Compilers, Static analysis, software performance optimizations.
This post has NOT been generated using AI. Like most of those who I believe would be reading this blog, I run my own home lab. My setup has, over the years, evolved from an old laptop running just my media centre, only accessible on my home network. Today, it is a more elaborate setup with containers running on two hosts on my local network, a third host running local LLMs, and a fourth (thin) host in the cloud running a reverse proxy. The reverse proxy forwards requests to services running in the hosts on my local network, allowing me to access some services from anywhere. I like the setup. In essence, it is quite simple. All I have to deal with is a container engine (I use Podman Quadlet) and my reverse proxy, outside the actual services deployed in the lab. I have avoided jumping into the Kubernetes (or similar) bandwagon, as I still don’t think the overhead is justified as yet.
TLDR We deployed an AI autoresearch agent to tune GLM-5.2 serving on our NVIDIA B200 cluster.
Track AI's real-world impact through sourced evidence, public signals, company impact scores, and interactive indexes.
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