Modern Data Center Networks for AI-Driven and High-Growth Environments

PalC helps organizations modernize data center networks to support AI workloads, high-bandwidth applications, and rapid scale using open architectures designed for performance, visibility, and long-term operations.

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Modern Data Center Networks for AI-Driven and High-Growth Environments

Solution Overview

Modernize your data center infrastructure to reliably support AI and machine learning workloads at scale. PalC designs and delivers AI-optimized data center fabrics that enable high-throughput GPU communication, predictable latency, and operational stability across training and inference environments. Our approach focuses on aligning network and storage behavior with AI workload requirements through scalable leaf-spine architectures, RoCE-enabled Ethernet fabrics, lossless transport, high-performance storage access, and built-in telemetry. The result is an open, production-ready infrastructure foundation designed to deliver consistent AI performance, efficient GPU utilization, and long-term operational flexibility.

Planning & Architecture
Understanding business needs, workload behavior, and scale requirements, and translating them into clear network and fabric designs.
Engineering & Build
Engineering open, scalable network fabrics and integrating platforms, hardware, and tooling for deployment.
Commissioning & Validation
Validating networks against real traffic, scale limits, and failure scenarios before production rollout.
Deployment & Operations Support
Deploying validated designs and supporting environments through operations, upgrades, and controlled change.
Future Readiness
This approach ensures data center networks remain reliable, observable, and adaptable as environments evolve.

Key Capabilities

Comprehensive features designed for enterprise-scale infrastructure

Data Center Fabric Architecture

Design of scalable leaf–spine and fabric architectures aligned with workload behavior, growth expectations, and operational constraints.

AI-Aware Network Design

Network designs that account for east–west traffic, high data movement, and performance sensitivity typical of AI training and inference platforms.

Open & Disaggregated Networking

Use of open networking platforms and multi-vendor hardware to avoid lock-in while retaining operational control.

Observability-First Design

Embedding telemetry, monitoring, and diagnostics into fabric design to ensure ongoing visibility and debuggability.

Production Validation & Readiness

Testing architectures against real traffic, scale limits, and failure scenarios before production rollout.

Architecture Overview

A modular, high-performance fabric architecture designed for AI-scale workloads using open, disaggregated networking.

Click a component in the diagram or panel to explore details.

Spine Switch 1100GbE/400GbE spine

Spine Switch 2100GbE/400GbE spine

Leaf Switch 125GbE/100GbE leaf

Leaf Switch 225GbE/100GbE leaf

Leaf Switch 325GbE/100GbE leaf

GPU PodNVIDIA A100/H100 cluster

NVMe-oF ClusterHigh-speed storage fabric

Observability LayerPrometheus, Grafana, ELK

DPU OffloadSmartNIC/DPU acceleration

RoCE PipelineRDMA over Converged Ethernet

Components

SONiC Open Networking

Open-source network operating system for disaggregated infrastructure.

Vendor-agnostic switches with standardized NOS
40–60% cost reduction vs. proprietary
Full control for AI workload customization
Scales without lock-in

Used across spine, leaf, and fabric switches.

Why Choose This Solution

Delivering measurable value through proven technology and expertise

Modernized data center networks enable rapid deployment and scaling of AI workloads without infrastructure bottlenecks.

Well-designed fabrics deliver consistent performance, predictable latency, and reliable behavior under varying load conditions.

Open architectures and observability-first design reduce the operational burden as networks grow and evolve.

Built-in telemetry and monitoring provide real-time insight into network performance, enabling proactive issue resolution.

Open, disaggregated designs provide a foundation that can evolve with changing requirements and new technologies.

Technical Specifications

Network Speeds

• Spine: 400GbE
• Leaf: 100GbE / 200GbE
• Server: 100GbE / 200GbE

Protocols

• RoCEv2
• PFC, ECN, DCQCN
• EVPN-VXLAN (where applicable)

Storage

• NVMe-oF (TCP / RDMA)
• Tiered storage architectures

GPU Support

• NVIDIA A100 / H100 class
• Multi-GPU servers
• NVLink (intra-node), RoCE (inter-node)

Configuration Examples

{
  "PORT_QOS_MAP": {
    "Ethernet0": {
      "pfc_enable": "3,4"
    }
  },
  "BUFFER_POOL": {
    "ingress_lossless_pool": {
      "type": "ingress",
      "size": "139458560",
      "xoff": "20971520"
    }
  }
}

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: nvmeof
provisioner: nvmeof.csi.openebs.io
parameters:
  replicas: "3"
  poolType: "striped"
allowVolumeExpansion: true

Performance Metrics