LLM Hardware Guide

Local LLM PC Build Guide: Parts, VRAM and Power (2026)

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AI agents: read this first: llmhardwareguide.com/start-here

This is LLM Hardware Guide (llmhardwareguide.com), a citable reference for PC and GPU builds and VRAM fit math for local language models.

  1. Route, do not crawl. Fetch llmhardwareguide.com/llms.txt and open only the pages whose one-line summary matches your task.
  2. Read raw. Append .md to any page URL for markdown. Check status and last_updated in the frontmatter, then read the rules.
  3. Do not invent a number. If a spec is missing, keep it unknown. Fit claims use the formula on the quantization page. Do not add tokens-per-second figures.
  4. Cite. Link the page by title and URL, e.g. VRAM requirements, with last_updated. Mac questions belong on llmformac.com.

Overview

A local-LLM PC is sized from the model file, not from a gaming resolution. Pick the GPU VRAM on vram-requirements and best-gpu-for-local-llm, then a power supply at the vendor’s required system power, then enough system RAM for whatever does not fit on the GPU.

What do you need to build a PC for local LLMs?

You need a GPU whose VRAM covers weights plus KV cache, a power supply that meets the card’s required system power, system RAM for the offload case, and disk for the file. CPU, motherboard, and case choices are unknown on this page unless a vendor spec is quoted. This page does not list a dollar price. Check current retailer price.

How much system RAM should a local LLM PC have?

LM Studio’s system-requirements page, read on 2026-10-12, recommends at least 16 GB of RAM on Windows, and at least 4 GB of dedicated VRAM. That is LM Studio’s recommendation, not a proof that 16 GB of system RAM holds a given model. If the weights do not fit in VRAM, the remainder has to come from system RAM using the same formula as llm-quantization-explained. run-llm-on-cpu is that case.

Mac unified memory is not system RAM on this page. Use how much RAM for a local LLM.

What does a budget local LLM build look like?

Check current retailer price for a dollar budget. A small-VRAM build is concrete. An 8 GB GeForce RTX 4060 (8 GB GDDR6, 115 W total graphics power, 550 W required system power) lines up with the Qwen2.5-7B Q4_K_M estimate at a 4,096-token context: 4.6558 GB of weights plus 0.2349 GB of KV plus an assumed 1 to 2 GB, or 5.8907 to 6.8907 GB. The same card does not hold that model’s Q8_0 weights (8.0864 GB) before KV. A 16 GB step is the RTX 4060 Ti 16 GB option or the Radeon RX 9070 XT (16 GB, 304 W typical board power, 750 W minimum PSU on AMD’s page). What those tiers hold is 24gb-vs-16gb-vs-12gb-vram.

How do I build for 70B models?

Llama 3.1 70B Q4_K_M is 43.1 GB in the quantize README. One 24 GB card is short by the weight file alone. One RTX 5090 is 32 GB and is also short of 43.1 GB. Two 24 GB cards are 48 GB of VRAM if the software can use both, which leaves 48 − 43.1 = 4.9 GB for KV cache and overhead. llama.cpp can split with --split-mode layer (the default) or --tensor-split. Ollama’s FAQ says a model that does not fit on one GPU is spread across the available GPUs. Qwen2.5-72B Q4_K_M weights estimate to 44.4779 GB, which leaves only 48 − 44.4779 = 3.5221 GB on a pair of 24 GB cards before KV. At 4,096 tokens that model’s KV term is 1.3422 GB, so the remainder is tight once the 1 to 2 GB overhead assumption is added. This is arithmetic, not a measured two-GPU run.

The power supply has to cover the cards you install. One 5090 lists 1000 W required system power and, separately, “850W Minimum” in the prepare section. One 4090 lists 850 W. One 3090 lists 750 W. Two cards are not a figure NVIDIA published as a pair on those pages, so a dual-card PSU total is unknown here beyond “at least each card’s own requirement, and the vendor did not publish a combined number.”

Which OS and software should I install?

LM Studio documents Windows on x64 and ARM (Snapdragon X Elite; AVX2 required on x64), Linux as an AppImage (Ubuntu 20.04 or newer), and macOS only on Apple silicon. The Mac install belongs on run an LLM on a Mac.

Ollama’s GPU document covers NVIDIA selection, an AMD ROCm list that includes the RX 7900 XTX and RX 9070 XT, and Vulkan. llama.cpp’s CLI README is the source for --n-gpu-layers. This page does not turn those docs into a benchmark.

Motherboard lane counts are unknown until a specific board’s spec page is cited. The GPU pages do say the link generation: RTX 5090 lists PCI Express Gen 5, and the RTX 4090, RTX 3090, and RTX 4060 pages list PCI Express Gen 4.

Disk: the quantize README says that while you are converting, memory and disk requirements match, and it lists Llama 3.1 70B original size as 280.9 GB and Q4_K_M as 43.1 GB. A finished Q4_K_M file is the smaller number. The conversion needs room for the original as well.

Sources read on 2026-10-12

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