The Cerio Platform
Architecture built for disaggregated compute.
Four layers, one coherent platform. Cerio.ai abstracts physical hardware into a composable resource pool your workloads can consume on-demand.
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Architecture Overview
From physical nodes to composable resources
Stack Architecture
Four layers. One control plane.
Compute Layer
Cerio agents run directly on compute nodes, presenting all GPU, CPU, and memory resources as a unified pool. Physical topology becomes irrelevant to workload placement.
Composable Fabric
A high-bandwidth, low-latency software fabric connects disaggregated resources. Cerio's proprietary protocol delivers sub-millisecond latency — indistinguishable from local attach for most workloads.
Scheduler & Orchestrator
ML-driven workload placement engine continuously optimizes resource allocation. Considers latency constraints, utilization targets, power budgets, and SLA requirements simultaneously.
Control Plane & API
A unified REST and gRPC API surface exposes the full composable infrastructure. Integrates with Kubernetes, Slurm, and custom orchestrators via standard interfaces.
Platform Capabilities
Granular control over every resource.
GPU Partitioning
Slice a single GPU into multiple logical instances. Each workload sees a dedicated GPU slice with hardware-enforced isolation.
Memory Pooling
Aggregate GPU HBM across nodes into a unified memory namespace. Large models that exceed a single GPU's VRAM run natively.
Dynamic Reallocation
Reclaim idle GPU capacity from finished jobs and immediately assign it to waiting workloads. No cold-start, no manual intervention.
Topology-Aware Placement
The scheduler understands NVLink, InfiniBand, and PCIe topology. Communication-intensive jobs are co-located to minimize inter-node traffic.
Composability
Infrastructure that reconfigures in real time.
Static clusters are provisioned for peak demand. Cerio.ai eliminates that waste by dynamically composing exactly the resources each workload needs — and releasing them when it's done.
A morning AI training run uses 64 GPUs. By afternoon, the same physical hardware serves 200 concurrent inference endpoints. No reprovisioning. No downtime.
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Get a technical deep-dive tailored to your environment, workloads, and scale requirements.
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