# LightSwitch – In-Network and Photonic LLM Inference in Transit

> Source: <https://github.com/pisantifrancesco89/Project-LightSwitch>
> Published: 2026-08-29 09:51:22+00:00

"Do not send data to centralized AI data centers: embed intelligence directly into the light traveling through global networks."

📖 **Languages**: **English** | 🇮🇹 [Leggi in Italiano (Italian Version)](/pisantifrancesco89/Project-LightSwitch/blob/main/README_IT.md)

**Project LightSwitch** is an experimental software prototype and hardware architecture for **In-Network & Photonic Artificial Intelligence Compute**. It simulates executing Large Language Model (LLM) inference and matrix transformations directly inside optical fibers, switching cabins, and satellite laser links while packets are in transit, eliminating the GPU Von Neumann memory bottleneck and reducing compute energy by ** $95% - 98%$**.

```
[ CLIENT (Copper / Wi-Fi) ] 
       ↓ (Tokenized prompt)
[ SWITCHING CABIN / EDGE PoP (P4 Switch / FPGA) ] ➔ Computes Initial Model Layers (15 ns)
       ↓ (WDM Optical Carrier Modulation @ 1550nm C-Band)
[ TERRESTRIAL & SUBMARINE OPTICAL FIBER ] ➔ Photons travel at 200,000 km/s in silica
       ↓ (Ternary Optical Phase Interference {-1, 0, 1})
[ LEO SATELLITE CONSTELLATION ] ➔ Inter-satellite laser links at 300,000 km/s (Speed of light in vacuum)
       ↓ (Downlink & Token Decapsulation)
[ DESTINATION GATEWAY ] ➔ Final output token already computed upon arrival! (Zero GPU queue latency)
```

-
**Zero Multiplications (**: Model weights are quantized to 1.58-bit ($100%$ Additive Transforms)${-1, 0, 1}$ ). Floating-point multiplications are replaced by optical constructive/destructive phase interference and integer add/subtract operations ($0\text{ FP Multiplications}$ ). -
**Zero VRAM Bottleneck**: Static weight matrices reside in on-chip routing tables (TCAM / UltraRAM / Micro-Ring Resonators), eliminating power-hungry HBM memory transfers. -
**Sub-2 Nanosecond Hardware Packet Filter**: Switches inspect incoming headers in, instantly distinguishing AI prompt packets from everyday Internet traffic (Netflix, Zoom, HTTPS) without CPU intervention.$< 1.5\text{ ns}$ -
**Commercial Off-The-Shelf (COTS) Readiness**: Can be deployed immediately using accessible hardware:** AMD Xilinx UltraScale+ FPGA SmartNICs**(~€2,800) or** Intel Tofino 2 P4 Switches**(~€8,500).

```
.
├── README.md                          # English Documentation & Quickstart
├── README_IT.md                       # Italian Documentation & Quickstart
├── requirements.txt                   # Optional dependencies (rich, numpy, matplotlib)
├── config.py                          # Optical physical constants & simulation parameters
├── core/
│   ├── encoder.py                     # WDM optical wavelength modulation (C-Band 1530-1565nm)
│   ├── switch.py                      # In-network optical switch and in-transit compute engine
│   └── matrix_sparse.py               # 1.58-bit ternary additive engine {-1, 0, 1}
├── simulation/
│   ├── fiber_channel.py               # Single-mode silica fiber delay & attenuation model (SMF-28)
│   ├── pipeline.py                    # Multi-hop end-to-end execution pipeline
│   ├── heterogeneous_network.py       # Global WAN model (Copper, P4 Cabins, Fibers, LEO Satellites)
│   └── packet_classifier.py           # Hardware packet classifier (<1.5 ns TCAM parser)
├── benchmarks/
│   ├── metrics.py                     # FLOPs, Latency, and Energy telemetry (picoJoules vs milliJoules)
│   ├── compare_gpu.py                 # Mathematical comparison vs NVIDIA H100 GPU server
│   └── hardware_costs.py              # 3-Year TCO financial engine (CAPEX & OPEX)
├── docs/
│   ├── WHITEPAPER.md                  # Formal scientific whitepaper & mathematical models
│   ├── hardware_architecture.md       # Technical hardware guide for COTS chips & optical interconnects
│   └── IMPACT_AND_ROADMAP.md          # Global launch manifesto, grants (EIC Pathfinder), and roadmap
├── tests/
│   └── test_all.py                    # Automated test suite (100% passing)
└── main.py                            # Interactive CLI with Rich terminal visualization
```

Zero complex setup required. Operates using standard Python 3.10+:

```
# Run baseline simulation (3 in-network optical switches with cost & GPU comparison)
python3 main.py --prompt "LightSwitch routes intelligence directly into optical media"
python3 main.py --global-wan --prompt "Global distributed intelligence in transit"
python3 main.py --traffic-filter
python3 -m unittest discover tests
```

| Hardware Architecture | Chip Type | CAPEX (Hardware) | Power Consumption | 3-Year Electricity (OPEX)* | 3-Year Total TCO | Savings vs GPU |
|---|---|---|---|---|---|---|
Traditional AI Server (8x NVIDIA H100) |
Monolithic GPU | €280,000 |
10,200 W | €70,815 |
€350,815 |
Baseline |
3x FPGA SmartNIC Cluster |
AMD Xilinx (COTS) |
€8,400 |
225 W | €1,562 |
€9,962 |
-97% 🚀 |
3x Programmable P4 Switch Cluster |
Intel Tofino 2 (COTS) |
€25,500 |
750 W | €5,207 |
€30,707 |
-91% 🚀 |
3x Silicon Photonics CPO Engine |
Ayar Labs (COTS) |
€36,000 |
105 W | €729 |
€36,729 |
-90% 🚀 |

**Calculated with industrial electricity rate of €0.22/kWh and cooling PUE = 1.2.*

- 📖
: Mathematical formulations, Maxwell wave propagation, and theoretical derivation.[Scientific Whitepaper](/pisantifrancesco89/Project-LightSwitch/blob/main/docs/WHITEPAPER.md) - 🛠️
: Commercial chips, QSFP28 pinouts, MZI optical meshes, and distributors.[Hardware Architecture Guide](/pisantifrancesco89/Project-LightSwitch/blob/main/docs/hardware_architecture.md) - 🌍
: Open-source strategy, European deep-tech grants (EIC Pathfinder), and telecom integration.[Roadmap & Manifesto](/pisantifrancesco89/Project-LightSwitch/blob/main/docs/IMPACT_AND_ROADMAP.md)

Released under the open-source [MIT License](/pisantifrancesco89/Project-LightSwitch/blob/main/LICENSE).