GPU Compute, at the
Scale You Need

Exclusive access to high-density GPU clusters — from small research deployments to hyperscale AI training infrastructure. Flexible delivery models designed for long-term operations.

GPU Cluster Product Matrix

We offer H100, H200, B300, and GB300 server configurations across four cluster sizes, with spot delivery, forward commitment, and long-term lease options.

Cluster Size	Nodes	H100	H200	B300	GB300
Small	8–16	✓	✓	✓	—
Medium	32–96	✓	✓	✓	—
Large	128–356	✓	✓	✓	✓
Hyperscale	512+	✓	✓	✓	✓

Delivery Models

Three modes, one provider.

Spot Delivery

Ready-to-deploy resources for urgent compute needs. Fastest time to access.

Forward Commitment

Reserve future capacity now. Built for planned training runs and product launches where scheduling predictability matters.

Long-Term Lease

Three-to-five-year terms for sustained AI workloads. Fixed pricing, high-availability SLA.

The Full-Stack Engagement

We don't just deliver servers. Our turnkey engagement covers the whole path from site to token.

Discuss Your Requirements →

Site selection and IDC co-design

GPU cluster integration and commissioning

Network interconnect design

InfiniBand, RoCE, and DDC fabrics.

24/7 intelligent operations

Managed maintenance with ≤15-minute incident response.

Token-as-a-Service (TaaS)

Value-added AI inference optimization layered on top of raw compute.

ATOM

Beyond Raw Compute: Intelligent Inference by Design

Getting GPUs is one thing. Extracting maximum value from them is another. ATOM is our inference acceleration architecture — a PD-separation approach that restructures how inference workloads run across GPU clusters, delivering dramatically better performance per dollar.

How ATOM works

ATOM separates the two distinct phases of LLM inference — prefill and decode — onto optimized node types, each tuned for its computational profile.

Prefill nodes

Long-sequence pre-filling and attention computation — compute-intensive, runs once per request.

KV-cache cross-node sharing

Generated KV-cache is shared across nodes, eliminating redundant computation — only KV-cache memory is required on decode nodes.

Decode nodes

Autoregressive token generation — memory-bandwidth-bound, benefits from lighter, lower-cost hardware.

Cross-datacenter scheduling

Prefill nodes sit close to end users for low latency; decode nodes sit at lower-cost remote compute for cost efficiency.

10× Overall inference throughput improvement

50% Reduction in total inference cost

<100ms Average response latency

4 GPU types supported — heterogeneous hardware compatible

Why it matters

AI inference costs at scale are dominated by compute efficiency, not raw GPU count. ATOM restructures that equation — clients running large-scale LLM inference on Ainfra infrastructure get more throughput per dollar than on conventional GPU rental, without managing the optimization stack themselves.