On July 16, 2026, Li Mengxuan, Co-founder & CTO of Dynamia and HAMi author, delivered a technical talk on vLLM deployment and compute optimization at vLLM Meetup. Built around one pointed question, "Are you making good use of your compute?", the talk laid out a complete evolution path for vLLM inference clusters, from "getting it to run" to "squeezing the hardware dry", broken down into three clear stages.
This recap walks through the talk slide by slide, combining the deck with the on-site Q&A notes.
Source: mesutoezdil.substack.com
GitHub Repo: kagentWithHami
Chinese translation by Jimmy Song, originally published on WeChat
One physical NVIDIA L40S virtualized into 10 vGPUs with HAMi. An AI Agent deployed as a Kubernetes CRD via kagent. Agent-to-Agent delegation, GPU pod creation, overcommit protection - all driven by Llama 3.3 70B with no closed-source dependencies.
Managing GPU resources in Kubernetes has long been a "blind spot" for operators. You know GPUs are being used, but answering questions like "which node has idle capacity?", "is this workload actually utilizing its allocated GPU?", or "what is the overall cluster utilization trend?" often requires piecing together kubectl get, Prometheus PromQL, and log output.
Today, the HAMi community is introducing HAMi WebUI - an open-source GPU monitoring dashboard that puts your entire GPU cluster into a single, visual interface.
HAMi WebUI v1.1.0 is now available as the first official major release.
Together with the core HAMi scheduler, WebUI completes the full loop: from GPU scheduling to visual observability.
