GPU Instances
NVIDIA GPU compute in China — aggregated capacity from multiple partners
Overview
We aggregate GPU compute across our partner network to provide NVIDIA H100, H200, A100, and L40S capacity for AI training, fine-tuning, and inference. Whether you need a single GPU or a multi-node cluster with high-speed interconnects, we find the provider with available capacity at the best rate. Pre-configured ML environments (CUDA, PyTorch, TensorFlow, vLLM) come standard. All deployments comply with China's data residency requirements.
Key features
- NVIDIA H100, H200, A100, and L40S GPUs sourced across multiple provider partners
- Multi-node configurations with high-speed interconnects for distributed training
- Pre-configured ML stacks: CUDA, PyTorch, TensorFlow, vLLM, DeepSpeed
- Choice of 1-GPU, 4-GPU, and 8-GPU node configurations from our partner pool
- High-bandwidth networking with RDMA support for gradient synchronization
- China-based infrastructure meeting data sovereignty requirements
Why choose GPU Instances
- Aggregated capacity: we find available GPUs when individual providers are sold out
- Better pricing through volume relationships across multiple hardware partners
- Dedicated allocation — no shared GPU virtualization overhead
- Provider switching: if one partner's pricing or availability changes, we migrate you
- Rapid sourcing: typical deployment within 24-48 hours across our partner network
Use cases
Large language model training and fine-tuning (LoRA, QLoRA, full fine-tune)
Production inference serving for AI applications with latency requirements
Computer vision model training for autonomous systems and medical imaging
Scientific computing, molecular dynamics, and simulation workloads
Interested in GPU Instances?
Talk to our infrastructure architects about how GPU Instances fits into your architecture.