Powering Scale-out AI Infrastructure
The AMD Pensando™ Pollara 400 AI NIC is engineered to accelerate applications running across back-end networks, achieving up to 400 Gigabit per second (Gbps) Ethernet speeds.
Built on the proven third generation, fully hardware programmable Pensando P4 engine, the AMD Pensando Pollara 400 AI NIC delivers industry-leading performance with the flexibility to be programmed to meet future requirements, helping to maximize infrastructure investments for enterprises, cloud service providers, and researchers.
Industry’s First AI NIC Offering Ultra Ethernet Consortium (UEC) Features
The AMD Pensando™ Pollara 400 AI NIC is the industry's first Ultra Ethernet Consortium (UEC) compatible AI NIC. With its programmability, the AMD AI NIC™ enables customers to select UEC features to bring intelligence to network monitoring and performance tuning. Through the fully programmable P4 engine, the AMD AI NIC allows customers to upgrade any Pensando Pollara 400 AI NIC to meet new industry standards, including those established by the UEC.
Accelerate AI Performance at Scale
AI Workload Performance
With 400 Gbps GPU-GPU communication speeds, the AMD Pensando™ Pollara 400 AI NIC can accelerate job completion times while training the largest AI models, deploying the next Gen AI model, or researching cutting-edge advancements with networking designed to accelerate AI workloads.
Lower Capex
Designed to meet the needs of AI workloads today and tomorrow, the AMD Pensando™ Pollara 400 AI NIC is compatible with an open ecosystem, allowing customers to lower capex, while remaining flexible to future infrastructure scalability.
Intelligent Network Monitoring
Save time on traditional network monitoring and performance tuning tasks. The AMD Pensando™ Pollara 400 AI NIC load balances networks while monitoring network metrics, allowing teams to proactively identify and address potential network issues before they escalate into critical disruptions.
Intelligent Network Monitoring and Load Balancing
- Intelligent Packet Spray
- Out-of-order Packet Handling and In-order Message Delivery
- Selective Retransmission
- Path-Aware Congestion Control
- Rapid Fault Detection
Intelligent Packet Spray
Intelligent packet spray enables teams to seamlessly optimize network performance by enhancing load balancing, boosting overall efficiency, and scalability. Improved network performance can significantly reduce GPU-to-GPU communication times, leading to faster job completion and greater operational efficiency.
Out-of-order Packet Handling and In-order Message Delivery
Ensure messages are delivered in the correct order, even when employing multipathing and packet spraying techniques. The advanced out-of-order message delivery feature efficiently processes data packets that may arrive out of sequence, seamlessly placing them directly into GPU memory without the need for buffering.
Selective Retransmission
Boost network performance with selective acknowledgment (SACK) retransmission, which ensures only dropped or corrupted packets are retransmitted. SACK efficiently detects and resends lost or damaged packets, optimizing bandwidth utilization, helping reduce latency during packet loss recovery, and minimize redundant data transmission for exceptional efficiency.
Path-Aware Congestion Control
Focus on workloads, not network monitoring, with real-time telemetry and network-aware algorithms. The path-aware congestion control feature simplifies network performance management, enabling teams to quickly detect and address critical issues while mitigating the impact of incast scenarios.
Rapid Fault Detection
With rapid fault detection, teams can pinpoint issues within milliseconds, enabling near-instantaneous failover recovery and significantly reducing GPU downtime. Tap into elevated network observability with near real-time latency metrics, congestion and drop statistics.
Boost AI Performance and Network Reliability
Up to
15%
Faster AI Job Performance
1
Enhance runtime performance by approx. 15% for certain applications. With features including intelligent network load balancing, fast failover and loss recovery, the AMD Pensando Pollara 400 AI NIC helps accelerate workloads while maximizing AI investments.
Up to
10%
Improved Network Reliability
2
Gain up to 10% improved network uptime. With the AMD Pensando Pollara 400 AI NIC, minimize cluster down-time while increasing network resilience and availability with state-of-the-art RAS and fast failure recovery.
