16. Serverless GPU Architecture: Ray Serve, Modal & RunPod for Auto-Scaling AI APIs with Zero Cold Starts • AI Infrastructure, GPU Serving & MLOps (CUDA, Triton, TGI) • Rishabh Ki Kaksha

AI Infrastructure, GPU Serving & MLOps (CUDA, Triton, TGI) › 16. Serverless GPU Architecture: Ray Serve, Modal & RunPod for Auto-Scaling AI APIs with Zero Cold Starts

50 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

Interactive Lesson Studio

# 16. Serverless GPU Architecture: Ray Serve, Modal & RunPod for Auto-Scaling AI APIs with Zero Cold Starts

> AI Infrastructure & MLOps Notes by Rishabh - Topic 16

---

### 1. The Serverless GPU Landscape ☁️

1. **Modal Labs:** Pythonic serverless functions with **sub-second GPU container boot time**!
2. **Ray Serve (Anyscale):** Distributed Python-native serving framework for complex multi-model pipelines.
3. **RunPod / Replicate / Baseten:** Managed serverless inference endpoints with per-second billing.

---

### 2. Modal Serverless LLM Deployment

```python
import modal

app = modal.App("llama3-fast-api")
image = modal.Image.debian_slim().pip_install("vllm")

@app.cls(gpu="A10G", container_idle_timeout=60)
class ModelServer:
    @modal.enter()
    def load_model(self):
        from vllm import LLM
        self.llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct")

@modal.method()
    def generate(self, prompt: str):
        return self.llm.generate([prompt])
```

---

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

1. **Serverless GPU cold starts ko < 1 second kaise optimize kiya jata hai?** -> Memory snapshotting (CRIU / Firecracker memory state restore) aur pre-allocated shared warm pool caches use karke.
2. **Per-Second Billing vs Dedicated GPU Instance me cost break-even point kya hota hai?** -> Agar GPU continuous $> 60\%$ daily active utilization par run ho raha ho toh Dedicated VPS cheaper hota hai; bursty/irregular traffic ke liye Serverless saves 80% costs.
3. **Ray Serve multi-replica routing kaise handle karta hai?** -> Centralized proxy router queue maintain karta hai aur least-busy worker replica ko incoming gRPC/HTTP request dispatch karta hai.

---
**Next Topic:** 17. Observability for GenAI: OpenLLMetry, Arize Phoenix, Langfuse & Prometheus GPU Utilization Metrics

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