Powering the AI Revolution: Next-Gen Networking for GPU Clusters

Your AI Investment Hinges on the Network

High-performance compute needs high-performance connectivity.

The AI Data Tsunami - Why AI Traffic is Unlike Anything Else

AI and ML workloads—especially training jobs—generate bursty, synchronized traffic flows that overwhelm traditional networks. Large model training involves petabytes of data moving between thousands of GPUs, often using all-reduce, pipeline, or expert parallelism.

Why Legacy Networks Break Under AI Pressure

Traditional data center networks weren’t built for the intense, low-entropy, high-bandwidth flows required by AI. Here’s what fails:

❌ Tail Latency: A few slow packets delay the entire job.
❌ Packet Loss: Retransmits cause GPU stalls and training inefficiency.
❌ Idle GPUs: High-dollar accelerators sit unused, waiting on data.

These issues can turn a state-of-the-art AI cluster into a performance bottleneck—and a budget drain.

The Need for Speed Doesn’t Stop at Training

While training gets most of the attention, real-time AI inference brings its own set of network demands, especially at scale.

Low Latency Becomes Critical: Real-time decisions—like fraud detection, recommendation engines, and autonomous systems—can’t afford lag. Inference often needs latency in microseconds, not milliseconds.
Edge and Multi-Cloud Connectivity: Inference often runs closer to the user—across hybrid clouds, data centers, or edge locations. This introduces complexity around consistent performance, security, and observability.
Smaller, More Frequent Bursts: Inference workloads involve smaller packet sizes but high frequency, which require fine-tuned Quality of Service (QoS) and efficient routing to avoid jitter and delay.

High-Performance AI Networking: The 4 Pillars That Power Modern AI Workloads

AI workloads demand more than raw compute — they require network infrastructure that can move massive volumes of data with precision, speed, and reliability. Our performance fabric is engineered to meet the exacting requirements of GPU-accelerated AI environments at scale.

Why High-Performance Networking Matters

In AI clusters, even microseconds of delay or packet loss can significantly degrade training performance and increase costs. Network infrastructure must evolve to support distributed compute, high concurrency, and flexible scaling, without locking you into proprietary architectures.

The 4 pillars of high-performance AI networking address these demands through: