Tag: LLM Inference

10 articles

• Multi-LoRA Adapter Caching in Production: Reducing Cold-Start Tail Latency with GPU/CPU Tiering, LRU Eviction, and LoRA Affinity

  A practical guide for multi-tenant LLM inference platforms covering Multi-LoRA adapter residency in GPU/CPU tiered caches, LRU eviction, cold-load latency, affinity routing, capacity planning, monitoring, release governance, and launch checklists.

  08/27/2026 LLM Inference / Multi-LoRA / LoRA Cache / GPU Inference / Affinity Routing / Adapter Management

• LLM RoPE Scaling in Production: Preventing Long-Context Degradation with Config Fingerprints, Length Buckets, and Dual-Track Evaluation

  A systematic guide to safely deploying long-context LLMs with RoPE scaling. Covers config fingerprints, length buckets, dual-track evaluation, and graceful rollback to prevent context degradation in production.

  07/13/2026 LLM Inference / Long Context / RoPE / Model Evaluation / Production Engineering / Positional Encoding

• LLM Weight Quantization in Production: Ensuring Quality and Throughput with Calibration Fingerprints, Kernel Compatibility Matrices, and Dual-Track Replay

  A deep dive into the complete pipeline for deploying LLM weight quantization from experimentation to production, covering AWQ/GPTQ algorithm comparisons, calibration set fingerprinting, a four-layer compatibility model, kernel compatibility matrices, dual-track quality and throughput replay, and canary release/rollback strategies.

  07/12/2026 LLM Inference / Model Quantization / AWQ / GPTQ / GPU Serving / Weight Quantization

• 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

• Edge LLM Inference in Production: Running Local Models Reliably with GGUF, mmap, and CPU/GPU Offloading

  A systematic guide to deploying open-weight LLMs on local workstations, edge nodes, and internal servers. Covers GGUF quantization selection, mmap memory-mapped loading, CPU/GPU layer offloading tuning, service governance, and a production launch checklist to elevate edge inference from 'it runs' to 'operationally sound'.

  07/06/2026 LLM Inference / GGUF / Edge AI / llama.cpp / Model Deployment / Quantization / Local Inference

• 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