Case study

Local AI & LLMs — Offline Intelligence

Running state-of-the-art language models on consumer hardware. Private, offline, no telemetry.

Python
Llama / Mistral
Quantization
llama.cpp
Docker

The Problem

Cloud AI is powerful but comes with privacy concerns, latency, and recurring costs. I wanted a stack that could reason, code, and chat completely offline — my data never leaves my network.

The Tech Stack

The Challenge

The hardest part was optimization. Running a multi-billion-parameter model on a consumer GPU means careful quantization (GGUF), CPU layer offloading, and managing system RAM to avoid OOM crashes mid-generation.

Inference Pipeline

How a user prompt travels through the local stack to generate a response:

sequenceDiagram participant User participant WebUI as WebUI participant API as API Layer participant Model as Llama-3 (Quantized) participant Hardware as GPU/CPU User->>WebUI: Enters Prompt WebUI->>API: Formats Request (JSON) API->>Model: Tokenizes Input Model->>Hardware: Loads Layers into VRAM Hardware-->>Model: Computes Attention Model-->>API: Streams Tokens API-->>WebUI: Streams Text WebUI-->>User: Displays Response

The Solution

I used llama.cpp for efficient inference on generic hardware. With 4-bit quantization, Llama-3 dropped from 16 GB to under 6 GB of memory and ran smoothly on my GPU. I also exposed an API endpoint so other apps on the homelab (including my smart-home glue) can query the model.

What I Learned

Quantization is a wild art — the difference between Q4_K_M and Q5_K_S is real and you can feel it in the outputs. Streaming tokens is more fun than batched generation. And running your own model makes you appreciate just how much engineering is hidden behind a chat box.