What GPU Servers Does Hetzner Offer for AI Workloads?

Hetzner currently lists three GPU server options for AI work: the GEX430, a multi-GPU server with four NVIDIA RTX PRO 6000 GPUs and 384 GB of VRAM; the GEX63, a single-GPU server with an NVIDIA RTX PRO 6000 Blackwell Max-Q and 96 GB of GDDR7 ECC VRAM; and the GEX45, positioned as entry-level professional GPU performance. The right pick depends on whether your workload needs multiple GPUs working together or fits on one card with a large memory pool.

The three GPU servers at a glance

The GEX430 is the only one of the three described as a multi-GPU server. Hetzner frames it around "four GPUs, one server" and a full 384 GB of VRAM, aimed at demanding AI workloads. The GEX63 and GEX45 were both announced as new additions in 2026, with the GEX63 emphasizing a single high-memory Blackwell-generation card and the GEX45 emphasizing an attractive entry-level price.

Which one fits your workload?

Choose the GEX430 if your work spans multiple GPUs

A 384 GB pool across four cards matters when a model or job cannot fit on a single GPU, or when you want to run parallel workloads on one machine. This is the option Hetzner explicitly ties to "demanding AI workloads." The trade-off is that multi-GPU setups add complexity: your framework and code need to actually distribute work across the four cards to get the benefit.

Choose the GEX63 if one large-memory GPU is enough

The GEX63 pairs a single RTX PRO 6000 Blackwell Max-Q with 96 GB of GDDR7 ECC VRAM. ECC memory is relevant for long-running jobs where a silent memory error would corrupt results. If your model fits within 96 GB and you don't need multi-GPU scaling, this avoids the coordination overhead of the GEX430.

Choose the GEX45 if you are starting out

Hetzner describes the GEX45 as bringing "professional-grade GPU performance at an attractive entry-level price." The source does not list its GPU model or memory, so confirm the exact specifications on the product page before committing. It suits experimentation, smaller inference jobs, or teams that want professional GPU hardware without paying for a flagship card.

What to check before ordering

• Memory fit: Estimate your model's VRAM requirement, including activations and batch size, and compare it against 96 GB (GEX63) or 384 GB (GEX430). If you exceed a single card, the GEX430 is the relevant option.
• Multi-GPU support: Confirm your training or inference stack can split work across four GPUs before choosing the GEX430.
• Exact GEX45 specs: The announcement does not state the GPU model or VRAM, so verify these on the product page.
• Pricing: The source gives starting prices for other Hetzner product lines but not for the GPU servers. Check current pricing on each product page rather than assuming a figure.

Beyond the GPU line

Hetzner's broader catalog includes dedicated servers, cloud servers, managed servers, web hosting, storage products, and custom solutions. If your AI work is light or intermittent, a standard cloud or dedicated server without a GPU may be more cost-effective; the GPU line is aimed at workloads that genuinely need accelerated compute.