GPU Clusters – high-performance GPU infrastructure for AI/ML and HPC

GPU Clusters suit LLM training, distributed training, fine-tuning, large-scale AI inference, HPC, simulation, data science and any workload that needs many GPUs running in sync.

B200 suits next-generation workloads that need Blackwell performance, large VRAM and heavy scaling. H100 suits training, HPC and steady production AI on the well-established Hopper ecosystem.

InfiniBand lowers latency and raises bandwidth when GPUs and nodes synchronize gradients, transfer tensors or process distributed data, particularly for multi-node LLM training.

Yes. HiTechCloud can advise on CUDA, drivers, container runtimes, PyTorch, TensorFlow, DeepSpeed, Slurm, Kubernetes GPU scheduling and workload-specific storage and network architecture.

Yes. GPU clusters suit HPC, simulation, rendering, large-scale data analytics, image and video processing, computational chemistry and many other parallel computing workloads.

Training workloads should favor NVMe and high-speed shared storage to reduce bottlenecks when reading datasets and writing checkpoints, artifacts, logs and model output.

You can choose a cluster of 16, 24, 32, 40, 48, 56 or 64 GPUs according to project scale, budget and throughput requirements.

Contact HiTechCloud for advice on GPU count, GPU model, nodes, CPU cores, VRAM, storage, InfiniBand and the billing cycle that fits your workload.