26 articles
A deep dive into production-grade governance for LLM chat templates, covering template version management, special token handling, precise token counting, tool call rendering, and replay testing to prevent quality regressions and cost overruns during model switches.
Learn how to build a production-grade GPU fault self-healing system for LLM inference. Covers DCGM health checks, Kubernetes four-layer probes, node isolation, request draining, CUDA OOM classification, and canary recovery to reduce inference interruptions and retry storms.
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.
This article covers production governance for LoRA adapter caches in multi-tenant LLM serving, including dynamic loading, hot/cold tiering, eviction strategies, tenant quotas, version releases, and go-live checks to reduce first-token latency and GPU memory jitter.
A systematic guide to managing backpressure, cancellation, and timeouts in LLM streaming outputs. Covers SSE event streams, slow clients, server queues, connection drops, resource cleanup, degradation strategies, and production checklists for stable long-form generation.
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.
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 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 practical guide to building overload protection for LLM inference services using SLO-driven autoscaling and admission control. Covers queue length, token pressure, TTFT/TPOT, and tenant quotas to prevent system collapse under traffic spikes.
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 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 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.
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.