Google Cloud’s G4 architecture delivers 168% higher throughput by maximizing PCIe Gen 5 performance. This deep dive examines the engineering stack driving these gains, from direct P2P communication and NUMA optimizations to Titanium offloads. Explore how G4 transforms standard connectivity into a high-speed fabric for demanding AI inference and training.
Understanding how to partition a single GPU into multiple isolated instances for cost-efficient AI workloads, with a deep dive into NVIDIA's MIG technology and the architectural differences between GKE and EKS..
As organizations pivot from AI experimentation to enterprise-scale deployment, a recurring structural friction often emerges. Through my engagements with leadership teams in APAC, it has become clear that without centralized orchestration, AI initiatives inevitably fragment, diluting ROI and increasing risk. In this analysis, I examine the strategic necessity of the Chief AI Officer (CAIO) - defining why this role is the essential bridge between technical capability and sustainable business value.
Generative AI has shifted data center traffic patterns, making network performance the new bottleneck for model training. This post contrasts how the "Big Three" cloud providers utilize distinct architectures to solve this challenge. We examine Google Cloud’s evolution from proprietary TCPX to standard RoCEv2 on its optical Jupiter fabric, AWS’s innovation of the Scalable Reliable Datagram (SRD) protocol to mitigate Ethernet congestion, and Azure’s adoption of native InfiniBand.
Demystifying hardware acceleration and the competing sparsity philosophies of Google TPUs and Nvidia. This post connects novel architectures, like Mixture-of-Experts, to hardware design strategy and its impact on performance, cost, and developer ecosystem trade-offs.
AI benchmarks are fundamentally broken, putting enterprise budgets at risk. This post deconstructs the technical flaws and outlines a strategy for building internal evaluations that actually predict real-world performance.