AMD Pensando™ 400 AI NIC Specifications
| Maximum Bandwidth | Form Factor | Ethernet Interface | Ethernet Speeds | Ethernet Configurations | Management |
| 400 Gbps | Half-height, half-length | PCIe® Gen5.0x16 | 25/50/100/200/400 Gbps | Supports up to 4 ports |
MCTP over SMBus |
Explore the full suite of AMD networking solutions designed for high-performance modern data centers.
Resources
Unlock the Future of AI Networking
Learn how the AMD Pensando Pollara 400 AI NIC can transform your scale-out AI Infrastructure.
Footnotes
- Dong, Jianbo & Luo, Bin & Zhang, Jun & Zhang, Pengcheng & Feng, Fei & Zhu, Yikai & Liu, Ang & Chen, Zian & Shi, Yi & Jiao, Hairong & Lu, Gang & Guan, Yu & Zhai, Ennan & Xiao, Wencong & Zhao, Hanyu & Yuan, Man & Yang, Siran & Li, Xiang & Wang, Jiamang & Fu, Binzhang. (2024). Boosting Large-scale Parallel Training Efficiency with C4: A Communication-Driven Approach. 10.48550/arXiv.2406.04594. Boosting Large-scale Parallel Training Efficiency with C4: A Communication-Drive Approach https://arxiv.org/pdf/2406.04594. Claim reflects technology used in AMD Pensando Pollara 400 NICs, however testing and data not specific to the Pollara 400. Results may vary.
- Dubey, Abhimanyu & Jauhri, Abhinav & Pandey, Abhinav & Kadian, Abhishek & Al-Dahle, Ahmad & Letman, Aiesha & Mathur, Akhil & Schelten, Alan & Yang, Amy & Fan, Angela & Goyal, Anirudh & Hartshorn, Anthony & Yang, Aobo & Mitra, Archi & Sravankumar, Archie & Korenev, Artem & Hinsvark, Arthur & Rao, Arun & Zhang, Aston & Zhao, Zhiwei. (2024). The Llama 3 Herd of Models. 10.48550/arXiv.2407.21783. Meta Research Paper, “The Llama 3 Herd of Models, Table 5. Claim reflects technology used in AMD Pensando Pollara 400 NICs, however testing and data not specific to the Pollara 400. Results may vary.
Footnotes
- Dong, Jianbo & Luo, Bin & Zhang, Jun & Zhang, Pengcheng & Feng, Fei & Zhu, Yikai & Liu, Ang & Chen, Zian & Shi, Yi & Jiao, Hairong & Lu, Gang & Guan, Yu & Zhai, Ennan & Xiao, Wencong & Zhao, Hanyu & Yuan, Man & Yang, Siran & Li, Xiang & Wang, Jiamang & Fu, Binzhang. (2024). Boosting Large-scale Parallel Training Efficiency with C4: A Communication-Driven Approach. 10.48550/arXiv.2406.04594. Boosting Large-scale Parallel Training Efficiency with C4: A Communication-Drive Approach https://arxiv.org/pdf/2406.04594. Claim reflects technology used in AMD Pensando Pollara 400 NICs, however testing and data not specific to the Pollara 400. Results may vary.
- Dubey, Abhimanyu & Jauhri, Abhinav & Pandey, Abhinav & Kadian, Abhishek & Al-Dahle, Ahmad & Letman, Aiesha & Mathur, Akhil & Schelten, Alan & Yang, Amy & Fan, Angela & Goyal, Anirudh & Hartshorn, Anthony & Yang, Aobo & Mitra, Archi & Sravankumar, Archie & Korenev, Artem & Hinsvark, Arthur & Rao, Arun & Zhang, Aston & Zhao, Zhiwei. (2024). The Llama 3 Herd of Models. 10.48550/arXiv.2407.21783. Meta Research Paper, “The Llama 3 Herd of Models, Table 5. Claim reflects technology used in AMD Pensando Pollara 400 NICs, however testing and data not specific to the Pollara 400. Results may vary.