Vision

The next layer of compute is light

AI is no longer limited by arithmetic. It is limited by how fast, and how cheaply, data moves between the chips doing the arithmetic. LightScale is building that layer in light: switches that reconfigure at packet cadence, links that carry die-to-die traffic over light at any reach, and one programming model across both. The same photonics point further, to optical compute and to switching for quantum computing.

The problem

Compute kept scaling. The network didn't

Where the time goes

Models outgrew the chip years ago. A training step or a served token now runs across thousands of accelerators, and a growing share of every step is spent waiting on the network: gradients to reduce, experts to exchange tokens, activations to arrive. The arithmetic finishes early and sits idle.

Where the energy goes

Today's fabrics are electrical switches joined by optical cables. Every hop turns light into electrons and back, and every lane that leaves a package becomes a serial link at several picojoules per bit. Multiply that by every port in a cluster and moving data becomes a power budget of its own.

And the shape of that network is fixed the day the racks are cabled. Every parallelism strategy wants a different topology, so a fixed fabric picks one compromise at build time, and every workload pays for it on every step.

What we believe

Light for the data path, software for its shape

The switch stays optical

A photon that enters a switch should leave it as a photon. Scale1 reroutes any input to any output entirely in light, on a non-thermal switching matrix built to scale radix without the thermal load or per-port power overhead of conventional reconfigurable optics.

Distance stops costing energy

Photon1 keeps its electronics at logic speed and lets light carry the distance, so the energy per bit barely moves whether the far die is a millimetre or a rack away. One link serves the package, the board and the rack.

The fabric follows the workload

Hyperlane exposes topology, routing and link assignment to software and reconfigures at packet cadence, fast enough to reshape the network inside a training step. Each phase gets the network it wants, and the code never notices.

What we're building

One platform, from the die to the pod

64 × 64Any-to-any, in light. The Scale1 pilot.
< 1 pJ / bitPhoton1's target, at any reach.
Packet cadenceHow fast Hyperlane reshapes the fabric.
One APILUMA, across switching and compute.

The horizon

A fabric that is light end to end

  1. Now

    Scale1 in the rack

    The SXNR-001-64 pilot, 64 × 64, in Open Rack V3 and as a 1U dev kit, with LUMA above it, PyTorch first.

  2. Next

    More ports, whole racks

    Higher port counts, then Pod1: racks of Scale1 switches joined by an all-optical fabric, so a pod is dedicated switching capacity that lowers into place.

  3. Then

    Light between dies

    Photon1 brings the same optics inside the package, so reticle-sized dies, their memory and the racks around them work as one system, with the host's protocol layers unchanged.

  4. Beyond

    Light beyond the interconnect

    The same photonics reach into compute itself. Optical compute, where the matrix operations at the heart of AI run in light. And quantum computing, where information already travels as photons, so a switch that routes them in light, with no conversion to electrons, is what joins quantum processors into systems. LUMA spans switching and compute from Scale1 onward, so the fabric keeps changing underneath and the code written today keeps running.

The next layer of compute, starting with Scale1

Photonic switching at packet cadence, optical links that hold their energy at any reach, and one programming model across both. The first pilot is under way.