Jimmy Song – Blog

Models getting bigger and GPU partitioning getting more important are two things that can be true at the same time.

Standing in the Shanghai venue in 2026, I found myself thinking about the days eight years ago when Dan Kohn and I organized the first KubeCon China.

GPU scheduling is not just “who gets which card”—it is answering three layered questions in sequence: which node, which card, and where the card sits inside the machine.

For GPU sharing to become a governable infrastructure capability, what has been missing was never partitioning—it was a verifiable correspondence between scheduling decisions and runtime isolation.

After HAMi became a CNCF Incubating project, I want to talk about something overlooked: AI is shifting the scarce resource of open source communities from code to consensus.

HAMi used to save cards in the cluster.

In a bamboo-weaving workshop in Anji, holding a strip of bamboo split hair-thin, I realized for the first time: a bolt of cloth, a machine, a country, all begin with this single thread in the hand.

Note This post builds on Ameen Alam’s three-part GPU Architecture series and draws on TrendForce, NVIDIA (Slinky/Slurm, the GPUDirect data path) and Mirantis material on GPU infrastructure and agentic AI.

Every GPU should not just be used.

Statement This is my reading and critique of the paper “AI Infrastructure Reliability Features and Architecture for Agentic AI” (June 2026), shared by Hesham ElBakoury in the Open Compute Project (OCP) community.

GPU utilization is not the destination.

A personal AI infrastructure is not a single tool — it’s a system of long-term synergy.

The Linux Foundation recently announced plans to establish the Tokenomics Foundation.

AI Native Landscape now has its own home. 600+ curated open-source projects, a brand-new standalone site, and direct search from your AI coding tools.

Recently, I’ve been following the progress of domestic GPU scheduling and Kubernetes AI resource models.

Kubernetes hasn’t been replaced by AI, but it’s being redefined by it.

Today marks my first day at KubeCon Europe 2026.

This redesign is more than a style update—it’s a step toward clearer technical communication and better user experience.

AI is quietly reshaping the infrastructure landscape, and GTC 2026 may become a key node in this transformation.

The future of GPU scheduling isn’t about whose implementation is more “black-box”—it’s about who can standardize device resource contracts into something governable.

“The best way to learn AI is to start building.

“If I have seen further, it is by standing on the shoulders of giants.” — Isaac Newton Figure 1: Standing on Giants’ Shoulders: The Traditional Infrastructure Powering Modern AI In the excitement surrounding LLMs, vector databases, and AI agents, it’s easy

Time flies—it’s already been a month since I joined Dynamia.

The role of Spec is undergoing a fundamental transformation, becoming the governance anchor of engineering systems in the AI era.

Voice input methods are not just about being “fast”—they are becoming a brand new gateway for developers to collaborate with AI.

The divide in technical standards and data sovereignty determines the global competitive landscape of infrastructure open source in the AI era.

Compute governance is the critical bottleneck for AI scaling.

I’ve been spending more time recently experimenting with vibe coding tools on real projects, not demos.

The waves of technology keep evolving; only by actively embracing change can we continue to create value.

The success or failure of AI applications often lies not in the technology itself, but in the ability to scale delivery and create a closed loop.

Institutionalized open source marks a new starting point for China’s AI Infra, but true breakthroughs and risks lie in the engineering and governance details.

In 2025, the core of software engineering is no longer just about code itself, but about runtime controllability and cost governance.

As a long-time practitioner in the cloud native field, I am increasingly convinced of one thing: AI Agents are not just a change in application form, but a migration of infrastructure paradigms.

The AI Open Source Landscape is not just a project directory, but an innovative attempt to bring transparency and quantifiability to the AI open source ecosystem.

The real turning point for AI in 2026 is not autonomy, but the maturity of infrastructure - where agentic runtimes, GPU efficiency, and organizational design will decide who wins.

Attending COSCon'25 in Beijing, I observed firsthand how open source in China is shifting: AI is now the default context, discussions are grounded in real engineering, and the community is embracing long-term thinking.

Goose is not a project that excites you at first glance in this wave of Agent innovation, but its entry into AAIF signals a deeper shift in how we think about Agentic Runtime and AI-Native infrastructure.

The deep integration of cloud native and AI, with the ARK platform, provides a new paradigm for engineering multi-agent systems.

“Open source” in the AI era is no longer a trustworthy promise.

The standardization and open collaboration of the agent ecosystem is no longer a luxury, but the critical watershed for whether AI Native can be engineered and implemented.

The shifting ownership of runtimes is reshaping the underlying logic of AI programming and infrastructure.

Statement ArkSphere has no affiliation or association with McKinsey Ark.

Statement ArkSphere has no affiliation or association with McKinsey Ark.

The real inflection point for AI engineering is not “how many people use it,” but “how many people cannot do without it.”

The biggest pain point when switching IDEs is user habits.

The greatest risks to modern internet infrastructure often aren’t in the code itself, but in those implicit assumptions and automated configuration pipelines that go undefined.

The second half of cloud native isn’t about being replaced by AI, but being rewritten by it.

NotebookLM is the most tailored AI tool I’ve used for knowledge workers.

The release of Helm 4 is not just a technical upgrade, but a deep convergence of cloud-native delivery paradigms.

China’s large language models have finally moved from “writing like humans” to “thinking like humans.”