Lumetrix Networks is pioneering intelligent optical fabrics to overcome the physical limits of electronic switching in today’s data centers. Our technology aims to replace complex, power-hungry network layers with a fully optical, massively scalable architecture.
At its core lies a passive Optical Circuit Switch (OCS) — designed to interconnect thousands of ports directly in light. No conversions, no amplification, almost no latency, and near-zero power draw.
This is more than a new component — it’s a new paradigm for data movement. By routing data at light speed, Lumetrix enables AI clusters to scale efficiently, communicate instantly, and operate sustainably.
We envision AI data flowing seamlessly, efficiently, and at light speed.
Lumetrix Networks is shaping the future of data centers — scalable, high-performance networks that keep pace with the demands of tomorrow’s AI.
Built for Performance, Scale, and Reliability
Data transfer is not limited by computation
No dropped packets, no re-synchronization delays
A single light path can reach multiple destinations simultaneously
Redundant solid-state LCOS design keeps operating even if individual pixels fail
Future-proof for multi-wavelength and multi-channel data centers
Simple scaling to high-volume production and large port counts —up to 1024×1024 and beyond
AI & Machine Learning Data Centers
High-Performance Computing (HPC)
Optical Interconnects & Research Labs
Cloud and Hyperscale Infrastructure
CEO & Co-Founder
Founding Advisor
Founding Advisor
We’re shaping the future of data connectivity — together
Lumetrix Networks collaborates with hyperscalers, system integrators, and technology investors to bring optical switching to global scale.
For partnership, investment, or collaboration inquiries — reach out to us directly:
Contact us GPUs waiting. AI cluster idle.
In large-scale AI training, thousands of GPUs must constantly exchange data in perfect sync. Traditional electrical switches slow this down — data hops through multiple stages, adding latency and idle time.
Studies show that over 50% of total training time in large AI clusters is lost to network communication, not computation. Meta found that up to half of GPU time can be wasted waiting for data to arrive.
As clusters scale, the slowest network link becomes the bottleneck — throttling the entire system’s performance.
The High Cost of Connectivity
AI data centers are massive investments — often exceeding USD 1 billion each. Yet, as much as 10 % of that total build cost goes into the network fabric alone.
And that fabric doesn’t come cheap:
10 GbE ≈ USD 300 per port
40 GbE ≈ USD 1,500 per port
100 GbE ≈ USD 6,000 per port
When scaled to tens of thousands of ports, interconnect costs can reach hundreds of millions of dollars — just for the links that move data around. Add in power, cooling, and space, and the price of keeping GPUs connected quickly becomes staggering.
Every watt and every dollar spent on inefficient networking is a drag on AI progress.
AI Networks Are Power-Hungry
As AI clusters scale, network energy use is exploding. A single enterprise-class switch can draw up to 13 kW per rack — multiplied across thousands of racks, this becomes a massive load.
Globally, data centers are projected to consume ~945 TWh of electricity by 2030, driven largely by AI and accelerated computing (IEA). Analysts estimate that AI-specific data centers already draw tens of gigawatts (GW) of power today.
Gartner warns that by 2027, power shortages could limit 40% of AI data centers if infrastructure can’t keep up.
The network layer alone contributes several GW to this crisis — a challenge traditional electrical fabrics can’t solve.
Network Bottlenecks Are Slowing AI
Even the fastest GPUs can be held back by network capacity limits. In Google’s Jupiter network, adding a 200 Gbps aggregation block into a 100 Gbps spine caused most links to underperform — only 25% reached full speed, while the rest were limited to 100 Gbps.
This isn’t an isolated problem. As AI workloads grow, rigid network fabrics struggle to scale, leaving GPUs waiting and potential bandwidth unused. Without flexible, high-speed connectivity, data centers risk bottlenecks that slow AI performance and limit scalability.