AI Fabrics & GPU Clusters
The network is the computer. Deconstructing the forensics of RoCE v2, InfiniBand NDR, RDMA hydraulics, and the non-blocking topologies required for hyperscale bisection bandwidth.
GPU Fabric & RoCE
42 articlesInfiniBand vs RoCE v2 & RDMA Mechanics
AI Infrastructure Hub: GPU Networking & Cluster Architecture
Deep engineering resources for architecting AI training fabrics: RoCE v2 vs. InfiniBand, non-blocking topologies, and 800G GPU interconnects.
Adaptive Routing vs. Ethernet ECMP: Forensic AI Fabric Balancing
Scaling GPU clusters without the bottleneck of static hashing. Comparing InfiniBand dynamic routing vs standard Ethernet ECMP at 800G/1.6T scales.
AI Networking Infrastructure: The GPU-Centric Fabric
Master the architecture of AI networking clusters. Deconstructing RoCE v2, InfiniBand vs. Ethernet, and the engineering of non-blocking fabrics for LLM training.
AI-Driven Predictive Maintenance
Discover how AI and Machine Learning are transforming network maintenance from reactive logic to proactive, self-healing architectures.
Jupiter Rising: A Decade of Clos Topologies and Centralized Control in Google's Datacenter Network
Scaling GPU fabrics without the power tax of electronic switching. How Optical Circuit Switching (OCS) is defining the next generation of AI clusters.
NVIDIA Blackwell B200: Architecture of the 20 PFLOPS GPU
Inside NVIDIA Blackwell: Engineering analysis of the B200 dual-die architecture, 5th Gen NVLink, FP4 precision, and the transition to the 72-GPU liquid-cooled rack.
CPO Technology Roadmap for AI Supercomputing
Solving the chip-to-fiber bottleneck: CPO vs LPO vs Plugable transceivers for next-gen AI fabrics.
Bringing HPC Techniques to Deep Learning
An engineering guide to collective communication in AI clusters. Understanding NCCL, All-Reduce algorithms, and the communication wall in distributed LLM training.
BlueField DPU Architecture and Programming Guide
Reclaiming CPU cycles for AI: How DPUs manage storage, security, and networking in modern data centers.
Edge AI: Benchmarking ARM vs. RISC-V for Inference (2026)
Deep dive into decentralized AI architecture. Comparing ARM Cortex-X5, RISC-V vectors, and INT4 quantization for private local inference.
A Study of Non-blocking Switching Networks
Scaling GPU fabrics: Why Fat-Tree dominates AI while Dragonfly promises cost reduction.
FlashAttention Deep Dive: Breaking the Quadratic Memory Ceiling
A technical exploration of IO-aware attention mechanisms and SRAM tiling.
FP8 vs BF16 vs INT8: The Forensic Guide to AI Precision Scaling
Forensic analysis of AI precision formats. Comparative study of FP8 (E4M3/E5M2), BF16, and INT8 stability and throughput.
A Study of Non-Blocking Switching Networks
Advanced data center topologies for AI workloads.
GPU Performance Benchmarks: The AI Infrastructure Audit
Cross-generational engineering analysis of H100, H200, and Blackwell TFLOPS vs. HBM bandwidth. Understanding the metrics that define AI cluster performance.
NVIDIA GB200 Liquid Cooling Design Guide v1.0
Scaling high-density AI infrastructure: How to dissipate 120kW per rack using Direct Liquid Cooling (DLC).
GPUDirect RDMA: The Blueprint for Zero-Copy Networking
A deep-dive into NVIDIA GPUDirect RDMA and kernel bypass. Understand zero-copy NIC-to-GPU data paths and the physics behind low-latency training fabrics.
GPUDirect Storage (GDS): Accelerating Data-Path Performance | Pingdo AI Infrastructure
Analyzing NVIDIA GPUDirect Storage (GDS) and its impact on large-model training and checkpointing. Removing the CPU/DRAM bounce-buffer to unleash 100GB/s+ storage paths.
NVIDIA H100 vs H200: The HBM3e Memory Revolution
Comparative engineering analysis of H100 (Hopper) vs H200: Impact of HBM3e bandwidth on LLM inference, training throughput, and memory-bound scaling.
HBM3e GPU Memory Deep Dive: The 8TB/s Bandwidth Wall | Pingdo
A deep-dive into HBM3e memory technology. Understand TSV stacking, 8TB/s per-GPU bandwidth, and how the memory wall shapes AI cluster design.
Ethernet 1.6T: The AI Networking Masterclass (2026)
Deep dive into 800G and 1.6T Ethernet, 224G SerDes, and the Ultra Ethernet Consortium (UEC). Learn why Ethernet is finally matching InfiniBand for AI.
The Addition of Explicit Congestion Notification (ECN) to IP
Explicit Congestion Notification: The first line of defense in high-performance GPU networking.
NVIDIA Collective Communications Library (NCCL) Programming Guide
Scaling GPU communication: How NCCL uses Ring, Tree, and NVLink to maximize bandwidth for AI training.
