Module 07: Scale & Multi-Node

Distributed AI: ZeRO, 3D Parallelism & Ray

Interactive technical curriculum synthesized from FAANG and Tier-1 AI startup interview rounds.

Distributed AI: ZeRO, 3D Parallelism & Ray Clusters

Comprehensive coverage of QA-20, QA-25, QA-30, QA-33, and QA-39. Master DeepSpeed ZeRO-1/2/3 static memory proofs, 3D Parallelism across NVLink and InfiniBand, and InfiniBand vs RoCE step-time jitter triage.

Interactive DeepSpeed ZeRO Memory Proof Calculator

Enter your model parameters and GPU cluster size to prove the exact per-GPU static memory savings across ZeRO stages:

Model Parameter Count (Billions): Data-Parallel GPUs (N):

Standard Mixed Precision (16P Static):

1,120 GB (Cannot fit on 1 GPU!)

ZeRO-1 (Optimizer Partitioning):

332.5 GB / GPU

ZeRO-2 (Optimizer + Gradient):

201.2 GB / GPU

ZeRO-3 (Full Parameters + Opt + Grad):

70.0 GB / GPU (Fits in 80GB VRAM!)

QA-25 • Multi-Node Topology

Tensor (TP) vs Pipeline (PP) vs Data Parallelism (DP) Across 256 GPUs

Source: Intel / Enterprise Infra Panel • Track: AI Platform • Difficulty: Hard

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The Hardware Boundary Rule:

Tensor Parallelism (TP=8): Must stay intra-node over NVLink (900 GB/s). TP splits matrix operations layer-by-layer, requiring massive high-frequency All-Reduce synchronizations. Running TP over Ethernet or standard InfiniBand will crater performance.
Pipeline Parallelism (PP=8): Runs inter-node over InfiniBand. Layers are partitioned sequentially across nodes; only activation tensors flow between pipeline stages.
Data Parallelism (DP=4): Replicates 3D grids across the cluster with ZeRO-3 / FSDP gradient reductions.