A runtime optimization layer that reduces the cost of every token — without changing the model
Get in touchBacked by research. Built for production.
Market Context
LLM API Spend
Annual LLM inference API revenue ($B)
The Compute Gap
LLM inference demand vs power/infrastructure scaling (indexed, 2024 = 1.0)
Inference API spend is growing +81% year-over-year while infrastructure scales at barely 30%. The gap between demand and capacity isn't closing — it's accelerating.
AI compute demand grows 4.5× per year — more than twice the pace of Moore's Law.
Epoch AI
Projected global data center electricity consumption by 2030 — double what it is today.
IEA, 2025
Typical GPU utilization in production inference workloads. Most compute is wasted.
The bottleneck isn't chips — it's how we use them.
Introducing
Software layer between your API and the GPU. No hardware swap. Just more tokens per watt.
No hardware changes. Drop-in runtime layer.
Built on deep GPU kernel expertise.
Optimized memory, reduced waste, fewer idle cycles.
Before/after on the same hardware. Instant gain.
cost reduction per token — on the same hardware, immediately
What If
Before
annual electricity spend
GPUs at baseline utilization
After Simplex
annual electricity spend
↓ $35–42M saved
effective GPUs
↑ +3,600 virtual — no new hardware
total power capacity
same infrastructure
Projection based on public infrastructure data. Not an existing deployment.
The Team
Top scientists with PhDs. Engineers who trained LLMs from scratch and shipped them to production. Published at NeurIPS, ICCV.
CEO
AI Transformation Lead at telco with 160M+ subscribers. Led ~20-person team. $6M+ revenue generated.
Top-50 World University · 6 Nobel Laureates · Applied Mathematics
CTO
Systems Architect at a Fortune 10 energy company. B2B platforms serving 45,000+ clients.
Top-50 World University · 6 Nobel Laureates · Applied Mathematics
Scientific Advisors
AI Research
Head of leading industrial AI research lab. Co-author of widely cited ICCV paper.
Machine Learning
PhD. Co-author of top-3 ML library (NeurIPS).
Systems Architecture
PhD. Ex-Microsoft, Amazon, NVIDIA. One of the world's top researchers in systems optimization.
We work with select partners and investors. If you're building at the frontier of AI infrastructure, reach out.
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