Jimmy Song – Jimmy Song's 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.

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Overview jimmysong.io is a personal technical website maintained by Jimmy Song (rootsongjc@gmail.com).

This deck uses HAMi’s growth story to explore what makes an open source community valuable in the AI-coding era.

On July 11, 2026, Cloud Native Community China was officially established.

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.

This slide deck offers a practitioner’s perspective on the key trends in AI Infrastructure: the core bottleneck has shifted from “compute scarcity” to “efficiency governance”, GPU virtualization and intelligent scheduling deliver the highest ROI, the clou…

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.

Yun (运) here refers to the developmental stages and temporal rhythms experienced by a system, which can be understood as the lifecycle cycles or “fortune” of infrastructure.

Yin-Yang is originally a fundamental concept in Chinese philosophy, representing two opposing yet interdependent forces present in all things in the universe.

To maintain the long-term healthy evolution of AI infrastructure, post-mortem summaries are far from sufficient.

This paper systematically presents the four-layer model of “Yin-Yang - Five Elements - Yun - Qi” for AI infrastructure, providing a comprehensive cognitive map from theory to practice.

Qi (气) in Chinese culture refers to the energy and flow field that permeates all things.

The Yin-Yang - Five Elements - Yun - Qi Model views AI Infrastructure as an organic whole, revealing its operational mechanisms from four dimensions.

Five Elements (Wǔxíng, Five Elements or Five Phases) theory divides everything in the world into five basic elements: Wood, Fire, Earth, Metal, Water.

The theoretical models mentioned above are not 停留在停留在 the conceptual level, but directly provide guidance for the engineering practice of AI infrastructure.

Yin-Yang × Five Elements: Intrinsic Tension of Elements Each Five Elements component contains both Yin and Yang aspects, manifesting with different polarities in different contexts: Figure 3: Yin-Yang states of Five Elements.

“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.

Compute and governance boundaries are the true foundation of AI-native infrastructure architecture.

The essence of AI-native infrastructure is to make model behavior, compute scarcity, and uncertainty governable system boundaries.

The following 10 questions assess whether an organization possesses the strategic and execution readiness for AI-native infrastructure.

Unified terminology is the first step toward organizational consensus.

Migration is not “rebuilding the platform,” but using governance loops and organizational contracts to transform uncertainty into controllable engineering capabilities.

The compute governance closed loop is the foundational safeguard for sustainable innovation in AI-native organizations.

The key to governing AI-native infrastructure lies in how to institutionalize the closed-loop management of costs and risks arising from uncertainty.

The true value of architecture is enabling organizational consensus on complex systems within five minutes, not creating another new technology stack.

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.