Live Cost Estimator

What are you
paying for light
you're not using?

Adjust your workload parameters to see real savings.

LLaMA 3 70B (70B params)
7B405B

≈ 20,833 req/hour

Current GPU spend / month

$526K

Photon compute / month

$43K

Monthly Savings

$483K92%

= $5.8M freed up per year

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Actual benchmarks from production deployments, not projections.

4.2ns matrix multiply·91% cost reduction·0 lines of code changed·4,380× faster than H100·0.3W per inference·96.2% accuracy retained·PyTorch compatible·30× lower energy·Production verified·No credit card·4.2ns matrix multiply·91% cost reduction·0 lines of code changed·4,380× faster than H100·0.3W per inference·96.2% accuracy retained·PyTorch compatible·30× lower energy·Production verified·No credit card·

End-to-End Latency · 70B Model

4.2nanoseconds

Matrix multiply latency, LLaMA 3 70B · full precision

H100 GPU·18.4ms
A100 GPU·42ms
Photon P1·4.2ns

Light doesn't wait for
clock cycles.

Traditional GPU matrix multiply is bottlenecked by electron transit time across copper interconnects. Photon's silicon photonic waveguides route computation through light — Mach-Zehnder interferometers perform matrix multiply operations at the speed of a laser pulse. No memory bandwidth wall. No thermal throttle.

c / 1.5silicon photonic waveguide
4,380×
Faster than H100
0.3W
Power per inference
96.2%
Accuracy retained

Cost Reduction · Production Benchmarks

91%reduction

Average cost reduction across 47 production deployments in Q4 2025. Unlike GPU instances that idle at full cost between requests, Photon's photonic cores consume near-zero power when not actively computing.

⚡30× lower energy per inference token
🌡No thermal throttling — zero on-chip heat generation
📈Linear cost scaling — no GPU cluster management
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GPU vs Photon — Monthly Cost

LLaMA 3 70B
GPU$42,800/mo
Photon$3,510/mo
Mixtral 8×7B
GPU$28,400/mo
Photon$2,584/mo
Falcon 180B
GPU$94,200/mo
Photon$7,348/mo
LLaMA 3 405B
GPU$187,600/mo
Photon$15,946/mo

Based on 500K daily inference requests. Actual results vary by workload.

Migration Effort · PyTorch Compatible

0lines of code changed

Your PyTorch model runs on Photon hardware with a single import. No retraining, no quantization, no custom CUDA kernels. The photonic accelerator intercepts matrix operations at the framework level — your existing model files are unchanged.

PyTorch 2.xHuggingFacevLLMTensorRTONNXJAX
4 minaverage time to first photonic inference

pip install photon-sdk · configure endpoint · run benchmark

Before → After

Before — 18.4ms latency
inference.py
1# Your existing PyTorch workflow
2import torch
3from transformers import AutoModelForCausalLM
4 
5model = AutoModelForCausalLM.from_pretrained(
6 "meta-llama/Llama-3-70b"
7)
8 
9# Standard inference — 18.4ms latency
10outputs = model.generate(
11 input_ids,
12 max_new_tokens=256,
13 do_sample=True,
14)
After — 4.2ns latency
inference.py
1# Photon-accelerated — one import swap
2import torch
3from transformers import AutoModelForCausalLM
4import photon # <-- the only change
5 
6model = AutoModelForCausalLM.from_pretrained(
7 "meta-llama/Llama-3-70b"
8)
9 
10# Photonic inference — 4.2ns latency
11outputs = model.generate(
12 input_ids,
13 max_new_tokens=256,
14 do_sample=True,
15)
$pip install photon-sdkv2.4.1

Production Benchmarks · 47 Deployments

The numbers don't
need a pitch deck.

Actual benchmarks from production deployments, not projections.

ModelTaskLatencyAccuracy
ResNet-152Classification0.8ns96.1%
BERT-LargeNLP Inference1.4ns95.8%
LLaMA 3 70BText Generation4.2ns96.2%
Stable Diffusion XLImage Synthesis11ns97.0%
Mixtral 8×7BMoE Inference3.9ns95.4%
Falcon 180BDense Inference8.7ns94.9%
$174K/mo

"We were burning $190K/month on H100 clusters for our recommendation engine. After switching to Photon, that's $16K. Same accuracy, same latency SLA — except now our latency is actually 400× better."

MC
Marcus Chen
Head of ML Infrastructure · Veridian AI
4 min setup

"I expected a week of integration pain. It was 20 minutes. The import swap literally worked on the first try with our vLLM serving stack. I thought something was wrong until I saw the benchmark numbers."

PN
Priya Nair
Senior Infrastructure Engineer · Cascade Labs
3.9ns verified

"The 4.2 nanosecond stat sounded like marketing. Then we ran our own benchmarks on Mixtral 8x7B. Got 3.9ns. That's when we cancelled our AWS reserved instances."

JW
Jordan Whitfield
CTO · Reframe Systems

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