20 articles
CUDA Graphs cut LLM inference launch overhead, but dynamic batching, shape variance, and first-capture costs create latency jitter. This guide covers shape buckets, piecewise capture, warmup strategies, memory trade-offs, and production rollback gates using vLLM and TensorRT-LLM to fix host-side bottlenecks when GPUs aren't saturated but latency won't drop.
MoE deployments often shift bottlenecks from compute to expert skew and All-to-All communication. This guide covers Expert Parallel, EPLB, and redundant experts with vLLM, TensorRT-LLM, and DeepEP, plus phased rollout, observability metrics, and a launch checklist.
Learn how to reduce LLM cold start latency in production using local NVMe model caches, weight streaming, warm pools, and model-locality-aware scheduling. A practical engineering guide for KServe, vLLM, and GPU inference clusters.
A production-grade guide for LLM inference services: using queue wait time, TTFT SLO, backpressure, and elastic autoscaling to handle traffic spikes, avoid scaling lag, tail latency blowups, and unbounded queuing. Includes key metrics, configuration, launch checklist, and common pitfalls.
The bottleneck in LLM serving isn't always the GPU. This article explains how to decouple the tokenizer preprocessing pool using async batch encoding, length-aware queues, CPU affinity, and backpressure metrics to prevent long prompts from blocking requests and keep the GPU fed with executable batches.
Same model, prompt, and seed can still yield different outputs. This article covers sampling state, dynamic batching, floating-point reduction, parallel topology, and version fingerprints to provide tiered reproducibility goals, implementation strategies, replay gates, and a deployment checklist.
LLM inference services often hit OOM due to fragmentation, not total capacity exhaustion. This guide covers Memory Snapshots, dynamic shapes, allocator tuning, and vLLM memory budgeting for observable, verifiable, and rollback-safe production management.
CPU kernel launch overhead in LLM decode is often overlooked. This deep dive explains CUDA Graph capture and replay, combined with shape bucket design, piecewise capture, warmup state machines, and eager fallback, delivering a production-ready solution to reduce tail latency and P99 jitter.
Production strategies for LoRA adapter cache management in multi-tenant fine-tuning services: heat tiering, cold-start budgets, eviction policies, and a governance framework for stable model serving.
A practical guide to transforming LLM inference capacity from reactive scaling to measurable, estimable, and alertable capacity engineering. Covers token throughput, queue depth, P99 TTFT, GPU headroom, and a pre-launch checklist to help teams identify bottlenecks and make quantitative scaling decisions.
A systematic guide to production VLM serving: image/video input governance, vision token budgets, preprocessing queues, cost control, and a go-live checklist for stable multimodal inference.
A systematic guide to Multi-LoRA serving in production: adapter caching, hot/cold tiering, rank-aware scheduling, eviction strategies, and deployment checks to stably serve hundreds of custom LoRA adapters under GPU memory constraints.
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 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 practical guide to Speculative Decoding for LLM inference: draft generation, parallel verification, acceptance length, and production metrics. Compares Draft Model, Medusa, and EAGLE with rollout and rollback checklists.
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.
A production-oriented analysis of speculative decoding's benefit boundaries, covering draft models, verification, acceptance rate, request load, and batching, with a pre-deployment checklist.
A systematic guide to LLM inference quantization: FP8, INT4, AWQ, GPTQ, KV Cache quantization, benefits, risks, selection methods, and a go-live checklist for production engineers.
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.