# Dedicated GPUs for LLMs: four different purchasing formats

Compare Hetzner, Latitude.sh, Oracle Cloud and CoreWeave dedicated resources: physical servers, bare-metal configurations and dedicated model endpoints.

Author: Retrivium team
Updated: 2026-11-01
Canonical URL: https://retrivium.ai/journal/top-dedicated-servers-llm/
Language: en

A search for dedicated LLM GPUs can lead to fundamentally different offers: a physical server, cloud bare-metal node or separate model service. One hourly rate cannot compare them without explaining those differences.

Prepared on 1 November 2026, this list illustrates four selection formats. It is neither a hardware ranking nor based on independent tests. Order implies no superiority.

## Editorial method

We selected four officially described offers where resource dedication matters to an owned model. The criterion is control format and division of operations. Deliberately different layers help readers choose the service class first.

Final budgets should compare equivalent configurations within that class. Check availability, region, account requirements and contract separately. Inclusion confirms no currently free capacity for an order.

## Four offers and their differences

| Provider | What to investigate | Comparison boundary |
| --- | --- | --- |
| [Hetzner GEX](https://www.hetzner.com/dedicated-rootserver/matrix-gpu/) | Catalogue dedicated GPU server | Complete node and self-managed environment |
| [Latitude.sh](https://www.latitude.sh/docs) | Metal GPU and management tools | Dedicated infrastructure lifecycle |
| [Oracle Cloud](https://www.oracle.com/cloud/compute/gpu/) | Bare-metal GPU alongside VMs | Specific shape and configuration |
| [CoreWeave](https://coreweave.com/products/coreweave-forge/dedicated-inference) | Dedicated Inference | Model service, not an arbitrary server |

The last row intentionally distinguishes layers. An endpoint is not a direct substitute for full-machine administration. Conversely, buying a whole server adds team work when the need is a working service.

## Ask beyond the name

Identify OS, engine, model, API and update owners. Clarify metrics, artifact preservation and incident responsibility. These answers turn dedicated into an understandable commitment.

For multi-card configurations, verify connections and supported execution. Total VRAM does not automatically describe context or concurrency. Test the specific model rather than device counts alone.

## Compare budgets without substitution

A hypothetical team first decides what it can operate itself. It then estimates infrastructure plus internal work against an appropriate managed format, including storage, network, support and duration in both.

The short test should reveal quality, latency, recovery and change effort. If services solve different tasks, say so. A smaller compute line alone does not identify a winner.

## Evaluate owned equipment

Compare an existing server with the chosen resource level, including placement, power, maintenance and software service. Another supplier's retail rate does not promise GPU-owner payment.

Read [pool onboarding](/journal/gpu-pool-onboarding/) and [inference economics](/journal/inference-economics/).