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LLM Inference Optimization Part 4 — Production Serving

LLM Inference Optimization Part 4 — Production Serving

Production deployment with vLLM and TGI. Continuous Batching, Speculative Decoding, memory budget design, and throughput benchmarks.

- AI Engineering

LLM Inference Optimization Part 3 — Sparse Attention in Practice

LLM Inference Optimization Part 3 — Sparse Attention in Practice

Sliding Window, Sink Attention, DeepSeek DSA, IndexCache, and Nvidia DMS. From dynamic token selection to Needle-in-a-Haystack evaluation.

- AI Engineering

LLM Inference Optimization Part 2 — KV Cache Optimization

LLM Inference Optimization Part 2 — KV Cache Optimization

KV Cache quantization (int8/int4), PCA compression (KVTC), and PagedAttention (vLLM). Hands-on memory reduction code and scenario-based configuration guide.

- AI Engineering

LLM Inference Optimization Part 1 — Attention Mechanism Deep Dive

LLM Inference Optimization Part 1 — Attention Mechanism Deep Dive

Build Self-Attention from scratch. Compare MHA → GQA → MQA evolution in code. KV Cache mechanics and Prefill vs Decode analysis.

- AI Engineering

Flash Attention vs Sparse Attention — The Key to Faster LLM Inference

Flash Attention vs Sparse Attention — The Key to Faster LLM Inference

From principles to benchmarks: Flash Attention vs Sparse Attention. DSA, DMS, Sliding Window comparison with a decision matrix for choosing the right approach.

- AI Engineering

KV Cache Explained — Why LLMs Eat So Much Memory

KV Cache Explained — Why LLMs Eat So Much Memory

What the KV Cache is, why it consumes so much memory, and how to calculate exact costs per model. GQA/MQA comparison, VRAM budget calculator included.

- AI Engineering