LLM Companies
For foundation and vertical model teams running inference at scale
Key Challenges
High inference cost
Self-built clusters sit idle off-peak, keeping per-token cost high
Capacity shortfalls at peak
Launches and traffic spikes outpace scaling, causing queues and timeouts
Long context slows first token
Multi-turn and agent workloads recompute the same prefixes, hurting latency and throughput
Fragmented chip stacks
Mixed NVIDIA and domestic chips mean separate engines, versions and drivers
Our Solution
Dedicated inference clusters
Isolated pools per model and SLO with dedicated capacity
KV-Aware Routing
Session affinity plus distributed KV cache reuse prefixes and cut TTFT
Unified heterogeneous chips
NVIDIA, domestic GPUs and NPUs scheduled on one platform by cost-efficiency
Autoscaling & canary
Load-based scaling, canary releases and one-click rollback
AI App Providers
For teams building assistants, coding tools, agents and content apps without their own GPUs
Key Challenges
High barrier to self-hosting
GPUs, data centers, drivers and engine tuning all need specialists
Painful multi-model integration
Different APIs, auth and billing mean code changes for every switch
Unpredictable cost
Usage fluctuates: reserved plans waste money, metered plans risk surprise bills
Fragile reliability
One provider's outage or rate limit takes your product down
Our Solution
Standard APIs
OpenAI / Anthropic formats: point existing SDKs and tools at a new URL
Unified model catalog
One key for many models, switch without code changes
Team quotas & metering
Quotas by team and project with real-time usage and cost
End-to-End Tracing
Every request traceable: latency, errors and usage at a glance
Industrial Parks / Government
For parks and platform companies with land, power and policy support building regional computing centers
(Domestic + NVIDIA)
Key Challenges
No specialist team
Power, liquid cooling, networking and GPU clusters need expertise local teams lack
Built but idle
Without operations and anchor tenants, utilization stays low
Hard technology choices
Balancing NVIDIA and domestic chips under localization rules is costly to get wrong
Weak industry pull
Computing centers lack links to local AI companies and supply chains
Full-Lifecycle Services
Planning
Demand modeling, site and energy assessment, chip selection and feasibility
Construction
AIDC engineering, liquid cooling integration, GPU cluster deployment and acceptance
Operations
24/7 operations and scheduling platform delivering token services
Industry Attraction
Bring model and app companies on site to build an AI cluster
Industrial Investors
For industrial capital, state-owned platforms and funds allocating computing assets
Key Challenges
Heavy assets, long payback
Large GPU and facility outlays depend on long-term stable demand
No professional operator
Unoperated GPUs sit idle and depreciate fast
Opaque returns
Rental income, utilization and O&M cost are hard to reconcile monthly
Fast tech turnover
Rapid GPU generations make selection and refresh strategy decisive
Investment Model
Asset form
GPU clusters and computing centers with clear ownership: co-built, built-to-suit or self-held
Revenue structure
Compute leasing plus token services, with monthly operating reports
Risk control
Pre-committed demand, phased investment and 24/7 operations
Operations
We run scheduling, O&M and customer operations to lift utilization
How We Work
- 01
Discovery
Response within 1 business day to understand your needs
- 02
Design
Architecture, configuration and quote
- 03
Validation
Token trial or project feasibility study
- 04
Delivery
Milestone-based rollout
- 05
Operations
24/7 operations with usage and revenue reports
FAQ
How do I integrate?
We are compatible with OpenAI and Anthropic APIs. Point your SDK, coding tool or agent framework at our endpoint.
Can it be deployed on-premises?
Yes. We deliver dedicated clusters on site so data never leaves your premises, ideal for finance and government.
How is it priced?
The public API is billed per token; private clusters and computing center projects are quoted per project.
Do you support domestic chips?
Yes. The platform manages NVIDIA, Ascend, Sugon, Moore Threads and more, configurable for localization requirements.
What partnership models are available?
Co-investment, built-to-suit and compute leasing, combinable to fit your capital and operating goals.
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