Dedicated clusters with customized infrastructure, integrated operations and facility management. 3–5-year committed contracts or annual leases. Built for training and large-scale inference.
VM and container instances with pre-installed AI environments, multi-tenancy and metering. Build first, then operate — hourly or per-minute billing. Covers annotation, deployment and inference serving.
Pay-per-use APIs across open-weight and licensed frontier models, served from our own and partner capacity. We aggregate demand, route it to the best-fit capacity, and distribute inference across regions.
Heterogeneous, large-scale GPU cluster management with fine-grained resource isolation, real-time cluster state, and a unified scheduling engine for complex workloads.
Distributed inference engine with dynamic KV-cache reuse and operator-level chip optimization. Per-card token throughput more than 40% above industry average.
Token-agent-application layer and development toolchain for agents and industrial models — the same stack we use to build our own industrial AI and Agent OS.
Direct sourcing of GPU systems and prefabricated, factory-built data-center modules. Construction cycle compressed from 24+ months to 6–9 months.
Integrated design of GPU cluster and facility: Tier III+ availability, 2N+1 redundancy, liquid cooling, low PUE, global backbone networking — one accountable project manager.
24/7 on-site engineering, ≤15-minute response, ≤24-hour major-fault recovery. Dual-person review for critical changes; ISO 27001 and defense-in-depth data security.
Equity and debt participation in compute projects, with structures that align operators, investors and off-takers.
Anchor customers, GPU systems and data-center modules, and power: site selection with direct connection to generation. Compute and power planned as one asset, not two contracts.
We sell compute; what makes it work is everything around it.
We are not trying to be the largest cloud. We are trying to be the most useful layer under real applications.
Small-to-mid clusters on each generation as it ships: GB300 in a near-term project, Vera Rubin next year. The fastest path to customized on-premises enterprise AI.
Deep collaboration with frontier-model developers on serving, optimization and hosting, open-weight and licensed.
Low-latency, high-reliability, high-concurrency scenarios: industrial control, autonomous driving, very large enterprise applications.
Aggregating demand and distributing inference across our own and partner capacity, region by region.
Companies that turn large volumes of raw data into AI-ready assets: data platforms, expert data and evaluation, labeling at scale. They need exactly the clusters we build.
Because our position is chosen, not defaulted, we can work with compute providers, model developers, data companies and integrators as partners rather than competitors.
Clusters are our business today, and we take them on selectively. Each one is chosen so both sides come out stronger: the client gets a workload-specific cluster, delivered and operated end to end; we gain the operating knowledge our industrial AI models and Agent OS will run on.
Private cluster today → industrial models and agents deployed where the data lives tomorrow.
Shared capacity → GPU cloud and token distribution for smaller companies and one-person companies.
Deeper collaboration → industrial AI built together; the client becomes a design partner and a reference.
We build clusters to learn what the stack needs — one partner at a time.