Tag: Multimodal AI

3 articles

• VLM Image Pipeline in Production: Stabilizing Multimodal Costs with Resolution Tiering, Token Budgeting, and Caching

  A practical engineering guide to designing a production-ready VLM image input pipeline. Covers resolution tiering, token budget estimation, multi-layer caching strategies, and a go-live checklist to control costs, latency, and quality for multimodal AI systems.

  07/08/2026 LLM / VLM / Multimodal AI / Cost Optimization / AI Infrastructure

• LLM Long Video Understanding in Production: Stabilizing QA Quality with Frame Sampling, Timestamp Indexing, and Clip Replay

  This article covers engineering practices for production-grade long video QA, including frame sampling, shot segmentation, subtitle fusion, timestamp indexing, clip replay, evidence verification, and launch checklists to reduce missed keyframes and temporal ordering errors.

  07/05/2026 LLM / video understanding / multimodal AI / production engineering / RAG / frame sampling / timestamp indexing

• 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