Urban-IT GPU instances are optimized for AI model training, large-scale inference, data processing, scientific simulation, and rendering workloads. They are ideal for LLM training (70B+), diffusion models, high-performance inference pipelines, and HPC research.

H200 offers the highest training performance with next-generation memory bandwidth.
H100 is the industry standard for both training and inference.
B200 is the next-gen Blackwell GPU with exceptional inference efficiency.
GB200 combines two B200 GPUs into a powerful compute cluster node.
A100 provides excellent price-to-performance for both training and inference.

The H200 features significantly higher memory bandwidth and capacity compared to the H100, enabling larger batch sizes, longer context lengths, and faster training throughput. It is ideal for training extremely large models or handling long-sequence workloads.

H100 SXM offers higher performance, supports NVLink, and is optimized for multi-GPU training.
H100 PCIe is slightly less powerful, does not provide full NVLink bandwidth, and is more cost-efficient.
Choose SXM for distributed training; choose PCIe for inference or lightweight training.

Yes. The A100 remains one of the most cost-efficient GPUs for training and inference workloads. It performs extremely well on Stable Diffusion, LoRA training, classical deep learning tasks, and many mid-scale transformer models.

The GB200 is not a single GPU but a combined compute module containing two B200 GPUs and a Grace CPU with high-speed interconnect. It is designed for extremely large-scale model training and enterprise-grade HPC workloads.

Custom pricing applies when the configuration requires multi-node clusters, additional networking (such as InfiniBand), dedicated capacity, or enterprise-level SLAs. Contact us for a tailored quote.

All major frameworks are supported, including PyTorch, TensorFlow, JAX, DeepSpeed, Megatron-LM, HuggingFace Transformers, vLLM, and TensorRT-LLM. Both training and inference workloads are fully optimized.