Why AI Workloads Need Composable Infrastructure

AI training and inference have wildly different resource profiles. A large-scale training run needs hundreds of tightly coupled GPUs with maximally available high-bandwidth memory for days at a stretch. An inference service needs a handful of GPUs with low-latency scheduling, spinning up and down with traffic.

Fixed infrastructure forces you to choose: provision for training and leave GPUs idle during inference lulls, or provision for inference and queue training jobs for days.

Composable infrastructure breaks that tradeoff. The physical GPU pool is shared; logical clusters are assembled on demand. A training job claims 256 GPUs at midnight, releases them at noon, and the inference cluster auto-scales into the freed capacity.

Beyond raw GPU count, composability extends to memory, networking, and storage. A fine-tuning job with a 70B parameter model needs 1.4 TB of GPU memory — today that means booking a specific node class. With composable memory fabric, that capacity is assembled from the pool and released when the job finishes.

The scheduling layer is the key enabler. Cerio.ai's scheduler tracks real-time availability across the pool, enforces QoS guarantees for latency-sensitive workloads, and preempts lower-priority jobs gracefully — checkpointing state before reclaiming resources. The result is a cluster that looks dedicated to each team while behaving as one shared, efficient system.