GPU Race
Lansoft names AI technologies that will reach production in 2026
Sber Develops GigaChain Without Forking LangChain, Betting on a Compatibility Package
Local Vision for z.ai GLM-5.1: 8B Model Closes 70% of the Gap to the Frontier
DeepSeek, Google, and Meta: 10 Techniques for LLM KV-Cache Compression to Reduce Inference Memory
KV-cache has become a memory hog for large LLMs, and a new survey reveals 10 approaches — from H2O and SnapKV to TurboQuant and DeepSeek's MLA — that reduce memory…
Proton CEO: privacy in the age of AI is possible, but autonomous agents pose a new risk
Proton CEO Andy Yen believes that privacy in the age of AI can be preserved through local models and encryption, but he considers autonomous agents with access to…
Poolside released Laguna XS.2 and M.1 — open models for agentic programming
Google Gemma 4 and Qwen 3.6 top the list of best local models for home use in 2026
Google Unveiled TurboQuant: 3-Bit KV-Cache for LLM, but Memory Market Panicked Prematurely
Canonical Makes AI in Ubuntu Optional: Focusing on Local Models and User Control
M2 delegated 40% of marketing texts to AI and maintained content quality
The M2 team embedded an AI copywriter into its internal editorial department, assigned 40% of product and marketing texts to the model, and maintained consistent…
Habr AI Shows How to Add Memory and Context to an LLM Chat in Python with Ollama and LiteLLM
In a new part of the tutorial on Python chat with Ollama and LiteLLM, the author demonstrates how to store message history, pass context to the model, and limit…