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AI Infrastructure & Hardware Performance Benchmarking

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AI Infrastructure & Hardware Performance Benchmarking

AI’s demand for processing power and network speed is growing exponentially, pushing infrastructure to terabit speeds. But compute alone isn’t enough—true performance comes from how your chips connect. GGateway engineers and validates high-performance, fabric-aware applications to ensure your infrastructure is optimized for AI, HPC, and next-generation data-center workloads.

The AI Networking Challenge & RDMA

Networking is now co-designed with chips and racks. To maximize throughput per watt, bandwidth, latency, and data flow must be perfectly orchestrated for AI inference and training.

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Core Capabilities

RDMA Optimization

Leveraging Remote Direct Memory Access to bypass the CPU, allowing direct memory-to-memory transfers via the NIC for the ultra-low latency that AI/ML workloads demand. We support RoCEv2 and InfiniBand transports across multi-vendor environments.

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Interoperability Testing

Validating performance across evolving network fabrics as infrastructure scales up and out.

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Co-Design Support

Aligning network configurations with compute hardware to prevent congestion in multi-gigabit and terabit-speed environments.

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Advanced Lab Infrastructure & Testing Suite

Our lab is purpose-built for RDMA and GPU-accelerated workload validation, featuring dual Dell PowerEdge R760XA servers equipped with NVIDIA L40S and AMD Instinct MI210 GPUs, a Dell PowerSwitch Z9664F-ON 400GbE fabric, and a comprehensive RDMA NIC inventory spanning Broadcom Thor 1 (BCM957508 100GbE), Broadcom Thor 2 (BCM57608 400GbE), and NVIDIA Mellanox ConnectX-7 (400GbE) — providing a multi-vendor, production-grade test environment for high-performance networking across PCIe Gen 4 and Gen 5 platforms.

Our Testing Toolkit

Python-Based RDMA Perftest Suite

Developed in-house with full feature parity to traditional C-based perftools. Our Python-native approach enables rapid integration with modern automation frameworks and CI/CD pipelines

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Deep Integrations

Seamlessly connected with pyverbs, advanced GPU workflows (GPUDirect RDMA), and modern automation frameworks.

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Comprehensive Coverage

Advanced testing for RDMA-CM connectivity, memory registration, multi-QP traffic, and precise rate-limiting.

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Vendor-Neutral Validation & Tuning

We go beyond standard benchmarking to provide actionable engineering insights, ensuring your network fabric never bottlenecks your compute capabilities.

Validation Focus

  • Measuring and optimizing absolute throughput and latency across 100GbE and 400GbE fabrics
  • Analyzing tail-latency behavior under sustained, large-scale AI training loads
  • Executing practical performance tuning to support next-generation processing requirements
  • Cross-vendor interoperability validation (Broadcom, NVIDIA, AMD)
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