We design and deploy scalable GPU clusters optimized for AI training, inference, and high-performance computing — so startups and enterprises can run complex models without bottlenecks.
Pre-configured GPU servers with optimized cooling, fast interconnects (NVLink / PCIe Gen 4), and tuned BIOS profiles. Deploy a single workstation or scale to a 32-node cluster.
A100 #1
94%
A100 #2
88%
A100 #3
91%
A100 #4
87%
A100 #5
76%
A100 #6
68%
A100 #7
93%
A100 #8
89%
Run your training jobs across 4, 8, or 32 GPUs with linear scaling. We pre-configure DDP, ZeRO, and FSDP — you just point at your model.
# 8-GPU distributed training on Glixy torchrun \ --nproc_per_node=8 \ --nnodes=4 \ --rdzv_backend="glixy" \ --rdzv_endpoint="cluster-01" \ train.py # Output [rank0] node-01 · A100 #0–7 ✓ [rank8] node-02 · A100 #0–7 ✓ [rank16] node-03 · A100 #0–7 ✓ [rank24] node-04 · A100 #0–7 ✓ total: 32 GPUs · 2.5 TB VRAM throughput: 4.8 PFLOPS step time: 1.24s · ETA 4h 12m
RTX 3090, RTX 4090, A100, and H100-ready architecture with NVLink topology.
Multi-node scaling with DDP, FSDP, DeepSpeed, ZeRO — pre-tuned for our fabric.
Kubernetes-based GPU scheduling and allocation. Fair-share, priority, and gang scheduling.
High-speed NVMe storage for fast data access. Shared, redundant, and burst-cached.
Optimized CUDA stacks via NVIDIA. Pre-built containers for PyTorch, TensorFlow, JAX.
Grafana dashboards for GPU util, VRAM, throughput. Slack/email alerts on any anomaly.
7B–70B parameter models, fine-tuning, RLHF
Object detection, segmentation, OCR
Encoding, super-resolution, generation
Diffusion models, GANs, voice synthesis
Tell us your workload. We'll quote, architect, and deliver in 48–72 hours.