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Model Training Infrastructure

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.

What Is Model Training Infrastructure?

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. 

Which GPU Should You Choose for Model Training?

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. 

Why vMind Model Training Infrastructure: Advantages

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. 

Clusters of Up to 64 GPUs Clusters of Up to 64 GPUs
InfiniBand NDR 400 Gb/s InfiniBand NDR 400 Gb/s
10 GB/s Data Access 10 GB/s Data Access
Checkpoint Resilience Checkpoint Resilience
Pay for What You Use Pay for What You Use

What Matters in Model Training Infrastructure?

  • 1- Inter-GPU bandwidth...
    In distributed training, GPUs constantly exchange gradients. If the connection is slow, you buy 8 GPUs and get the performance of 4.
  • 2- Dataset access speed...
    A GPU can process thousands of samples per second; if storage cannot keep up with that speed, the GPU waits idle.
  • 3- Checkpoint resilience...
    Interruptions are inevitable in trainings that last for days. Without regular checkpoints, an interruption means days of lost work.
  • 4- Cluster size planning...
    Beyond a certain point, increasing the number of GPUs does not yield linear gains; the optimum size is determined according to the workload.
  • 5- Dataset versioning...
    Dataset versions are tagged and stored so that it can be traced which model was trained on which data.

Share your training workload; let us determine the right cluster configuration together based on model size, dataset volume and target duration.

Knowledge Center

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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