GenAI 모델 트레이닝 너머 : 실전 환경에서 AI 추론 워크로드의 비용 및 레이턴시의 삭감과 확장성의 향상

The IDC Perspective explores the challenges and innovations in scaling generative AI (GenAI) inference workloads in production, emphasizing cost reduction, latency improvement, and scalability. It highlights techniques like model compression, batching, caching, and parallelization to optimize inference performance. Vendors such as AWS, DeepSeek, Google, IBM, Microsoft, NVIDIA, Red Hat, Snowflake, and WRITER are driving advancements to enhance GenAI inference efficiency and sustainability. The document advises organizations to align inference strategies with use cases, regularly review costs, and partner with experts to ensure reliable, scalable AI deployment."Optimizing AI inference isn't just about speed," says Kathy Lange, research director, AI Software, IDC. "It's about engineering the trade-offs between cost, scalability, and sustainability to unlock the potential of generative AI in production, where innovation meets business impact."

Executive Snapshot

Situation Overview

• What Is AI Inference, and Why Is It Important?
• Growing Demand for Efficient AI Inference
  • The GenAI Inference Infrastructure Stack
  • Factors That Influence GenAI Inference Performance
    • Model Compression Techniques
    • Data Batching Techniques
    • Caching and Memorization Techniques
    • Efficient Data Loading and Preprocessing
    • Reducing Input and Output Sizes
      • Parallelization
      • Model Routing
      • Which Software Platform Optimization Techniques Are Considered Most Effective?
      • Test-Time Compute (aka Inference-Time Compute)
      • An Emerging Field of Research
  • Technology Supplier Innovation

Advice for the Technology Buyer

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• Related Research
• Synopsis