AMD Pensando Pollara 400 AI NIC

The AMD Pensando POLLARA-400 AI NIC delivers efficient scale-out networking for enterprise data centres running distributed AI workloads. With Ethernet, RDMA and RoCEv2 support, it helps keep GPU-to-GPU communication predictable and accelerators productive across demanding clusters.

AMD Pensando Pollara 400 AI NIC
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AMD Pensando Pollara 400 AI NIC Overview

As distributed GPU workloads grow, Ethernet fabrics must keep pace with low-latency data movement, predictable congestion handling, and clear operational visibility.

Scale-Out Networking for AI Infrastructure

AMD Pensando Pollara 400 AI NIC is built for production AI environments that depend on fast, efficient communication between GPU servers. It fits scale-out training and inference clusters where 400G throughput, RoCE, and GPUDirect support matter more than a general-purpose server NIC.

Designed for Dense, Connected Clusters

With a PCIe Gen5 x16 host interface, the platform is aligned to high-bandwidth server deployments. Its programmable design and hardware-assisted transport features support Ethernet fabrics that need to move large east-west traffic flows with less tuning overhead.

Operational Outcomes That Matter

The result is more consistent GPU-to-GPU communication, fewer network stalls, and better utilisation of expensive accelerator capacity. It also gives infrastructure teams a practical way to grow AI clusters on familiar Ethernet foundations while keeping troubleshooting and fabric management more manageable.

Our team can help you assess AMD Pensando Pollara 400 AI NIC for your environment and align it to your cluster and networking requirements.

AMD Pensando Pollara 400 AI NIC Key Features​

AMD Pensando Pollara 400 AI NIC is a networking platform for AI infrastructure deployments, designed to support high-throughput data movement and host connectivity in demanding enterprise environments.

AI Data Path Acceleration

High-Throughput Fabric Offload
The platform is built to support accelerated network data handling for AI workloads and other traffic-intensive deployments.

400G Port Capability
Maximum port speed reaches 400G to align the NIC with high-bandwidth infrastructure requirements.

PCIe Gen5 Host Integration
PCIe Gen5 x16 host interface provides a high-bandwidth attachment path to the server platform.

GPU-Adjacent Connectivity
GPUDirect support helps align the NIC with GPU-centric data flows in AI systems.

Operational Control For AI Networks

Infrastructure-Ready Deployment
The NIC fits into enterprise AI environments where network performance and host integration must be maintained together.

Platform-Level Consistency
Its architecture is intended to deliver predictable connectivity behavior across deployed systems.

Workload-Aligned Networking
The design supports operational efficiency where AI traffic patterns demand close coordination between servers and accelerators.

Speak to an AMD Networking Specialist

When AI clusters need higher bandwidth and tighter GPU connectivity, our team can help you evaluate how the Pollara 400 fits your host and fabric design.

AMD Pensando Pollara 400 AI NIC Technical Specifications

Full specifications for this model are listed below.

Additional information

Product Family

AMD Pensando
Product Series

Pollara 400
Deployment

Data Centre
Target Organisation Size

Hyperscaler
Primary Use Case

AI Scale-Out Networking
Product Tier

Flagship
PCIe Generation

Gen5
Form Factor

HHHL / OCP 3.0 TSFF
Device Type

AI NIC
Host Interface

PCIe Gen5.0 x16
Network Protocols

Ethernet / RoCEv2 / UEC-ready RDMA
Max Network Ports

4
Maximum Port Speed

400G
RDMA Support

Yes
RoCE Support

Yes
Security Features

Inline Encryption / Decryption
Virtualisation Support

SR-IOV

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AMD Pensando Pollara 400 AI NIC Deployment Scenarios

The AMD Pensando Pollara 400 AI NIC is built for backend networking in distributed AI and analytics environments where GPU servers depend on predictable Ethernet performance. It helps keep accelerator traffic moving in clusters that are already running at scale.

Large-Scale GPU Training Clusters

In data centres running 400GbE AI clusters, the pressure is on collective traffic between GPUs, not server access. Pollara 400 helps reduce idle accelerator time by keeping east-west communication predictable as cluster size and utilisation rise.

Distributed Model Development and Test Environments

Software teams running multi-node training need consistent RDMA performance and less time spent chasing congestion issues. Pollara 400 supports open Ethernet-based development clusters where predictable data movement and easier troubleshooting matter more than constant manual tuning.

AI-Driven Network Operations Platforms

Telecom operators deploying AI for network automation need fast, observable backend connectivity inside central model clusters. Pollara 400 fits environments that must handle heavy east-west traffic on familiar Ethernet while keeping performance stable as workflows evolve.

Render Farms and Generative Media Pipelines

Media and entertainment teams moving large model, frame and asset sets between GPU servers need the network to stay out of the critical path. Pollara 400 helps keep multi-server rendering and generative jobs flowing without avoidable transfer delays.

Distributed AI and Analytics in Financial Services

Finance teams running shared AI and analytics clusters need low-latency communication, strong visibility and resilience around costly compute. Pollara 400 supports those operational demands by reducing network stalls that can waste accelerator capacity and disrupt time-sensitive workloads.

Planning an AMD Pensando Pollara 400 AI NIC Deployment?

Our team can help design and deploy AMD Pensando Pollara 400 AI NIC environments for GPU clusters, distributed training, telecom automation, media pipelines and finance workloads with the right balance of performance, visibility and operational control.

Tailored recommendations for your infrastructure

Below you’ll find alternative models, suitable software and services that pair with this solution – helping you to avoid compatibility issues, reduce support overhead and deploy with confidence.

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