Entry-Level PC Build for Local LLMs: A Roughly 8000 Yuan RTX 3060 12GB Machine
Entry-level PC build for local LLMs: an RTX 3060 12GB and i5-12600KF machine at about 7899 yuan, with prices, models it runs, upgrades and where not to skimp.
Entry-level PC build for local LLMs: an RTX 3060 12GB and i5-12600KF machine at about 7899 yuan, with prices, models it runs, upgrades and where not to skimp.
LLM context length VRAM guide: the KV cache formula, worked examples for 7B to 70B models, f16 vs q8_0 vs q4_0 cache, Ollama defaults and out-of-memory fixes.
Enterprise private LLM deployment: pilot, department and company tiers, reference hardware budgets, layered design, OWASP-based security checklist and rollout.
Gemma local deployment with Ollama: compare Gemma 4 e2b, e4b, 12b, 26b and 31b sizes and context, pick one by VRAM, and set thinking mode, sampling, and fixes.
What is GGUF? Learn the GGUF format, how to install and run llama.cpp, how Q4_K_M and other quantizations change file size, and how it relates to Ollama.
gpt-oss local deployment with Ollama and llama.cpp: official memory needs for 20b and 120b, MXFP4, MoE offload flags, and reasoning effort.
Choosing a laptop for local LLMs? Compare RTX 50 laptop VRAM, bandwidth and power limits, cooling, and Mac unified memory, and see what 8GB to 24GB can run.
Local LLM for coding: set up Ollama with Continue, pick Qwen Coder sizes by VRAM, split autocomplete, chat and agent roles, and avoid context and speed traps.
Local LLM privacy explained: what stays on your machine, where data can still leak, which Ollama and Open WebUI settings to check, and a practical checklist.
How to run Qwen locally: Ollama file sizes for Qwen3, 3.5, 3.6, 3.8, Coder and VL, a VRAM matching table, one-line commands and thinking-mode control.