Compute is the total processing resource used to train or run an AI system. It may be described through chip hours, floating-point operations, accelerator counts, memory capacity, energy consumption, or cloud cost. Training a large model can require many processors working together for weeks, while inference compute is spent each time the deployed model handles a request. More compute can enable larger experiments and models, but results also depend on algorithms, data quality, hardware utilization, and software efficiency. Because compute affects cost, latency, energy use, and access to advanced AI, it is both a technical design constraint and an important economic factor.
Microsoft has committed $33 billion to neocloud partnerships, including a $19.4B deal with Nebius, to secure GPU capacity and avoid looming AI infrastructure bottlenecks.