09. Tensor Parallelism in Action: Megatron-LM Matrix Splitting across NVLink & NVSwitch Networks • AI Infrastructure, GPU Serving & MLOps (CUDA, Triton, TGI) • Rishabh Ki Kaksha

AI Infrastructure, GPU Serving & MLOps (CUDA, Triton, TGI) › 09. Tensor Parallelism in Action: Megatron-LM Matrix Splitting across NVLink & NVSwitch Networks

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Course Syllabus

Module 1: GPU Architecture, CUDA Internals & Hardware Hierarchy

01. NVIDIA GPU Architecture: Streaming Multiprocessors (SMs), Tensor Cores, CUDA Cores & HBM3 Memory Bandwidth 02. GPU Memory Hierarchy: Registers, Shared Memory (SRAM), L1/L2 Caches & High-Bandwidth VRAM 03. CUDA Programming Basics: Writing Custom GPU Kernels in C++/CUDA & Triton Python DSL

Part 1: GPU Acceleration, Serving & Production Guardrails

Module 2: High-Performance LLM Serving Engines (vLLM, TGI, Triton)

04. The KV Cache Bottleneck: Memory Footprint Calculations & PagedAttention Virtual Memory Architecture 05. Dynamic & Continuous Batching (Iteration-Level Scheduling) vs Static Batching in LLM Serving 06. Production Benchmark: vLLM vs HuggingFace TGI (Text Generation Inference) vs NVIDIA Triton Inference Server 07. Speculative Decoding & Medusa Multi-Head Decoding for 2-3x Faster Token Generation

Module 3: Multi-GPU & Distributed Inference: Tensor & Pipeline Parallelism

08. Distributed AI Parallelism Paradigms: Data Parallelism (DP), Tensor Parallelism (TP), Pipeline Parallelism (PP) 09. Tensor Parallelism in Action: Megatron-LM Matrix Splitting across NVLink & NVSwitch Networks 10. DeepSpeed & PyTorch FSDP (Fully Sharded Data Parallel): Zero Redundancy Optimizer (ZeRO-1, ZeRO-2, ZeRO-3)

Module 4: MLOps Pipelines: Model Registry, Experiment Tracking (MLflow/W&B)

11. MLOps Foundations: Model Versioning, Artifact Registries & Lineage Tracking with MLflow and Weights & Biases 12. Automated Continuous Training Pipelines with Kubeflow Pipelines & GitHub Actions CI/CD for AI Models 13. Feature Stores in Production (Feast): Real-Time Online Feature Retrieval vs Offline Batch Processing

Module 5: Kubernetes GPU Orchestration, Auto-Scaling & Continuous Observability

14. Kubernetes for AI Workloads: NVIDIA GPU Operator, Device Plugins & Time-Slicing vs Multi-Instance GPU (MIG) 15. Horizontal Pod Auto-Scaling (HPA) for LLM Services based on TTFT (Time to First Token) & Queue Depth 16. Serverless GPU Architecture: Ray Serve, Modal & RunPod for Auto-Scaling AI APIs with Zero Cold Starts 17. Observability for GenAI: OpenLLMetry, Arize Phoenix, Langfuse & Prometheus GPU Utilization Metrics 18. Enterprise Cloud Cost Optimization: Spot GPU Instances, Multi-Cloud Orchestration & Slicing VRAM Costs

Interactive Lesson Studio

# 09. Tensor Parallelism in Action: Megatron-LM Matrix Splitting across NVLink & NVSwitch Networks

> AI Infrastructure & MLOps Notes by Rishabh - Topic 09

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### 1. Megatron-LM Column-Parallel & Row-Parallel Matrices (NVIDIA) 📐

*   **Column Parallel ($W_1$):** Weight matrix ko column-wise split karta hai: $W_1 = [W_{1,1} \mid W_{1,2}]$. (No communication needed after split!).
*   **Row Parallel ($W_2$):** Weight matrix ko row-wise split karta hai: $W_2 = \begin{bmatrix} W_{2,1} \\ W_{2,2} \end{bmatrix}$.
*   *Output Combination:* Dono outputs ko sum karne ke liye **1 single `All-Reduce` operation** lagta hai!

```
[ Input x ] ──► [ Column Parallel W1 ] ──(GeLU)──► [ Row Parallel W2 ] ──► [ All-Reduce Sum ] ➔ Final Output
```

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### 2. Interview Ke 3 Sawal Is Topic Se

1. **NVSwitch fabric multi-GPU communication me kya advantage deta hai?** -> Har GPU har doosre GPU se full 900 GB/s bidirectional bandwidth par communicate kar sakta hai (Non-blocking crossbar switch).
2. **Column-Parallel MLP activation function se pehle sync kyu nahi karta?** -> Kyunki GeLU element-wise function hota hai: $\text{GeLU}([y_1 \mid y_2]) = [\text{GeLU}(y_1) \mid \text{GeLU}(y_2)]$, isliye communication delay zero hota hai.
3. **NCCL (NVIDIA Collective Communications Library) Tensor Parallelism me kaunse primitives use karta hai?** -> `ncclAllReduce`, `ncclAllGather`, aur `ncclReduceScatter`.

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**Next Topic:** 10. DeepSpeed & PyTorch FSDP (Fully Sharded Data Parallel): Zero Redundancy Optimizer (ZeRO-1, ZeRO-2, ZeRO-3)

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