Modern networking platform built for Distributed AI

KEY FEATURES

Distributed AI at the Edge

ACEAI brings powerful distributed AI to the network edge, enabling real-time insights, faster decisions, and secure, scalable AI workloads across data center, cloud, and edge environments.

Distributed Model Training

ACEAI supports distributed model training by enabling AI and ML models to run across multiple network nodes simultaneously. This dramatically increases computational efficiency and performance for large, complex models that require massive data processing.

Federated Learning Across Devices

With federated learning, ACEAI trains AI models on data spread across diverse devices like smartphones, tablets, and wearables. This approach enhances privacy, minimizes data movement, and leverages the network’s distributed nature for seamless, secure training.

Edge Inferencing for Low Latency

ACEAI brings inferencing closer to the end user by deploying AI models at the network edge. By processing data locally instead of routing it back to a central cloud, it reduces latency, boosts application responsiveness, and optimizes real-time decision-making.

Resilient, High-Performance Fabric

To support distributed AI, ACEAI delivers a robust networking foundation with high throughput, predictable low latency, and zero-impact failover. Its fabric-wide visibility and lossless connectivity ensure reliable AI operations, even at hyperscale.

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