Live Latency Simulator — Silicon Available Q3 2026

Computation
at the speed of light.

Configure your workload. Watch photonics render GPU latency irrelevant.

1512
HighLow
—
GPU Cluster
—
TPU Pod
—
Photon
Configure your workload above and hit Simulate to begin.
Explore the data
Performance Comparison

Every axis. Photonics wins.

Inference Latency
GPU Cluster
798 µs
→
Photon
2.7 µs
Speedup
295×
798
GPU
612
TPU
2.7
Photon

Measured on GPT-scale matrix multiplication, batch 32. Photon processes in less than half a nanosecond per MAC operation.

Energy per FLOP
10−17
Joules / FLOP
vs GPU (10⁻¹² J/FLOP)100,000× more efficient
Batch Scaling — Interactive
Batch Size64
1512
GPU Latency
51.1ms
Photon Latency
0.17µs
Read the Whitepaper ↗
Thermal Envelope
700W
GPU Node
per accelerator
450W
TPU Pod
per chip
12W
Photon
per module

Near-zero thermal losses from optical signal propagation eliminate active cooling overhead. Photon modules run at ambient datacenter temperature.

Rack Density
0 Tbps
per-rack bandwidth
Photon modules / 1U8
Max TFLOPS / rack960
Watts / TFLOP0.09
Precision Mode
INT8
FP162× memory, higher accuracy
Photon natively supports FP16 and INT8 with on-chip reconfigurable waveguide routing. No software emulation — hardware-level precision switching.
Fabrication Cost
CMOS
Compatible Manufacturing

Built on standard 300mm wafer lines. No exotic materials. Fab cost trajectory mirrors conventional silicon — not III-V compounds.

75% cost parity
Silicon Credibility

Working silicon.
Not a simulation.

A single Photon chip integrates 16,000+ components — modulators, detectors, waveguides, and MZI arrays — fabricated on standard CMOS-compatible silicon. No exotic materials. Ships from any Tier-1 foundry.

16,384
Components

Photon P1 — silicon photonic integrated circuit, 7nm CMOS

On-chip components16,384+
MAC latency< 0.5 ns
Inference accuracy96.2%
Training accuracy96.8%
Bandwidth38 Tbps
Process node7nm CMOS
Wafer size300mm
Compute density10¹⁶ FLOPS/mm²
Accuracy benchmarks on CIFAR-100 and ImageNet validation sets. Photon achieves 96.2% inference accuracy — within 0.3% of FP32 baseline on equivalent GPU hardware.
Waveguide Signal Path
Input Layerλ = 1550nmOutput Layer
Thermal & Energy

Power draw,
measured honestly.

Photon modules run at ambient temperature. No liquid cooling. No overhead cooling budget. The numbers below are wall-socket watts, not TDP ratings.

H100 GPU
700W
TPU v4
450W
A100 GPU
400W
Photon P1
12W
Sustainability note: At scale, shifting 10% of global ML inference to photonic hardware eliminates an estimated 2.4 million tonnes of CO₂ annually — equivalent to removing 500,000 passenger vehicles from the road.
↓
98.3%
Power reduction vs H100
⚡
0.12 mJ
Joules per inference
♻
1.02
PUE improvement
🌿
0.4g
CO₂ per 1M inferences
Operating Temperature
85°C
GPU
72°C
TPU
28°C
Photon
Benchmark Access

Run your own benchmark.

Describe your current stack. We provision a simulation environment calibrated to your workload — real hardware results delivered within 48 hours.

No sales call required. Results delivered via email. Read the Whitepaper for methodology.

Run Your Own Benchmark →