Articles

Searchable, citable, durable content on LLMs and AI agents. 128 articles, page 3 / 13.

• LLM Real-Time Voice Input in Production: Low-Latency Recognition with VAD Segmentation, Partial/Final Reconciliation, and Audio Contracts

  Real-time voice applications face challenges beyond model accuracy. This article systematically breaks down how to build an observable, rollback-capable low-latency speech recognition pipeline—from audio format contracts and VAD segmentation strategies to Partial/Final state reconciliation, timestamp replay, and real-world noise evaluation. Designed for LLM, generative AI, and real-time Agent practitioners.

  07/13/2026 LLM / real-time voice / speech recognition / VAD / production engineering / Agent / RAG

• 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 Confidential Inference in Production: GPU Remote Attestation, Key Release on Proof, and Evidence Chain for Sensitive Data

  This article provides a production architecture deep dive into how LLM confidential inference uses CPU/GPU Trusted Execution Environments, remote attestation, key release on proof, and evidence retention to protect sensitive prompts, model weights, and intermediate inference states. It also covers compatibility boundaries and pre-deployment checklists.

  07/12/2026 LLM Security / Confidential Computing / GPU Attestation / Remote Attestation / Key Management

• LLM GPU Multi-Tenant Isolation in Production: Sharing Inference Stably with MIG Profiles, Time-Slicing Layering, and Reconfiguration Gates

  A systematic guide to MIG, Time-Slicing, and MPS isolation differences for LLM inference on shared GPUs. Covers profile selection, Kubernetes scheduling, reconfiguration gates, monitoring, and rollback to build a verifiable production plan balancing utilization, latency, and fault boundaries.

  07/12/2026 LLM Serving / NVIDIA MIG / GPU Multi-Tenancy / Kubernetes / Inference Infrastructure

• LLM CPU–GPU Data Path in Production: Reducing Inference Jitter with NUMA Affinity, Pinned Memory, and Async Copy

  A systematic guide to optimizing the CPU-to-GPU data path for LLM inference, covering NUMA affinity, pinned memory, async copy, topology scheduling, and container alignment to reduce host-side transfer jitter, tail latency, and cross-node resource misconfiguration.

  07/12/2026 LLM Serving / CUDA / NUMA / GPU Performance / PyTorch

• LLM Attention Backend in Production: Managing FlashAttention and FlashInfer with Compatibility Matrices, Numerical Replay, and Automatic Fallback

  LLM inference frameworks select kernels among FlashAttention, FlashInfer, and SDPA. This article provides a compatibility matrix, numerical replay, performance baselines, and automatic fallback strategies to prevent startup failures, output drift, and tail latency degradation after upgrades.

  07/12/2026 LLM Serving / FlashAttention / FlashInfer / GPU Kernel / Inference Engineering

• 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 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 Tokenizer Version Governance in Production: Preventing Cost Surprises with Compatibility Fingerprints, Token Drift Replay, and Budget Gates

  Tokenizer changes can silently shift token counts, truncation boundaries, and costs for the same prompt. This article provides practical solutions including compatibility fingerprints, golden corpus replay, dual counting, and budget gates to help teams identify risks before model upgrades and safely roll out changes.

  07/11/2026 LLM Engineering / Tokenizer / Model Migration / Cost Governance