# OVHcloud GPUs: distinguishing cloud instances from other services Evaluate OVHcloud Public Cloud GPU through product, configuration, region, charging model and the boundary between infrastructure and managed AI services. Author: Retrivium team Updated: 2026-11-01 Canonical URL: https://retrivium.ai/journal/ovhcloud-gpu-guide/ Language: en OVHcloud offers several infrastructure and AI directions. Teams launching their own language models should distinguish Public Cloud GPU from the brand's other products. A provider name alone describes neither management level nor commitments. This official-product-page review is current to 1 November 2026. It neither recommends a tariff nor reports independent location testing. ## Inspect Public Cloud GPU The [Cloud GPU page](https://www.ovhcloud.com/en/public-cloud/gpu/) describes accelerated instances for compute. Record exact GPU, VRAM, device count, host and region, connecting each to model requirements. If full physical-machine control is needed, discuss that separately. A cloud instance and dedicated server are different resources. Clarifying the boundary before testing is easier than discovering it during environment preparation. The reviewed GPU section includes L4, L40S, H100 and H200. They provide candidates for different memory and execution requirements. Match each device to an actual configuration and location; the general catalogue does not confirm a launch path. ## Do not transfer properties between products Development, training and model-answer services may provide more management than an ordinary VM. Identify who installs the engine, operates the API and updates the model. Check data and access requirements across the selected chain. This particularly matters for estimates. A managed-service price and compute-only price are not equivalent. Include your own engineering effort rather than leaving it outside the comparison. ## Build the expense estimate The [Public Cloud price catalogue](https://www.ovhcloud.com/en/public-cloud/prices/) separates products and configurations. Clarify charging period, commitments and related resources. Save the selected conditions and estimate date when regions or instance types differ. Include model and artifact storage, networking and necessary service components. Check stopping and deletion consequences separately. Test expense should cover every created resource, not only successful generation time. ## Compare two infrastructure formats A hypothetical team compares a cloud GPU instance with its own server using identical model and requests. It checks deployment, quality and recovery, then preparation time, tools and operating duties alongside speed. A different subsequent configuration is another hypothesis. Findings from a small VM do not transfer to a distributed cluster without testing. A constraint-and-action list is more useful than one tokens-per-second number. Define continuation criteria beforehand: required quality, acceptable latency and understandable operations. Apply them equally to both machines, rather than choosing a convenient presentation for each. ## Assessment notes The reviewed product rents compute; it does not establish a programme accepting your physical GPUs. Read the [Hetzner, OVHcloud and Scaleway comparison](/journal/hetzner-vs-ovhcloud-vs-scaleway/) and [full inference economics](/journal/inference-economics/).