Est. Reading Time: 15 Mins Prereq: GPU Acceleration
Architectural Track // AI Infra 03

Distributed AI Fabrics

Tagline: Networking the Neural Network.

Strategic engineering for multi-node GPU scaling. Focus: InfiniBand architecture, RDMA (Remote Direct Memory Access) logic, and RoCE v2 implementation patterns.

The Protocol

Level 100: RDMA Logic

  • Zero-Copy: Moving data between GPU memories without CPU interrupts.
  • Kernel Bypass: Eliminating OS overhead for direct hardware-to-hardware flow.
  • Latency Targets: Designing for sub-microsecond synchronization.

Architect’s Verdict: Traditional TCP/IP is too slow for AI. RDMA is the mandatory baseline for distributed training.

Fabric Choice

Level 200: InfiniBand vs. RoCE

  • InfiniBand: Lossless, credit-based flow control for maximum efficiency.
  • RoCE v2: RDMA over Converged Ethernet for leveraging existing switching.
  • Congestion Control: Managing tail-latency in high-density AI clusters.

Architect’s Verdict: InfiniBand remains the gold standard for performance, while RoCE v2 is the path for Ethernet-first organizations.

The Grid

Level 300: Non-Blocking Design

  • Rail-Optimized Topology: Dedicated switches per GPU rail for minimal hops.
  • Adaptive Routing: Dynamically bypassing fabric congestion.
  • SHARP Technology: Offloading collective communications to the network fabric.

Architect’s Verdict: At the scale of 10,000+ GPUs, the network becomes the computer. Architecture is everything.