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Beyond GenAI Model Training: Reducing Cost and Latency and Improving Scalability of AI Inferencing Workloads in Production

Publisher IDC
Published Sep 19, 2025
Length 18 Pages
SKU # IDC20411691

Description

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."

Table of Contents

18 Pages

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

Learn More

Related Research

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