Guide

What is a local-inference PC for AI agents?

A local-inference PC runs large language models and AI agents on your own hardware instead of a cloud API. Your prompts and data never leave the machine. Here is what that means, why it matters now, and how the hardware actually works.

The plain definition

A local-inference PC is a computer built to serve LLM inference on-premises. Instead of sending each request to a cloud endpoint and paying per token, you point your tools at a box on your desk. It loads the model into memory once and answers locally, at a fixed cost, fully private.

Why it matters now

Two things changed. Open models in the 27B class (Qwen, GLM, DeepSeek, Llama) now match what you needed a frontier API for a year ago. And the software to run them fast on consumer hardware, custom CUDA kernels and speculative decoding, finally exists. Together they make a desk-sized box a real alternative to a recurring cloud bill.

How the hardware works

Running a large model quickly comes down to two resources working together:

• Fast VRAM for the hot weights. A 24 GB RTX 3090 holds a 27B model at Q4_K_M and pushes raw tokens per second with 10,496 CUDA cores at 936 GB/s.
• Large unified memory for long context and bigger models. 128 GB of LPDDR5X on a Ryzen AI MAX+ 395 keeps context resident without spilling to disk.

The trick is pairing the two and then tuning the inference engine to the exact silicon. Most machines run a general-purpose runtime at stock and leave four to six times the throughput on the table. A tuned stack does not.

What to look for

• Throughput on a real model. Ask for tokens per second on a named 27B model, not a synthetic score.
• Memory pairing. A GPU alone is not enough; you want VRAM plus unified memory.
• A tuned engine, not a stock runtime.
• Tool compatibility. It should speak the OpenAI or Anthropic API so your existing agents just work.
• Privacy and support. Fully local, with a warranty.

A worked example

On a single RTX 3090 with a tuned stack (lucebox-hub: custom kernels plus DFlash speculative decoding), Qwen3.5-27B Q4_K_M runs at up to 207 tok/s, several times faster than the same card at stock. Long-context prefill, normally the slow part, drops from minutes to seconds with speculative prefill. That is the difference between hardware and a tuned product.