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.
The integration target here is strictly HAMi-core, not the full HAMi platform. KAI Scheduler keeps its own scheduling capability and brings in HAMi-core to provide GPU memory isolation.
In June 2026, two core PRs were officially merged into the NVIDIA KAI Scheduler main branch. HAMi's GPU memory hard isolation shipped as a built-in feature starting with KAI Scheduler v0.16.4. Cloud-native GPU scheduling has officially moved from "cooperative sharing" into the "hard isolation" era.
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.
