Beyond PCIe and NVLink: Polyhedral Optical Mesh Networks for AI Acceleration

🚀 GPU Parallelism vs. Connectivity Bottlenecks

"In the future, connection defines performance."

🔍 1. Why Should GPUs Worry About External Connectivity?

Modern GPUs execute massive parallel operations across thousands of cores. However, once the computations are complete, transferring data externally—to other GPUs, CPUs, storage, or network systems—becomes the bottleneck.

The real challenge isn't inside the GPU, but how it connects to the outside world.

⚠️ 2. Internal Speed vs. External Limitations

• Internal memory bandwidth (e.g., NVIDIA A100): 600+ GB/s
• PCIe 4.0 x16 external link: ~32 GB/s (theoretical maximum)
• Result: Severe mismatch → External I/O throttles overall system performance

🧩 3. NVIDIA and Industry-Level Solutions

NVLink

• Direct high-speed connection between GPUs
• Limited to short distances, only within the same server

NVSwitch

• Interconnects 8–16 GPUs into a unified cluster
• Centralized switch hub, but again, intra-server only

GPUDirect RDMA

• Enables direct data transfer between GPU and external devices
• Bypasses the CPU, but requires complex setup and specialized network gear

Grace Hopper Superchip

• Integrates CPU and GPU for high-bandwidth shared memory
• Still constrained by traditional external interfaces

Quantum Interconnect (Research Phase)

• Future-oriented optical or quantum-level communication
• Promises unlimited parallelism, but not yet commercially viable

🌐 4. In the Era of AI and Distributed Processing

Training and deploying large-scale AI models often requires hundreds or thousands of GPUs working in parallel. In such environments, overall system throughput is governed by:

• GPU-to-GPU latency
• Synchronization overhead
• Data movement inefficiencies

Even the most advanced internal GPU architectures suffer from network-induced bottlenecks at the inter-node level.

🔦 5. The Solution: Velsanet’s Optical Parallel Network

Velsanet offers a revolutionary architecture that replaces PCIe and NVLink dependency. By using multi-faceted polyhedral devices and multi-core optical channels, Velsanet enables direct parallel communication across distributed GPU nodes, AI agents, and users.

• Per facet: 18,432 optical cores
• Each channel: 800 Gbps throughput
• Max connections: Up to 10 facets per device
• Aggregate capacity: Hundreds of Pbps in real-time parallel streams

🧠 Final Insight

GPU performance isn't limited by its internal architecture anymore. It's limited by how—and how fast—it connects to others.

💡 Velsanet is not just a network; it's an optical nervous system that interlinks GPUs, AI agents, and humans in true parallelism. No bottlenecks. Just pure, scalable connectivity.