Tag: gpu-inference

11 articles

• Production 2:4 Structured Sparsity for LLMs: Turning Pruning into Real Speedups with Prune Gates, Tactic Audits, and Dense Fallbacks

  A production guide to 2:4 structured sparsity in LLMs—using prune gates, tactic audits, and dense fallbacks to turn pruning into real GPU speedups.

  09/22/2026 LLM Serving / Structured Sparsity / GPU Inference / Model Compression / TensorRT

• LLM Cold Start Optimization in Production: Reducing Model Load Time with Local NVMe Cache, Weight Streaming, and Warm Pools

  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.

  08/28/2026 LLM Serving / Cold Start / KServe / vLLM / GPU Inference

• Multi-Tenant LLM Inference Fair Scheduling in Production: Taming Noisy Neighbors with Token Cost Accounting, VTC, and Priority Lanes

  A practical guide to fair scheduling for shared-GPU multi-tenant LLM inference. Learn why request-rate limits fail, and how token cost accounting, VTC, priority lanes, and tenant quotas balance throughput, isolation, and tail latency.

  08/27/2026 llm-serving / multi-tenancy / fair-scheduling / vtc / gpu-inference

• 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 Inference Reproducibility in Production: Controlling Output Drift with Batch Invariance, Fixed Seeds, and Configuration Fingerprints

  This article dissects why LLM inference can still produce drifting outputs even at zero temperature, and provides production-grade methods including fixed seeds, Batch Invariance, environment fingerprints, replay matrices, and tiered determinism gates to stabilize evaluations, audits, caching, and regression tests.

  07/12/2026 LLM Serving / Reproducibility / Batch Invariance / GPU Inference / MLOps

• LLM MoE Expert Parallel in Production: Stabilizing Inference Throughput with Expert Load Profiling, All-to-All Budgeting, and Dynamic Replicas

  MoE's sparse activation doesn't guarantee efficient inference. This article breaks down Expert Parallel's All-to-All communication, expert hotspots, and dynamic replica mechanisms, offering a complete production governance strategy from load profiling to topology placement and capacity gating.

  07/11/2026 LLM Serving / Mixture of Experts / Expert Parallel / GPU Inference / Distributed Systems

• 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 Serving Autoscaling in Production: Safe Scaling with Queue Depth, P95 Latency, and GPU Utilization

  A deep dive into production-grade autoscaling for LLM serving. Build a multi-layer control loop using queue depth, P95 latency, token throughput, and GPU utilization, with warm pools, admission control, cost boundaries, and cooldown strategies to meet inference SLOs under cost constraints.

  07/09/2026 LLM Serving / Autoscaling / GPU Inference / P95 Latency / SLO / Scaling

• LLM Tensor Parallel Inference Deployment: Reducing Cross-GPU Jitter with Parallelism, NCCL, and Topology Awareness

  A practical guide to Tensor Parallel, Pipeline Parallel, NCCL communication, and topology-aware tuning for large model cross-GPU inference deployment. Covers parallelism selection, node networking, stress testing metrics, and pre-launch checks to help teams reduce multi-GPU service jitter.

  07/07/2026 LLM Serving / Tensor Parallel / NCCL / GPU Inference

• 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

• 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