Today's computing systems will undergo a massive transformation to efficiently and scalably serve AI workloads. NiNI Labs is an applied research lab dedicated to envisioning the next generation of these systems.
Three shifts that make general-purpose infrastructure the wrong foundation for AI.
For the last decade the hard problem was training one model. Now the hard problem is serving millions of model calls a day, each with a different shape, latency budget, and cost ceiling. The systems underneath were never designed for that.
An agent is not one request. It is a long-running loop of model calls, tool invocations, searches, and state. Bursty, branching, and stateful — the opposite of the stateless request/response that today's serverless platforms assume.
Accelerators sit idle between batches, memory bandwidth is the bottleneck, and schedulers still treat a GPU like a CPU. Closing that gap is a systems problem as much as a hardware one.
Where the lab spends its time. Each area is measured against live workloads from the Nicrron gateway and the Ninoo agents.
Execution models where an agent's stages — model calls, tool use, search, custom code — are scheduled independently, scaled to zero when idle, and resumed without losing state.
Placing requests across heterogeneous accelerators, batching without hurting tail latency, and routing between models by cost, quality, and load in real time.
How an agent remembers across hours or weeks: durable conversation state, retrieval that stays fresh, and caches that cut repeated context without changing answers.
Pipelines that chain multiple models with non-model stages such as search, ranking, and structured data — and the tooling to import, observe, and optimize them as one unit.
Energy, memory, and dollars per useful token. Speculative decoding, quantization, KV-cache reuse, and co-scheduling, measured end to end rather than on a benchmark harness.
Instrumentation for multi-agent systems: tracing every stage, attributing cost, catching regressions, and turning production traffic into the next experiment.
Research that holds up becomes a service. This is the first.
Serverless inference for AI agents. Run everything from simple agents to complex multi-agent systems with custom logic and data sources. Import existing agentic pipelines, chain multiple models with non-model stages such as search and custom data sources, and scale it all seamlessly. The platform handles scheduling, orchestration, and optimization, so you can focus on adding new capabilities to your agents.
Our test bed is real traffic. The Nicrron gateway and the Ninoo agents give the lab live workloads to measure against, so ideas are judged on tail latency and cost, not on slides.
We do not train frontier models. We work on everything around them — the runtime, the scheduler, the data path, the metal — where most of the remaining efficiency lives.
Research that holds up graduates into a product. NiNI Cloud is the first: the serverless agent runtime that came out of the lab's scheduling and orchestration work.
The lab sits between the products that generate the workloads and the facilities that run them.
The unified gateway routes production traffic to 65+ models. It is where the lab's routing, caching, and failover ideas get tested first.
nicrron.aiA coding agent in the terminal and a browser builder — long-running, tool-heavy, stateful. Exactly the kind of workload NiNI Cloud is being built to run.
ninoo.aiAI data centers built for AI. The lab's findings on accelerator utilization and scheduling feed directly into how Nicroo Core facilities are designed.
Nicroo CoreAn applied research lab inside the Nicrron family. It operates like a lab — hypotheses, experiments, measurements — but its output is products and infrastructure, not just papers.
NiNI Cloud, serverless inference for AI agents, is the lab's first service and is currently taking waitlist signups. Routing and caching work from the lab already runs inside the Nicrron gateway.
We intend to share findings as write-ups and open tooling where it does not compromise customer workloads. If you are working on similar problems, we would like to hear from you.
Yes. Email support@nicrron.ai with the subject "NiNI Labs" and tell us what you are working on — research collaborations, design partnerships for NiNI Cloud, and engineering roles are all open conversations.
Research collaborations, design partners for NiNI Cloud, and engineers who want to work on the runtime — all welcome.
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