InfiniBand Architecture Specification Volume 1
Scaling InfiniBand: Why the Subnet Manager is the brain of the AI infrastructure.
NVIDIA NDR800 vs. NDR400: Scaling 800G AI Fabrics
Deep dive into 800G InfiniBand architecture. Comparing Quantum-3 vs. Quantum-2, 224G SerDes, and the mechanical limits of AI cluster scaling.
Mobile NPU Optimization: Squeezing LLMs into 8GB (2026)
Advanced guide to Apple A19 Neural Engine, Qualcomm Hexagon, and Google Tensor G6. Learn about KV-cache sharding and ExecuTorch optimization.
Ethernet Frame Size and its Impact on Network Throughput
Scaling network throughput by increasing packet size. Why AI environments cannot survive on the standard 1500B MTU.
InfiniBand XDR: The Physics of Zero-Jitter AI Fabrics (2026)
Exploring InfiniBand XDR (800G) and the GDR roadmap. SHARP v4, Dragonfly+ topologies, and why IB remains the gold standard for LLM training.
NVLink vs. NVSwitch: Scaling the Intra-Node Fabric | Pingdo AI Infrastructure
Deep dive into NVIDIA's memory fabric. Analyzing the bandwidth, topology, and scale-up limits of NVLink 4.0 and NVSwitch systems for LLM training.
NVM Express Base Specification Revision 2.0
Scaling high-speed storage for AI: How NVMe-oF uses RDMA to deliver millions of IOPS over the fabric.
Parallel File Systems: Lustre, BeeGFS & GPFS | Pingdo AI Infrastructure
Deep dive into Parallel File Systems for AI cluster scaling. Analyzing the architecture of Lustre, BeeGFS, and IBM Storage Scale (GPFS) for multi-petabyte datasets.
Efficient Large-Scale Language Model Training on GPU Clusters
Scaling LLM training: How sharding models and data across thousands of GPUs changes the demands on the network fabric.
PCIe Gen6 & 7: The Host Interface for AI | Pingdo AI Infrastructure
Analyzing the transition to PCIe Gen6 (PAM4) and PCIe Gen7 for AI accelerators. Comparing bandwidth, power profiles, and IOPS requirements for dense GPU servers.
IEEE Std 802.1Qbb: Priority-based Flow Control
Understanding the balance between flow control and fair scheduling in RDMA fabrics.
Thermal and Electrical Load Balancing in Multi-GPU Racks
Moving past PUE: How voltage stability and VRM response times impact high-frequency AI inference.
DGX H100 System Architecture and Network Optimization
Scaling distributed training across thousands of GPUs: Strategy for rail alignment in AI network fabrics.
RDMA Performance Optimization: Tuning for AI | Pingdo AI Infrastructure
Deep dive into RDMA (Remote Direct Memory Access) tuning for RoCE (RDMA over Converged Ethernet) and InfiniBand. Optimizing queue depth, adaptive routing, and buffer management.
Supplement to InfiniBand Architecture Specification: RoCE v2
A deep dive into the protocol efficiency of RDMA over Converged Ethernet vs. InfiniBand.
InfiniBand Architecture Specification Volume 1
Engineering guide to InfiniBand Architecture Specification Volume 1.
Storage Infrastructure for AI | GPUDirect & NVMe-oF Deep Dive
Solve the AI IO wall. Engineering guide to GPUDirect Storage (GDS), NVMe-over-Fabrics, and high-performance checkpointing for GPU clusters.
Universal Chiplet Interconnect Express (UCIe) 1.1 Specification
Standardizing die-to-die communication: How UCIe is breaking the reticle limit for the next generation of AI compute.
Ultra Ethernet Consortium: A Scalable Transport for AI and HPC
Solving the scale problem: How UEC is building a high-performance transport layer for the world's largest GPU clusters.
Training Dynamics
3 articlesScaling Laws, MoE & Synthetic Datasets
Mixture of Experts (MoE) Explained: The Logic of Sparse Scaling
Sparse neural architectures vs. Dense models: Learn about Gating Networks, Expert Parallelism, and the engineering behind GPT-4 and Mixtral.
Synthetic Data Generation: Scaling AI Training with Procedural Data
Deep engineering guide to synthetic data pipelines: Gaussian Splatting, LLM-based refiners, and the mechanics of avoiding model collapse.
Transformer Scaling Laws: The Compute-Optimal Frontier
Scaling laws govern the relationship between compute, data, and model parameters in Transformer architectures.
Explore Specialized Engineering Hubs
Deep-dive into dedicated listing pages for every major networking discipline, optimized for professional reference and architectural planning.
GPU Fabric & RoCE
InfiniBand vs RoCE v2 & RDMA Mechanics
Cluster Topology
Rail-Optimized Fat-Tree & Non-Blocking Fabrics
800G & Optics
OSFP/QSFP112, PAM4 & Bit Error Rate (BER) Logic
Collective Comms
All-Reduce, NCCL/RCCL & Gradient Synchronization
Training Dynamics
Scaling Laws, MoE & Synthetic Datasets