🚀HardInference OptimizationPREMIUM

Scaling LLM Inference

Explores LLM inference optimization: KV cache management, continuous batching, PagedAttention, and speculative decoding.

What you'll master

Prefill vs decode phases

TTFT vs TPOT metrics

KV cache memory management

Static vs continuous batching

PagedAttention and vLLM

Speculative decoding

Memory-bandwidth bottleneck

Model Bandwidth Utilization (MBU)

Disaggregated inference

Context parallelism

Low-precision inference (FP8/FP4)

Hard50 min readIncludes code examples, architecture diagrams, and expert-level follow-up questions.

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