The Fabric for GPU Performance: Optimizing Data Flow for AI, HPC, and Edge Workloads

Maximize your GPU clusters’ potential with our high-speed networking solutions—engineered to eliminate bottlenecks

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Network Solutions for GPU Workloads

Accelerate data pipelines between GPUs, storage, and edge devices with ultra-low-latency architectures

GPU Server

Micas Networks 400G 128P Tomahawk 5 Ethernet Switch

Its high-density 400GbE ports and RoCEv2 support enable seamless connectivity for large GPU clusters, making it a robust solution for AI-driven data centers.

CPU

MEM

HD

PCIE

see detailed specification
GPU Server

Micas Networks M2-W6920-4S

The M2-W6920-4S is a high-density, 1U switch optimized for AI/ML applications, offering 12.8 Tbps bandwidth and 400GbE/100GbE connectivity.

CPU

MEM

HD

PCIE

see detailed specification
GPU Server

Micas Networks 51.2T Co-Packaged Optics

By integrating optics with the switch chip, it overcomes copper interconnect limitations, delivering high bandwidth and low latency for AI/ML workloads.

CPU

MEM

HD

PCIE

see detailed specification
GPU Server

Micas Networks M2-W6940-64OC

This high-density 800G Ethernet switch is designed to meet the demands of AI/ML workloads, offering ultra-high performance, low latency, and scalability for large-scale data centers.

CPU

MEM

HD

PCIE

see detailed specification

Powering Next-Gen GPU Deployments

Enable seamless scaling for AI training, real-time inference, and distributed computing.

AI/ML Cluster Optimization

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RDMA over Fabrics: Reduce GPU-to-GPU latency by 80% vs. traditional TCP/IP.
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Adaptive Routing: Prevent congestion in 10,000+ GPU clusters.
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Zero-Touch Provisioning: Deploy GPU network fabrics in under 15 minutes.

Edge AI Orchestration

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5G GPU Offload: Process 1,000+ video streams with less 2ms edge-to-cloud latency.
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Deterministic QoS: Guarantee bandwidth for critical GPU workloads.
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Secure Segmentation: Isolate GPU traffic across multi-tenant environments.

Expert recommendations for Your Workflow

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Reduce AI training time by up to 40%
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Cut rendering costs by 30%
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Scale compute resources on demand"

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