How to Self-Host AI Inference on Hetzner's GEX63 Blackwell GPU Server with Kubernetes

Hetzner’s GEX63 dedicated server combines an NVIDIA RTX PRO 6000 Blackwell Max-Q GPU with 96 GB of VRAM, 128 GB of system memory, NVMe storage, and a flat €999 monthly price plus setup fee, positioning it as a potentially economical option for steady self-hosted AI inference. The guide recommends Ubuntu 24.04, a single-node K3s cluster, and NVIDIA’s GPU Operator to expose the GPU to Kubernetes, followed by deployment of vLLM or Ollama with persistent model caching, appropriate health probes, TLS, and authentication. Its VRAM can support approximately 32B-parameter models in BF16 or 70B-class models in 4-bit quantization, although KV-cache demands from long contexts and concurrent requests remain a primary capacity constraint. Because Blackwell uses CUDA compute capability sm_120, the server requires recent drivers, CUDA 12.8 or later, and PyTorch or vLLM builds containing sm_120 kernels; otherwise, containers may fail with a “no kernel image is available” error. The comparison argues that the server can cost less than comparable always-on cloud GPU rentals at high utilization, while cloud providers remain preferable for bursty workloads, interruptible batch processing, managed services, or redundancy needs. It also emphasizes operational responsibilities such as pinning drivers and kernels, monitoring GPU health and ECC or Xid errors, handling out-of-memory failures, securing public endpoints, and planning for the single node as a point of failure, while presenting Qovery as an optional deployment platform for adding self-service workflows to a self-managed cluster.

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