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HAMi Meetup Shanghai Deep Dive (1): Where Does HAMi Fit in the CNCF × PyTorch Cloud Native AI Foundation?
The model runs, yet the business never ships; one team waits for GPUs while another's sit idle. In his opening keynote at HAMi Meetup Shanghai, CNCF China Director Keith Chan laid out a layered view of a cloud native AI R&D foundation — with HAMi in the scheduling and resource management layer, handling heterogeneous compute sharing. Understanding that position tells you what HAMi solves, and which components it needs alongside it.
View more →HAMi Meetup Shanghai Deep Dive (7): From User to Co-Builder — Why Enterprises Need a Sustainable HAMi Community
Once open source enters production, enterprise attention extends beyond the code: will issues get answers, how do needs reach the public roadmap? The HAMi Meetup Shanghai panel covered real-world usage, contribution barriers, responsibility boundaries in AI-assisted development, and knowledge preservation — a second set of selection criteria beyond technology fit.
View more →HAMi Meetup Shanghai Deep Dive (2): After GPU Sharing — Isolation, Scheduling, and Observability in HAMi 2.10
Slicing one GPU across multiple jobs is only the beginning of sharing. Dynamia co-founder and CTO and HAMi Maintainer Li Mengxuan walked through HAMi v2.10 progress, DRA ecosystem adaptation, and the v2.11 roadmap announced on stage (Remote GPU, CPU NUMA alignment). The through-line: after sharing, compute must be allocated on demand and constrained, observable, and diagnosable while it runs.
View more →HAMi Meetup Shanghai Deep Dive (4): Turning GPU Performance into Inference Service Capability — Lessons from Iluvatar CoreX
The GPU benchmarks well, yet users still wait for the first token or feel stalls mid-generation. Ye Chenglin, Infra R&D Director at Iluvatar CoreX (天数智芯), shared a full engineering path for domestic-GPU inference at HAMi Meetup Shanghai: execution optimization, topology-aware scheduling, and inference orchestration — accepted against end-to-end SLOs, with HAMi covering the small-model GPU-sharing branch.
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