Homelab

Running large language models locally usually comes down to a hard hardware boundary: video RAM. If a model does not fit into your GPU’s VRAM, performance usually falls off a cliff as soon as standard runtimes fall back to system memory over the PCIe bus. Mixture of Experts (MoE) architectures offer an attractive theoretical escape hatch. While the total parameter count can be large (30B to 70B+), only a sparse subset of expert layers activates for any given token. However, standard local runtimes still require loading the entire weight footprint into memory, which puts 30B+ models out of reach for everyday consumer laptops with 8 GB of VRAM. ...

I have a 32 GB MacBook Air. It is not a workstation GPU box, but its unified memory makes it a surprisingly capable machine for local models, provided I choose models that fit and keep expectations sensible. This is the first post in a small, practical series about running models locally. I am starting with Ollama because it gets a model running quickly without building a runtime from source or hand-managing dependencies. ...

Hey, I’m Vishnu. Welcome to my little corner of the internet. What I Do Professionally, I have about 9 years of overall experience under my belt. I actually started out writing code as a .NET developer before pivoting hard into DevOps and infrastructure engineering. Since 2019, I have been deep in the Kubernetes ecosystem, architecting and managing infrastructure across physical data centers and public clouds. Over the years, that has meant running everything from standard high-traffic web platforms to mission-critical telco workloads, airgapped enterprise environments, and AI-native GPU clusters. Nowadays, my work revolves heavily around container orchestration, SUSE and Rancher setups, vLLM inference deployments, and homelab GPU testing. ...