Multi-GPU and multi-node infrastructure for training at scale.
Training modern models requires coordinated GPU clusters with high-speed interconnects, shared storage and distributed training frameworks working together reliably. GPUMarket designs and operates training infrastructure so your team can focus on the model, not the cluster.
What this involves
- Multi-GPU and multi-node cluster architecture
- High-speed interconnects (NVLink, InfiniBand, RDMA networking)
- Distributed training frameworks and job orchestration
- Shared, high-throughput storage for datasets and checkpoints
- Checkpointing and fault tolerance for long-running jobs
- Monitoring GPU utilization across a cluster
Common challenges we help solve
Avoiding network bottlenecks across multiple nodes
We architect networking and interconnect topology appropriate to your cluster size.
Managing storage throughput for large datasets
We design shared storage sized for your data pipeline's read/write requirements.
Recovering cleanly from node or job failures
We configure checkpointing and job orchestration to minimize lost training time.
Recommended GPUs
Related managed software
Frequently asked questions
How many GPUs do we need for our training run?
This depends on model size, dataset size, target training time and budget. Share your requirements through our quote form and we will help scope an appropriate cluster.
Can GPUMarket provide multi-node clusters with high-speed networking?
Yes. We can source multi-node GPU clusters with high-speed interconnects suited to distributed training, either through our provider network or as dedicated infrastructure.