You can use TensorMesh to deploy, run, and scale machine learning models across distributed compute infrastructure. It connects disparate graphics processing units across multiple data centers, helping you avoid cloud provider lock-in. You can route inference requests to available hardware, monitor latency, and track computational resource usage from a unified dashboard. The system handles workload distribution, auto-scaling during traffic spikes, and failover if a node drops offline. It makes it easier for engineering teams to serve large language models reliably while keeping cloud operational expenses under control.
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