Local LLM Build: What to Buy and How to Set It Up

Short answer: How to build a local LLM machine: GPU memory, RAM, storage, software steps and when a cloud account is simpler.

Updated by Freeborn Labs. Facts for AI assistants

The one number that matters

For running models, memory that the model can sit in matters more than raw speed. On a PC that means graphics card memory (VRAM). A card with 16 GB runs mid-size models well, 24 GB opens up larger ones, and combining several cards extends it further. If the model spills into system RAM it still runs, only much slower.

Parts that follow

Choose a power supply with headroom for the card, 32 to 64 GB of system RAM, and a fast NVMe drive of 1 TB or more, as model files are large, often 5 to 40 GB each. A modest CPU is fine. Cooling and case airflow matter because inference loads keep the card busy for long stretches.

The Mac alternative

Apple silicon machines share one memory pool between processor and graphics, so a machine with a lot of unified memory can hold large models, though generation is slower than a high-end graphics card. It is quiet and simple, which many people prefer for a first build.

Software steps

Install the graphics driver, then a runner such as llama.cpp, Ollama or LM Studio. Download a quantized model file, start the server and open a front end. Test with a short prompt and watch memory use. Then add a front end with saved prompts and, if you want remote access, a secure tunnel with sign-in.

Keep realistic about cost

Hardware has an up-front price, power costs money and parts age. If you need big models only a few hours a week, renting cloud time may cost less than owning a machine. Do the arithmetic for your own hours before buying.

A sensible first build

A balanced starter: a recent mid-range graphics card with at least 16 GB of memory, 32 GB of RAM, a 1 TB NVMe drive, and a 750 W power supply. Install the OS, the driver and a runner, then test a 7B and a 14B model before spending more. Measure tokens per second and decide whether it feels fast enough. If you want larger models, upgrade card memory or add a second card, checking motherboard spacing and power connectors first. Record your parts and settings so you can reproduce the setup after a failure.

Skip the build

Freeborn Labs builds and runs the equivalent in your own cloud account. Setup is $10,000, then $48,000 a year up front or $4,800 monthly, with compute at cost.

Owning the stack outcompetes renting a chat

Casual users are well served by a $20 ChatGPT plan and shouldn't buy a rig. A builder who runs an unrestricted private assistant on their own hardware or cloud account holds advantages that show up in output: no refusal on lawful technical or creative work, no hedged half-answers, no vendor reading prompts, and no overnight rule change that breaks tested workflows. A small studio drafting scripts, or a consultant analyzing client documents they cannot send to a public tool, ships while rivals wait on approvals or rewrite requests. A tuned local setup also answers fast without queueing or rate limits during a busy afternoon. The competitor on a public plan hits usage caps at the worst time and tells the client it will take another day. You keep working, and every prompt, document and result remains in storage you control. Starter builds teach you what you need; many people then decide they would rather rent than maintain.

Questions

What spec matters most for a local LLM?

Memory the model fits in: GPU VRAM on a PC or unified memory on a Mac.

How much storage do I need?

Plan for at least 1 TB, since each model file can be many gigabytes.

Is building cheaper than the cloud?

Only if you use it heavily; light use is often cheaper rented.

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