07. Speculative Decoding & Medusa Multi-Head Decoding for 2-3x Faster Token Generation • AI Infrastructure, GPU Serving & MLOps (CUDA, Triton, TGI) • Rishabh Ki Kaksha

AI Infrastructure, GPU Serving & MLOps (CUDA, Triton, TGI) › 07. Speculative Decoding & Medusa Multi-Head Decoding for 2-3x Faster Token Generation

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

# 07. Speculative Decoding & Medusa Multi-Head Decoding for 2-3x Faster Token Generation

> AI Infrastructure & MLOps Notes by Rishabh - Topic 07

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### 1. The Memory-Bound Decoding Wall 🧱

LLM autoregressive decoding memory bandwidth bound hoti hai (1 token generate karne ke liye poora 70GB model weights VRAM se GPU register me stream karna padta hai).  
**Speculative Decoding (Leviathan et al. ⚡):**
*   **Draft Model (e.g. Llama-3-1B):** Fast $K=4$ candidate tokens generate karta hai in parallel.
*   **Target Model (e.g. Llama-3-70B):** Saare 4 candidate tokens ko **1 single forward pass me parallel verify karta hai!**
*   *Outcome:* $2\times$ to $3\times$ faster token generation with **0% loss in mathematical output distribution!**

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### 2. Medusa: Multi-Head Speculative Decoding Without Draft Models 🐍

Draft model manage karne ke badle main model ke last layer par **Multiple Speculative Heads** attach kar dena jo future positions $t+1, t+2, t+3$ simultaneously predict karte hain!

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

1. **Speculative Decoding output distribution exact identical kyu rehti hai?** -> Acceptance/Rejection sampling algorithm ensure karta hai ki rejected tokens ke baad exact target model probabilities se fallback resample ho.
2. **Draft Model acceptance rate kab drop hoti hai?** -> Highly unpredictable, creative, ya high-temperature text generation me; technical, repetitive code generation me acceptance rate $> 85\%$ rehti hai.
3. **Speculative Decoding compute-bound systems par kyu nahi kaam aati?** -> Jab batch size bahut bada ho (e.g. Batch=128), GPU already compute saturated hota hai aur memory bandwidth available nahi hoti.

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**Next Topic:** 08. Distributed AI Parallelism Paradigms: Data Parallelism (DP), Tensor Parallelism (TP), Pipeline Parallelism (PP)

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