# Jef Spaleta: I’ve started my LLM journey

> **Key Architectural Takeaway:** I’m going to be providing dispatches in blog form that detail my learning journey concerning LLMs.

**Published:** 2026-10-14T14:07:32+00:00 
**Source:** Planet Fedora 
**Category:** distro-planet 
**Canonical URL:** https://fosswire.org/news/jef-spaleta-ive-started-my-llm-journey.html 

## Executive Summary
I’m going to be providing dispatches in blog form that detail my learning journey concerning LLMs. I’m going to be experimenting with fully open source inference that I can run locally on hardware I control. Local Language Models; the other LLM. What utility can I get without reaching for the frontier “as a service” models? I’ve made a commitment to myself to learn, and the way I learn best is experiential.

## Architectural & Systems Analysis
From an artificial intelligence architecture, model weights governance, and inference efficiency perspective:

- **Weights Accessibility & Sovereignty:** Evaluates whether weights are open for private self-hosting or locked behind centralized cloud APIs.
- **Quantization & Edge Performance:** Kernel optimizations (4-bit/8-bit GGUF, AWQ, EXL2) allow high tokens-per-second on consumer GPUs and Apple Silicon.
- **Reasoning & Architectural Scaling:** Scrutinizes mixture-of-experts (MoE), attention mechanisms, and fine-tuning datasets against open community benchmarks.

## Impact on the Open Ecosystem
Protects developers and enterprises from proprietary black-box entrapment, fostering auditable, sovereign AI infrastructure.