GPU servers tuned for your model.
Training, fine-tuning, inference — each has a different optimal hardware shape. We spec, integrate, and validate GPU servers around your specific workload, then ship them ready to join your fabric. From single-node inference boxes to multi-rack training clusters.
Three things that separate us from a reseller.
Anyone can quote you an HGX 8-GPU box. Knowing whether you actually need NVLink fabric, what the right CPU-to-GPU ratio is, and how to debug an InfiniBand link that drops at 2 AM — that's the work.
GPU configurations we have shipped.
From single-card inference nodes to multi-node training clusters with NVLink and InfiniBand fabric. Each baseline has been validated in production.
What we've put into production.
Two recent GPU deployments — one for inference at scale, one supporting an active research portfolio.
Production inference cluster, paired with a 100G fabric upgrade.
39ai needed an inference cluster sized for sustained production traffic, then needed it to actually use that capacity once the storage tier could keep up. We delivered both halves.
Scope: GPU server spec'd for inference latency targets, integrated with the existing data plane, then a follow-on network upgrade taking the inter-rack fabric from 25G to 100G — the same fabric upgrade that unlocked storage throughput also unlocked sustained GPU utilization.
GPU cluster supporting multiple research projects in parallel.
Lingxingyu runs multiple active research initiatives that share a common GPU pool. The cluster needs to handle bursty multi-tenant scheduling without resource contention killing job throughput.
Scope: GPU server fleet integrated with a paired storage tier and unified network fabric, BMC standardized for fleet management, partitioning and scheduler integration validated with real research workloads before handover.