15 articles
A systematic deep dive into Continuous Batching for LLM inference serving—covering principles, scheduling, engineering deployment, monitoring metrics, and go-live checklists to reduce queuing waste and maximize GPU throughput.
A systematic guide to PagedAttention principles, paged KV Cache management, continuous batching synergy, monitoring metrics, and pre-deployment checks to reduce GPU memory fragmentation and boost throughput in LLM inference serving.
A comprehensive guide to Prefix Caching for LLM inference: principles, suitable workloads, engineering integration, monitoring, security boundaries, and common pitfalls for RAG, Agent, and multi-turn dialogue applications.
A systematic guide to KV cache quantization for long-context LLM inference: value, principles, vLLM/TensorRT-LLM implementation, evaluation metrics, and production risks to help teams safely reduce memory and latency costs.
A systematic guide to disaggregating LLM prefill and decode phases. Covers the root cause of TTFT vs TPOT conflicts in long-context scenarios, KV cache transfer, dynamic routing, resource allocation, monitoring metrics, and a production launch checklist.
A systematic guide to continuous batching for LLM serving: core principles, scheduling strategies, KV Cache constraints, engineering deployment steps, and monitoring metrics. Covers the full path from proof-of-concept to production, helping teams boost throughput while controlling tail latency in high-concurrency scenarios.
A deep dive into how PagedAttention mitigates GPU memory fragmentation and over-reservation in LLM inference via paged KV cache management, helping engineering teams increase throughput, reduce latency variance, and providing a production deployment checklist and monitoring strategy.
A systematic guide to Prefix Caching for reusing KV Cache of shared prompts and contexts, reducing TTFT, inference costs, and redundant prefill in RAG, Agent, and long-document QA scenarios. Includes engineering checklists for vLLM, OpenAI API, and SGLang.
A practical guide to Prefill-Decode disaggregation for LLM serving: principles, production architecture, monitoring, and a go-live checklist to govern TTFT and TPOT under long-context, high-concurrency workloads.
Explains the Attention Sink phenomenon, how StreamingLLM retains initial sink tokens and a recent window to reduce GPU memory pressure without fine-tuning, and provides production deployment boundaries, monitoring metrics, and a go-live checklist.
KV Cache quantization isn't just about lowering precision; it's a complete serving governance strategy involving memory capacity, attention bandwidth, model accuracy, calibration data, and online fallback policies.
A systematic guide to KV Cache reuse for long-context LLM serving: Prefix Caching, RadixAttention, multi-tier offloading, hit rate, latency, cost, and production checklists.
Long contexts and agentic requests shift LLM inference bottlenecks from single-card throughput to tail-latency governance. This post dives into Prefill-Decode disaggregation, KV cache transfer, resource planning, and a pre-launch checklist to move LLM serving from 'working' to stable and scalable.
Long contexts and agentic requests shift the LLM inference bottleneck from single-GPU throughput to tail-latency management. This article dives into the principles, trade-offs, KV cache transfer, resource planning, and a go-live checklist for Prefill-Decode disaggregation.
A deep dive into KV Cache memory fragmentation, PagedAttention, continuous batching, and prefix caching — and how they boost throughput and reduce latency in LLM inference.