Gaming PC vs. Local-AI Workstation
Which gaming-PC parts carry over to local AI, where the priorities diverge, and when one machine can do both jobs well.
Local AI PC Guides
Local AI hardware is mostly a memory-sizing problem. The model weights must fit somewhere, the context cache grows while you work, and every concurrent agent adds another live workload. A gaming GPU name alone does not answer any of those questions.
These guides translate model size, quantization, context, and concurrency into VRAM, system RAM, storage, power, and cooling. They are a validation section: small enough to keep the advice rigorous while we learn whether readers want a full local-AI build catalog.
How Much GPU VRAM Does a Local LLM Need?
A practical VRAM-sizing guide for local language models, including weights, quantization, context cache, and multi-GPU caveats.
How to Build a PC for Local Coding Agents
Hardware priorities for local coding agents: VRAM headroom, long-context memory, system RAM, storage, concurrency, and CPU offload.
Local LLM PC Builds by Model Size
Choose local-AI hardware for small, 30B-class, and 70B-class language models without confusing gaming speed with model capacity.