Private LLM hosting vs GMI Cloud
We rent DGX Sparks, so weigh that against everything below. GMI Cloud operates its own infrastructure directly, which puts it in the same single-fleet category as GPUwerk rather than a marketplace reselling third-party capacity. The differences that matter are scale, GPU class, and location. GMI Cloud is built around large H100 and H200 class clusters aimed at training and high-throughput inference, with capacity concentrated in the US market. GPUwerk rents a single dedicated DGX Spark per customer, in a data center it controls in Prague, sized for a team's private LLM hosting rather than a large training run.
Side by side
| GMI Cloud | Dedicated DGX Spark (GPUwerk) | |
|---|---|---|
| What it is | A GPU cloud provider operating its own infrastructure directly, focused on H100 and H200 class clusters for training and large-scale inference. | A single-purpose GPU host: one dedicated DGX Spark, owned and operated by GPUwerk, rented by the hour. |
| Who operates the hardware | GMI Cloud directly; it's a single-fleet operator, not a marketplace of third parties. | GPUwerk directly. The company you're paying is the company running the machine. |
| GPU class and sizing | High-end datacenter GPUs (H100, H200 class), available as single instances or larger clusters; check GMI Cloud's current site for configurations. | One fixed-spec machine per deployment: 128GB unified memory, NVIDIA's DGX Spark architecture, no cluster sizing decision. |
| Region | Capacity concentrated primarily in the US, with additional regions; confirm current EU availability directly with GMI Cloud if data residency matters. | EU-Central (Prague), exclusively. One location, no region selection to get wrong. |
| Who can see your data | Governed by GMI Cloud's own published terms and DPA; review their current documents for specifics on cross-border data handling. | Nobody at GPUwerk. Our DPA states GPUwerk "hosts the machine but does not access, read, copy, index, or analyse the content of the controller's instance." |
| Hardware consistency | Depends on the GPU model and cluster configuration selected at deployment time. | Every node is the same: 128GB unified memory, NVIDIA's DGX Spark architecture, identical specification across the fleet. |
| Pricing model | Set by GPU model and commitment term; check GMI Cloud's current pricing page for the configuration you'd need. | Per hour, billed per minute: $0.79/hour on-demand, $0.59/hour for a customer-requested stop that holds your reservation. One rate regardless of workload, per pricing. |
| Contracts and DPA | GMI Cloud's own standard terms; review their current published DPA for EU-specific data transfer terms. | A standard GDPR Article 28 DPA published free at /legal/dpa, no negotiation required. Sub-processor list at /legal/sub-processors states none are engaged for instance workloads. |
| What you operate yourself | Everything on the instance or cluster: orchestration, model server, monitoring, backups. | OS, model server, any RAG or agent layer, monitoring, backups on a single machine. GPUwerk keeps only a recovery copy of /workspace for hardware failures, refreshed roughly every six hours; it is not a backup service, so that responsibility is entirely yours. |
The worked cost example
Take a private inference workload that runs continuously across a full month on a single machine, not a training cluster. Two ways to serve it:
GMI Cloud. Priced by GPU model and commitment term, published on GMI Cloud's own pricing page and varying by the H100 or H200 configuration you'd deploy. A fair single-machine comparison needs their current rate for a comparable instance, so GPUwerk didn't invent a blended figure here. Note that an H100 or H200 is a substantially more powerful (and typically more expensive) GPU class than a Spark's, so a lower per-hour Spark rate reflects different hardware, not a like-for-like discount.
Dedicated Spark. At $0.79/hour, running continuously for a 730-hour month costs 730 × $0.79 = $576.70, flat, at one known rate for one dedicated machine. If you hold the reservation instead of running it, the held rate drops that to 730 × $0.59 = $430.70.
For workloads that need H100 or H200 class throughput, or a multi-GPU training cluster, GMI Cloud's model fits that need in a way a single Spark won't match. For a smaller private inference workload where EU residency and a fixed rate matter more than raw throughput, a Spark is the simpler and cheaper fit.
Migration path
Both expose a Linux GPU instance, so migration for a single-node workload is mostly re-deploying your own stack. Serve a model through vLLM on a Spark and on a GMI Cloud instance, and both expose an OpenAI-compatible endpoint from vLLM itself. Put LiteLLM in front of either for request logging and key management. Moving a workload sized for H100/H200 memory down to a Spark's 128GB unified memory may require re-checking model size and batch settings.
When GMI Cloud is the right choice
- You need H100 or H200 class GPUs, individually or as a larger training cluster.
- US-rooted capacity and its associated data handling terms are acceptable for your workload.
- Raw throughput and cluster scale matter more than a fixed EU-hosted machine.
When a dedicated Spark is the right choice
- Your workload fits on one machine and doesn't need H100/H200 class throughput or cluster orchestration.
- EU data residency and a standard GDPR DPA by default are requirements.
- You want one fixed, published hourly rate under one accountable EU operator.
FAQ
Is GMI Cloud a good alternative to GPUwerk for LLM hosting?
GMI Cloud operates its own GPU infrastructure directly, so it's a single-fleet provider in the same category as GPUwerk rather than a marketplace of third-party operators. The real differences are scale, target workload, and data residency. GMI Cloud is built around large H100 and H200 class clusters for training and high-throughput inference, with its roots and much of its capacity in the US market. GPUwerk rents a single dedicated DGX Spark per customer from a data center it controls in Prague, aimed at a team's private LLM workload rather than a large training cluster. If you need a big cluster and US-based capacity, GMI Cloud fits that. If you need one EU-hosted dedicated machine under a standard EU DPA, GPUwerk fits better.
What's the difference between a DGX Spark and a GMI Cloud instance?
A GMI Cloud instance is typically a high-end datacenter GPU such as an H100 or H200, sized individually or as part of a larger cluster; check GMI Cloud's current site for available GPU models and regions. A DGX Spark is a standard machine GPUwerk operates directly: 128GB unified memory, NVIDIA's DGX Spark architecture, the same specification on every node in EU-Central, sized for a single team's private inference workload.
Is a DGX Spark cheaper than GMI Cloud?
It depends heavily on GPU model, since a single H100 or H200 instance is a materially different class of hardware than a Spark, and GMI Cloud's current rates are on their own pricing page. A dedicated Spark costs $0.79/hour flat, one known number for one fixed-spec machine. Compare that against GMI Cloud's published rate for the specific GPU tier you'd actually need, keeping in mind you're comparing different hardware classes, not identical machines at different prices.
Why choose a DGX Spark over GMI Cloud?
Mainly EU data residency, a standard DPA available without negotiation, and simplicity for a single-machine private workload. A Spark runs in Prague at one published rate under one company's direct operation. GMI Cloud's strength is large-scale H100/H200 capacity for training and heavy inference; if your workload needs that scale, or you're comfortable with capacity rooted outside the EU, GMI Cloud is a reasonable option. If EU residency and a single dedicated machine are the priority, a Spark fits better.
GPUwerk did not find a single GMI Cloud price representative of every GPU model and commitment term; check GMI Cloud's own pricing page. GPUwerk's own figures ($0.79/hour, $0.59/hour, $576.70/month) come from our published pricing.