Local AI Out of Memory: How to Fix It (November 2026)
A local model that will not load is usually memory arithmetic. Decode the error strings, then cut context, KV cache, parallel requests and layers in order.
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A local model that will not load is usually memory arithmetic. Decode the error strings, then cut context, KV cache, parallel requests and layers in order.
Where local AI models live on disk, what each model costs at every quantization, why the same weights get stored three times over, and how to reclaim the space.
Run Ollama in a container on a Synology or QNAP NAS: model storage, memory limits, context sizing, and reaching it from your laptop and phone.
How to install Ollama on Linux and Windows, run a first model, confirm the GPU is being used, and fix the setup errors that stop a first run.
Home Assistant can run a voice assistant with nothing leaving the house: Whisper or Speech-to-Phrase for speech, Piper for replies, a local LLM for the brain.
Run a private Discord or Telegram chatbot on a local model: wiring, token handling, the intents it needs, rate limits, and a systemd unit.
How llama.cpp, vLLM and LM Studio decide whether to reuse your prompt's KV cache, what each reports about tokens saved, and where the reuse stops.
Containerize Ollama without losing GPU acceleration: the NVIDIA toolkit setup, the AMD device flags, the volume that keeps your models, and how to check.
Which open-weight models hold up behind Claude Code, how Ollama's Anthropic-compatible endpoint connects them, and what each Ollama tag costs to run.
Temperature, top-k, top-p and seed decide how random your local model is. What they do in Ollama, llama.cpp and vLLM, and how to get repeatable answers.
Muse Spark Open, Reflection Beam, and Granite Switch 4.1 all landed on Hugging Face within two days. What fits on one GPU, a Mac, or a cluster.
Which open-weight models stay in character across long roleplays, what frontend to drive them with, and what the privacy picture looks like for intimate chats.
Which open-weight models actually solve math on your hardware, from quick arithmetic to AIME-level problems, ranked by VRAM tier and use case.
Open-weight chat LLMs by VRAM tier: Qwen3.5, Qwen3.6, Gemma 4 and gpt-oss picks, with Ollama tag sizes and licences.
Side-by-side look at what Ollama, LM Studio, llama.cpp, vLLM, AnythingLLM, and Hugging Face TGI collect by default, and how to switch each one off.
Self-hosted models that turn plain-English questions into SQL for PostgreSQL, MySQL, SQLite, and DuckDB, with BIRD-SQL results, licences, and RAM needs.
Plug a local Ollama model into Obsidian, Joplin, Logseq, or Notion so notes never leave your machine. Plugins that work, ones that do not, and what each costs.
JSON mode, GBNF grammar-constrained decoding, and OpenAI-style tool calling across Ollama, llama-server, LM Studio, and vLLM.
Run a proofreading LLM on your laptop so drafts, manuscripts, and sensitive documents never leave your machine. Models, front ends, and what each tier gets you.
Which Ollama models work as a local backend for Claude Code's CLI, what features degrade without Anthropic's API, and how to wire it up.
A plain-language walk through parallelism, matrix multiplication, VRAM, memory bandwidth, and why a CPU can run an LLM but rarely fast enough.
Practical tier guide to running a local LLM for cover letters, resumes, and CV drafts. Picks by RAM, instruction following, and what to keep offline.
Which open-weight model actually handles non-English work, what each provider claims about language coverage, and the tradeoffs between breadth and depth.
Which AI chats accept image uploads without training on your photos, what they retain, and the local fallback.