Practical notes and experiments around local LLM inference, AI agents, model evaluation, memory/retrieval, and GPU/runtime constraints.
This repository is my working lab notebook as I move deeper into AI infrastructure, LLMOps, and agentic AI systems.
It is based on my own hands-on experimentation with local LLMs, consumer GPU hardware, inference runtimes, long-context workflows, local/cloud hybrid agents, and practical model evaluation.
This is not intended to be a polished product or a perfect benchmark suite. It is a place to document what I test, what I learn, what breaks, and what seems useful for real AI infrastructure work.
Most AI discussions focus on the model itself.
In practice, the model is only one part of the system. The surrounding infrastructure matters just as much:
- how the model is served
- how much VRAM and RAM it needs
- how context length affects reliability
- how quantization changes performance and quality
- how agents use tools and memory
- how local models compare with cloud models
- how workflows behave under real usage, not only clean demos
This repository helps me organize those lessons in a way that is useful for AI infrastructure, LLMOps, AI platform, and GenAI solution architecture roles.
- Local LLM inference
- GPU-based model serving
- llama.cpp and GGUF model usage
- Quantization and runtime trade-offs
- Long-context model behavior
- Local/cloud hybrid AI agents
- Memory and retrieval layers
- LLM evaluation for real workflows
- Agentic coding reliability
Main local lab machine:
- CPU: AMD Ryzen 7 7800X3D
- GPU: NVIDIA RTX 4090 24GB
- RAM: 32GB DDR5
- Storage: NVMe SSDs
- OS: Ubuntu / Windows dual-boot environment
This setup is useful because it forces practical constraints. A single consumer GPU is powerful, but it still requires trade-offs around model size, quantization, context length, runtime choice, and memory usage.
This repository is still early. Planned and ongoing notes include:
- local LLM runtime setup
- llama.cpp and GGUF model notes
- quantization trade-offs
- long-context testing notes
- local agent architecture
- model evaluation methodology
- memory and retrieval experiments
- practical lessons from local AI infrastructure work
I am using this lab to connect my infrastructure and systems solution engineering background with my next career direction: AI infrastructure, LLMOps, AI platform engineering, and GenAI solution architecture.
My focus is practical: understanding how LLM systems behave when they need to run, scale, use tools, manage context, retrieve memory, and stay reliable beyond simple demos.