Training AI Is The Easy Part

2026-02-28 · Source: No Priors: AI, Machine Learning, Tech, & Startups · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

The complexity of AI inference workloads is significantly underestimated compared to training, presenting unique challenges for businesses. While training often utilizes GPUs at 100% capacity, inference demand is highly variable, leading to issues with latency, resource fungibility, and cost optimization. A critical observation is that inference is primarily a memory throughput problem, particularly concerning the "prefill" and "decode" phases. Optimizing these phases across a fleet of GPUs represents a distinct technical challenge that requires specialized solutions beyond those used for model training.

Key takeaway

For Machine Learning Engineers managing production AI systems, recognize that inference optimization demands a different approach than training. Focus on addressing memory throughput issues during prefill and decode phases, and implement strategies to handle highly variable demand efficiently to control costs and maintain latency targets.

Key insights

AI inference is more complex than training, driven by variable demand and memory throughput challenges.

Principles

• Inference demand is highly variable, unlike training.
• Inference is fundamentally a memory throughput problem.

In practice

• Optimize prefill and decode phases across GPU fleets.
• Manage variable inference demand for cost efficiency.

Topics

• AI Inference
• Inference Optimization
• Memory Throughput
• Latency Management
• GPU Workloads

Best for: Machine Learning Engineer, NLP Engineer, MLOps Engineer, AI Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by No Priors: AI, Machine Learning, Tech, & Startups.