A dedicated endpoint built around your traffic that keeps getting faster after it ships, run by a system that improves with every customer.
We are building a real Recursive Self-Improving Intelligence. The first place we are pointing it is the hardest and most profitable problem in AI: inference acceleration. We will prove that RSI is the basic organizational form of intelligence, then take it into every field. But it starts with inference.
Any company putting intelligence into its product or service. In a few years, almost every company.
A seven-figure payroll for expertise that is scarce, and a setup that breaks every time the model updates.
A ceiling. They hand over the core of their product with no control over its speed, quality, or price.
Bring the model, a sample of real traffic, and a target. Get a private endpoint with performance written into the SLA.
All you see is an endpoint. Kernels, engine, quantization, scheduling, parallelism, and the card itself are searched together against your actual request mix.
Traffic drifts, models update, drivers change. We adapt as it happens: endless optimization, tailored only to your production needs.
The fastest setup is never a single switch. Kernels, serving engine, batching, quantization, hardware, and traffic shape all push on each other. Real performance is a combination, and combinations are what the system searches.
Profiles the customer's stack on real traffic to see where latency or throughput is lost: scheduling, decoding, kernels, memory pressure, or hardware fit.
Generates candidate configurations across batching, decoding, quantization, engines, kernels, and hardware, and measures every one. Thousands of runs, verified in hours.
Deploys the fastest verified configuration, then keeps profiling production traffic as load, models, and hardware evolve.
That is what makes the loop trustworthy, and what makes its memory worth something. Every solved endpoint shortens the next search.
Model, engine, kernels, and hardware optimized together against the outcome you care about.
Fused ops, attention paths, GEMM variants, and decode kernels for the model shape and hardware target.
Serving engine auto-tuned to the model, traffic shape, memory pressure, and latency target.
Speculative decoding, FP8 / FP4 / W8A8 formats, batching, and expert sharding for MoE.
Companies are moving off closed APIs and onto open weights they can run themselves. Open-model router volume is up 5x in six months.
Businesses in every sector are putting intelligence into their own product, and discovering that the hard part is not the model. It is running it well.
Product companies have figured out that the model is the moat, and renting it from a big lab is not one. Nobody has made owning it affordable yet.
Per card-hour plus reserved capacity, with performance targets written into the SLA. At least 30% below your current cost per token at equal or better latency.
20-30% of the measured reduction against your current bill. The easiest contract to sign, and the one that proves our numbers.
The fastest endpoint for the hottest open models, listed on OpenRouter at a speed premium. The router sells; inbound converts to dedicated.
Try the public modelsCS, Sun Yat-sen University (national top-talent program, HCP Lab); MSc Venture Creation, NUS. Self-funded two years delivering private multi-agent systems to enterprise clients in exoskeletons, game advertising, and e-commerce. Founded Launchpad S1 and SG Hacker House; ran a 200-person agent hackathon.
Get a private endpoint with performance written into the SLA. Custom in a week, not years.
Talk to us