LightScale
LightScale builds ultrafast photonic switching for AI data center networks, enabling optical fabrics that reconfigure at packet cadence to match workload demands. Its Scale 1 switch uses a non-thermal photonic matrix, while the LUMA software layer exposes topology control to developers without requiring changes to existing PyTorch code. The company also develops Photon 1, an optical die-to-die interconnect targeting under a picojoule per bit.
- Semiconductor
- Hardware
- Artificial Intelligence
- AI Agents
- Telecommunications
Funding
Founders
Founder details are not available yet.
Product
Problem
AI workloads impose extreme communication demands on data center networks, but conventional electrical switches and fixed optical fabrics force a compromise: they either consume high power, add latency, or lock operators into a single network topology that poorly matches the varied communication patterns of distributed training, expert routing, and inference. Rapidly changing workload phases—gradient reduction, all-to-all token exchange, pipeline streaming—each prefer different network shapes, yet traditional fabrics cannot adapt quickly enough to serve them all efficiently.
Solution
LightScale delivers ultrafast photonic switching for AI interconnects, enabling optical networks that reconfigure at packet cadence rather than as slow control-plane actions. Its Scale 1 switch uses a non-thermal photonic switching matrix integrated at the device level, avoiding the thermal load and per-port power overhead of conventional reconfigurable optics. The Hyperlane architecture exposes topology, routing, and link assignment to software, letting operators reshape the fabric inside a training step—not just between jobs—so each workload phase gets the network shape it wants. The LUMA software layer provides a single API spanning switching and compute, so developers write once and run across all hardware generations without per-device rewrites or changes to existing PyTorch code.
Target Audience
Primary customers are AI infrastructure operators—including cloud providers and enterprises running large-scale training, inference serving, and MoE routing—as well as developers building distributed machine learning systems who need adaptive, low-power network fabrics.
Features
- Non-thermal photonic switching matrix integrated at device level, scaling switch radix without per-port thermal overhead
- Packet-cadence reconfiguration fast enough to change topology within a collective operation, not just between runs
- Hyperlane exposes topology, routing, and link assignment to the control plane, with real-time congestion and link-health feedback into reconfiguration decisions
- 64 × 64 pilot switch (SXNR-001-64) in 1U chassis with 96-core ARM CPU, 256 GB DDR5 RAM, and dual ConnectX-7 200/400G NICs; higher port counts up to 768 × 768 planned
- LUMA ships as a library that slots beneath PyTorch's collective and runtime layers, requiring no new operators, rewrites, or model changes
- Supports a broad range of network topologies including ring, mesh, 2D/3D torus, hypercube, fat-tree, dragonfly, butterfly, Slim Fly, Jellyfish, and Xpander
- Photon 1 optical die-to-die interconnect chiplet carries traffic over light between GPU and memory, targeting under a picojoule per bit at any reach