17 articles
An in-depth analysis of the six-stage bottleneck of LLM cold starts in serverless GPU scenarios, covering warm pool tiering strategies, staged pre-warming mechanisms, engineering architecture, and metrics to help teams find a verifiable balance between GPU cost and first-token latency.
A systematic guide on how Continuous Batching improves LLM inference throughput via iteration-level scheduling, covering static batching pain points, scheduling mechanics, KV Cache constraints, key parameters, monitoring metrics, and deployment pitfalls.
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 Speculative Decoding's Draft-Verify mechanism, covering vLLM and TensorRT-LLM integration, metric tuning, applicability boundaries, and production deployment checks to reduce decode latency and avoid overhead from low acceptance rates.
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 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 systematic guide to Speculative Decoding for LLM inference: principles, engineering trade-offs, model selection, monitoring metrics, and common pitfalls. Learn when and how to deploy draft models for latency reduction.
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.
From fixed batching and iteration-level scheduling to PagedAttention, this article systematically explains the principles, benefits, trade-offs, and production deployment checklist for Continuous Batching in LLM inference serving.
A practical guide to Expert Parallelism for MoE inference: communication overhead, load balancing, parallel strategy combinations, and a pre-deployment checklist for production.
A practical guide to Multi-LoRA Serving: how to share one base model across multiple LoRA adapters for cost-effective, multi-tenant LLM inference.
A deep dive into how Chunked Prefill and Continuous Batching work together to manage latency in long-context LLM inference, covering scheduling principles, chunk size tuning, production metrics, and a deployment checklist.
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.