Tag: vLLM

16 articles

• LLM Tokenizer Preprocessing Pool in Production: Avoiding GPU Starvation with Async Tokenizer Pools, Length-Aware Queues, and CPU Affinity

  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.

  07/22/2026 LLM Serving / Tokenizer / Performance Engineering / CPU Optimization / vLLM

• LLM Inference Reproducibility in Production: Controlling Output Drift with Seed Contracts, Batch Invariance, and Deterministic Kernels

  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.

  07/15/2026 LLM Serving / Reproducibility / vLLM / CUDA / MLOps / Batch Invariance / Deterministic Inference / Seed Contract

• LLM GPU Memory Fragmentation in Production: Taming OOMs with Memory Snapshots, Allocator Tuning, and Admission Control

  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.

  07/11/2026 LLM Serving / CUDA / GPU Memory / PyTorch / vLLM

• LLM CUDA Graph Production Practices: Reducing Decode Launch Overhead with Shape Buckets, Piecewise Capture, and Eager Fallback

  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.

  07/11/2026 LLM Inference / CUDA Graph / GPU Inference / vLLM / Performance Optimization

• LLM LoRA Adapter Cache in Production: Stabilizing Multi-Tenant Fine-Tuning Services with Heat Tiering, Cold-Start Budgets, and Eviction Policies

  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.

  07/09/2026 LLM Serving / LoRA / Adapter Cache / Multi Tenant / Model Serving / GPU / Fine Tuning / RAG / vLLM

• LLM Inference Capacity Planning in Production: Estimating GPU Headroom with Token Throughput Stress Tests and Queue Depth

  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.

  07/05/2026 LLM Serving / Capacity Planning / GPU Inference / SLO / vLLM / Token Throughput

• 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

• 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

• 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

• KV Cache Quantization in Production: Reducing Long-Context Inference Memory with FP8/INT8

  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.

  07/01/2026 LLM / Inference Optimization / KV Cache / Quantization / LLMOps / vLLM / TensorRT-LLM

• Continuous Batching in Production: Keeping LLM Services GPU-Saturated Under High Concurrency

  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.

  07/01/2026 LLM Serving / Continuous Batching / vLLM / KV Cache / Performance Engineering / Inference Optimization / GPU Scheduling

• Speculative Decoding in Production: Reducing LLM Per-User Latency with EAGLE and Draft Models

  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.

  06/30/2026 LLM Inference / Speculative Decoding / vLLM / EAGLE

• KV Cache Reuse in Practice: Prefix Caching, Offloading, and Long-Context Cost Management

  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.

  06/29/2026 LLM Inference / KV Cache / Prefix Caching / vLLM / SGLang

• Speculative Decoding in Production: Why Low QPS Speeds Up but High Load Slows Down

  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.

  06/29/2026 LLM Inference / Speculative Decoding / vLLM / TensorRT-LLM / Performance Optimization

• LLM Quantization in Practice: FP8, INT4, AWQ, and Production Inference Trade-offs

  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.

  06/29/2026 LLM Inference / Quantization / vLLM / TensorRT-LLM / Model Serving

• PagedAttention in Action: Why KV Cache Scheduling Matters More Than Compute for LLM Serving

  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.

  06/29/2026 LLM Inference / PagedAttention / KV Cache / vLLM / LLM Engineering