Local LLMs and workstation agents: Part 1

Official Schedule Context

Have you heard "Buy a GPU," "Opensource AI Must Win," or "Local AI FTW" before? This workshop will

be a practical window into that confusing world and a practical map for understanding what different

Local AI hardware is actually capable of and which models make sense on each class of machine.

Whether you are just getting started or already running models every day, we will demo and work

through why a Mac mini, M4 Pro MacBook Pro, M5 Max MacBook Pro, RTX 5070 8GB laptop, Strix Halo box,

DGX Spark, and 2x RTX PRO 6000 Blackwell machine should not be configured, benchmarked, or used the

same way. What are you trying to run? How much VRAM or Unified Memory do you actually need? When

does a small machine make sense? When do you need a real GPU box? When does long context, tensor

parallelism, or serving infrastructure start to matter? This should be useful to everyone: people

curious about local AI, people buying their first capable machine, people already running models,

and people trying to use local inference for scalable agentic workflows. We will close by showing

how Codex can automate the boring part: give it my Inference Engine article, the hardware target,

and the model of your choice, then ask it to propose the engine, environment, flags, batch settings,

KV-cache settings, and benchmark and evaluation plan.

No official session recording transcript was found by exact title match on the AI Engineer YouTube channel during this run.

Pending transcript synthesis when an official recording or confirmed matching video is available.