We share GPUs across teams with vGPU and MIG, deploy Kubernetes with the GPU Operator, and run LLMs and other models in production. As part of Master Cloud or on standalone infrastructure.
We configure every layer: from GPU virtualization to production model monitoring.
We partition physical GPUs into virtual ones for different tasks: training, inference, development.
Full pipeline: from training to inference via API with autoscaling.
Transparency: GPU utilization, inference quality, degradation alerts.
Battle-tested Open Source stack. No vendor lock-in — full control on your side.
On-premise GPU servers for models that can't leave your perimeter. Strict inference latency SLAs.
GPU virtualization so multiple teams share the same servers. Each sees their own vGPU.
We size GPU count and memory to the model, then set up quantization and parallelism. LLMs and RAG run inside your perimeter, so data never leaves.
Training and inference for CV models. Streaming video processing, edge deployment.