17. Observability for GenAI: OpenLLMetry, Arize Phoenix, Langfuse & Prometheus GPU Utilization Metrics • AI Infrastructure, GPU Serving & MLOps (CUDA, Triton, TGI) • Rishabh Ki Kaksha

AI Infrastructure, GPU Serving & MLOps (CUDA, Triton, TGI) › 17. Observability for GenAI: OpenLLMetry, Arize Phoenix, Langfuse & Prometheus GPU Utilization Metrics

45 mins

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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

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# 17. Observability for GenAI: OpenLLMetry, Arize Phoenix, Langfuse & Prometheus GPU Utilization Metrics

> AI Infrastructure & MLOps Notes by Rishabh - Topic 17

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### 1. The 3 Pillars of GenAI Observability 🔭

1. **Tracing (OpenTelemetry / Langfuse / Arize Phoenix):** Multi-hop RAG chains, Agent tool calls aur intermediate prompts ka visual execution graph.
2. **Cost & Token Telemetry:** Per-user, per-feature token consumption tracking.
3. **GPU Hardware Telemetry:** Prometheus + Grafana monitoring GPU SM utilization, VRAM usage, NVLink bandwidth and thermal throttling.

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### 2. Master Comparison Matrix

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

1. **OpenLLMetry tracing bina code change kiye kaise inject hoti hai?** -> Monkey-patching / dynamic wrapping OpenAI, Anthropic, LangChain SDK network calls using OpenTelemetry spans.
2. **User Feedback (Thumb Up / Down) latency trace se kaise correlate hota hai?** -> Unique `trace_id` ko frontend response header me return karke analytics score se bind karna.
3. **GPU Throttling Grafana dashboard me kaise detect hoti hai?** -> `DCGM_FI_DEV_POWER_VIOLATION` ya `DCGM_FI_DEV_THERMAL_VIOLATION` events spike hone par.

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**Next Topic:** 18. Enterprise Cloud Cost Optimization: Spot GPU Instances, Multi-Cloud Orchestration & Slicing VRAM Costs

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