GPU Server Deployment
GPU infrastructure is unforgiving — wrong driver versions, CUDA mismatches, and memory errors waste days. We deploy GPU servers with properly configured drivers, container runtimes, and monitoring so your team trains models instead of debugging hardware.
Need this done for your project?
We implement, you ship. Async, documented, done in days.
Driver & CUDA Stack
We install and pin NVIDIA drivers, CUDA toolkit, and cuDNN to versions validated against your ML framework. Driver upgrades go through a test pipeline — not applied blindly. The NVIDIA Container Toolkit gets configured so containers access GPUs without host-level CUDA installs. We've seen teams lose weeks to driver mismatches; we prevent that from day one.
Multi-GPU & Scheduling
For multi-GPU setups, we configure NVLink topology awareness and GPU affinity. If you're on Kubernetes, the NVIDIA device plugin handles GPU allocation per pod with resource limits. Slurm or Kubernetes scheduling ensures training jobs don't fight over GPUs. Fractional GPU sharing via MIG (Multi-Instance GPU) on A100s lets inference workloads share hardware efficiently.
Monitoring & Alerting
DCGM Exporter feeds GPU metrics — utilization, memory, temperature, ECC errors — into Prometheus. Grafana dashboards show per-GPU utilization across your fleet. Alerts fire on thermal throttling, ECC errors, and GPU memory leaks. You see exactly where your compute budget goes.
Delivery & Handoff
You get provisioned GPU servers with validated driver stacks, container runtime configured, monitoring wired, and documentation covering upgrade procedures. We hand off a tested system, not a pile of Ansible playbooks and a 'good luck'.
Why Anubiz Engineering
Ready to get started?
Skip the research. Tell us what you need, and we'll scope it, implement it, and hand it back — fully documented and production-ready.