vMind Model Training Infrastructure offers a multi-GPU NVIDIA H100 and H200 cluster of up to 64 GPUs. The connection between GPUs is NVLink within a node and InfiniBand NDR 400 Gb/s between nodes; training datasets are fed from a storage layer with 10 GB/s read speed, and checkpoint files are written to Object Storage. The cluster is brought up for the duration of training and shut down when the job is complete; billing is for running time only. Model training is a short-lived but intensive workload, and vMind infrastructure is designed around this rhythm.
Model training infrastructure is the whole of the GPU cluster, high-speed network and data storage layers required to train AI models on data. The training process runs as infrastructure that sits idle for weeks working at full capacity for a few days.
vMind Model Training Infrastructure is designed around this rhythm. You bring up the cluster for the duration of training and shut it down when the job is complete; billing is for running time only.
If the model has fewer than 70 billion parameters and the weights fit in 80 GB of memory, the NVIDIA H100 is sufficient. For larger models or trainings that require long context, the H200 with 141 GB of memory should be preferred.
Cluster size matters as much as GPU choice. Increasing the number of GPUs to shorten training time does not always yield linear gains; beyond a certain point, the inter-node connection speed becomes the limiting factor. Share your workload, and let us determine the right configuration together.
Keep Your GPUs from Waiting for Data, Finish Training on Time
In model training, GPUs sitting idle directly means cost. In vMind infrastructure, the GPU, network and storage layers are sized together.
Share your training workload; let us determine the right cluster configuration together based on model size, dataset volume and target duration.
You can find answers to frequently asked questions about Model Training Infrastructure in the Knowledge Center.
To go to the Knowledge Center, click here.
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