Pre-validated. Pre-loaded. Ready for training. Designed for data scientists, university laboratory deep learning education, and small teams for lightweight inference. The ideal starting point for AI development – no complex configuration required to begin model training.
| Component | Specification |
|---|---|
| Chassis | Tower (optional rack conversion kit) |
| Processor | Intel Core i9 / Xeon W |
| GPU | 1x NVIDIA RTX 4090 / RTX 6000 Ada |
| Memory | 64GB DDR5 (expandable to 128GB) |
| Storage | 1TB NVMe + 2TB HDD |
| Network | Dual Gigabit Ethernet |
Data scientists conducting new model prototype development, university laboratory deep learning courses, small teams running lightweight inference tasks. Suitable for budget-sensitive startups and research projects.
Second GPU acceleration card, liquid cooling system, 5-year extended warranty, additional NVMe storage expansion, professional workstation graphics card replacement options.
Pre-validated. Pre-loaded. Built for distributed training. A multi-GPU training platform designed for medium-sized teams, supporting PyTorch DDP and Horovod distributed training frameworks. This system can serve as a single-node high-performance workstation or the starting point for a multi-node cluster.
| Component | Specification |
|---|---|
| Chassis | 4U rackmount, supports 4 dual-slot GPUs |
| Processor | Dual Intel Xeon Gold / AMD EPYC (64+ cores) |
| GPU | 4x NVIDIA RTX 4090 / RTX 6000 Ada / L40S |
| Memory | 256GB DDR5 ECC (expandable to 1TB) |
| Storage | 2x1.92TB NVMe RAID-0 + 4x3.84TB data drives |
| Network | Dual 10GbE, optional InfiniBand or 100GbE |
| Power | Redundant 3000W 80+ Platinum |
Pre-installed with Ubuntu 22.04 LTS, NVIDIA Driver 550+, CUDA 12.4/cuDNN, Docker and NVIDIA Container Toolkit, Kubernetes single-node configuration, PyTorch 2.3 and TensorFlow 2.15 distributed training support.
Capable of training ResNet-152 in hours, BERT-base models in days. Supports multi-GPU parallelization (DDP, Horovod), suitable for teams scaling from single workstations to clusters.
Medium-sized teams conducting distributed model training, multi-GPU research projects (computer vision, natural language processing), enterprise AI development teams, university laboratories and corporate research groups.
Upgrade to NVIDIA H100 or L40S GPUs, add second node to build 8-GPU cluster, InfiniBand interconnect for high-speed multi-node training, extend warranty to 5 years.
Compact. Rugged. Pre-loaded with inference software stack. A real-time inference server designed for edge AI deployment, suitable for manufacturing quality control, retail analysis, security monitoring, and other low-latency edge scenarios. Small form factor design enables flexible deployment in space-constrained edge environments.
| Component | Specification |
|---|---|
| Chassis | Small form factor |
| Processor | Intel Xeon D / Core i9 low-power |
| GPU | 1x NVIDIA L4 / A2 |
| Memory | 32GB DDR4 ECC (expandable to 128GB) |
| Storage | 512GB NVMe + optional 1TB cache |
| Network | Dual Gigabit Ethernet, optional 5G/LTE module |
| Power | 12V DC, typical power consumption < 200W |
Configured with 4 GPUs: ResNet-50 inference > 100+ inferences/second, latency < 10ms. Configured with 2 GPUs: balanced cost-performance, supports 4-6 video streams, suitable for medium-scale deployments. Full TensorRT optimization ensures minimum latency.
Real-time computer vision (manufacturing, retail, security), edge AI deployments (low latency requirements), remote sites (where cloud centralization is not feasible), edge video analytics (single server supports up to 6 streams).
Ubuntu 20.04/22.04 LTS, NVIDIA AI production drivers, CUDA/TensorRT, Triton Inference Server (optional), Docker and NVIDIA Container Toolkit, common model optimization scripts.
High-density. Pre-wired. Cluster-ready. An enterprise-grade GPU server designed for large-scale AI training, supporting 8 high-end GPUs per node and scalable to 4-64 node clusters via InfiniBand. This is the core computing unit for building AI factories and HPC clusters.
| Component | Specification |
|---|---|
| Chassis | 4U/8U rackmount, supports dual-width GPUs |
| Processor | Dual Intel Xeon Platinum / AMD EPYC |
| GPU | 8x NVIDIA H100 / H200 / B200 |
| Memory | 1TB DDR5 ECC (expandable to 2TB) |
| Storage | 4x3.84TB NVMe RAID-10 hot-swappable |
| Network | Dual 100GbE, optional InfiniBand NDR 400Gb/s |
| Power | Redundant 6kW 80+ Titanium |
Pre-loaded with Ubuntu 22.04 LTS (HPC/AI optimized), NVIDIA Driver/CUDA/cuDNN/NCCL, Docker/Enroot/Singularity container support, Slurm/Kubernetes cluster orchestration (optional), PyTorch/TensorFlow/JAX multi-node support.
Configured with 8 H100 GPUs: training speed is 20x faster than single-GPU workstations, suitable for large models. Designed for scaling to multi-node via InfiniBand, ideal cluster size 4-64 nodes. Each node can process tens of billions of parameter models (using model parallelism).
Enterprise custom training for Llama 3, GPT-like models and other large language models.
Large-scale research projects and cutting-edge algorithm exploration.
Production training platform requiring maximum throughput.