Articles

Searchable, citable, durable content on LLMs and AI agents. 147 articles, page 11 / 15.

• VLM Serving in Production: Stabilizing Image & Video Inference with Vision Token Budgets

  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.

  07/02/2026 VLM / LLM Serving / Multimodal AI / Vision Token Budget / Multimodal Inference / Inference / Vision Token / vLLM / SGLang

• LLM Gateway in Production: Governing Multi-Tenancy, Budgets, and Model Access with a Policy Engine

  A practical guide to building an LLM Gateway policy engine for multi-tenant model access control, budget limits, rate limiting, audit logging, and canary releases. Learn how to transform scattered model calls into a controllable, observable, and auditable unified entry point.

  07/02/2026 LLM Gateway / Policy Engine / Multi-Tenancy / Rate Limiting / Budget Governance / Model Access Control / AI Infrastructure

• Serverless GPU Inference Cold Start Mitigation: Reducing First-Token Latency with Warm Pools and Staged Pre-Warming

  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.

  07/01/2026 LLM Serving / Serverless GPU / Cold Start / Warm Pool / Inference Optimization / GPU Inference / Cold Start Optimization

• Multi-LoRA Production Practices: Stable Multi-Tenant Inference with Adapter Caching and Hot/Cold Tiering

  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.

  07/01/2026 LLM Serving / LoRA / Inference / GPU / Multi Tenant / Adapter Cache / vLLM

• LLM Serving Overload Protection: SLO-Driven Autoscaling and Admission Control

  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.

  07/01/2026 LLM Serving / Autoscaling / SLO / Admission Control / Overload Protection / Inference Infrastructure

• Continuous Batching in Production: Boosting LLM Serving Throughput with Dynamic Batching

  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.

  07/01/2026 LLM Serving / Continuous Batching / Inference Optimization / GPU / vLLM

• Continuous Batching in Production: Boosting LLM Serving Throughput with Dynamic Batching

  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.

  07/01/2026 LLM Serving / Continuous Batching / Inference Optimization / KV Cache / MLOps

• Structured Outputs in Production: Using Constrained Decoding for Reliable JSON from LLMs

  A systematic guide to structured outputs in LLM applications: JSON Schema, constrained decoding, business validation, fallback strategies, and monitoring metrics to reduce parse failures and improve API stability.

  07/01/2026 LLM / Structured Outputs / Constrained Decoding / JSON Schema / Engineering Practices

• PagedAttention in Production: Boosting LLM Serving Throughput with Paged KV Cache

  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.

  07/01/2026 LLM / LLM Serving / PagedAttention / KV Cache / Inference Optimization

• Prefix Caching in Production: Reducing TTFT with KV Cache Reuse

  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.

  07/01/2026 LLM / Inference Optimization / KV Cache / Prefix Caching / Engineering Practices