Private LLM hosting vs io.net
We rent DGX Sparks, so weigh that against everything below, and note upfront that io.net isn't quite the same kind of thing GPUwerk is. io.net is a decentralized GPU marketplace: it aggregates compute contributed by many independent operators around the world, coordinated through its own network layer, rather than running a fleet of its own data centers. That's fundamentally different from a single company renting out hardware it owns and operates. A DGX Spark from GPUwerk is the latter: one company, one machine specification, one EU location, one accountable operator you're contracting with directly. Comparing the two isn't quite apples to apples, and this page tries to be honest about that rather than pretend otherwise.
Side by side
| io.net | Dedicated DGX Spark (GPUwerk) | |
|---|---|---|
| What it is | A decentralized marketplace coordinating GPU capacity contributed by independent third-party operators worldwide, not io.net's own hardware. | A single-purpose GPU host: one dedicated DGX Spark, owned and operated by GPUwerk, rented by the hour. |
| Who operates the hardware you land on | An independent operator somewhere in io.net's network, generally not identifiable or vettable in advance; io.net coordinates the marketplace, it doesn't run the machines itself. | GPUwerk directly. The company you're paying is the company running the machine, full stop. |
| Region | Wherever the allocated operator's hardware happens to be; check io.net's current documentation for how much region control an allocation actually offers. | EU-Central (Prague), exclusively. One location, no region selection to get wrong. |
| Who can see your data | Governed by io.net's network terms plus whatever access the underlying independent operator's setup allows; worth reading closely given the decentralized model routes workloads to third parties you likely can't identify. | 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 | Varies by which operator's machine you're allocated: GPU model, RAM, network, and reliability history all differ across the network. | Every node is the same: 128GB unified memory, NVIDIA's DGX Spark architecture, identical specification across the fleet. |
| Pricing model | Set by network supply and demand across independent operators, published on io.net's own marketplace pages and changing accordingly. | 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 | Governed by io.net's network-level terms, layered on whatever the individual contributing operator's arrangement covers; check current terms closely given the multi-party structure. | 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 allocated instance: OS, model server, monitoring, backups, same as any bare GPU rental. | The same: OS, model server, any RAG or agent layer, monitoring, backups. 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 workload that runs inference continuously across a full month. Two ways to serve it:
io.net. Priced by network supply and demand across independent operators, published on io.net's own marketplace pages and varying by GPU class and network conditions at the time. A fair dollar comparison needs a specific allocation and its actual rate at the time you'd rent it, so GPUwerk didn't invent a blended figure here. Check io.net's current marketplace rates for the GPU class you'd actually need for a real number, and factor in that you're renting from an operator you likely can't identify in advance.
Dedicated Spark. At $0.79/hour, running continuously for a 730-hour month costs 730 × $0.79 = $576.70, flat, at one known rate from one known, accountable operator. If you hold the reservation instead of running it, the held rate drops that to 730 × $0.59 = $430.70.
A decentralized marketplace can undercut a fixed rate on raw price, that's part of the appeal of aggregating idle third-party capacity. What it structurally can't offer is a single accountable operator you're contracting with, or certainty about where your data physically sits. Weigh scale and price against provenance for your own risk tolerance, these are genuinely different products.
Migration path
Both ultimately expose a Linux GPU instance, so migration is mostly re-deploying your own stack. Serve a model through vLLM on a Spark and on an io.net allocation, and both expose an OpenAI-compatible endpoint from vLLM itself, not from the underlying infrastructure layer. Put LiteLLM in front of either for request logging and key management. Because io.net allocations can vary in GPU model and network characteristics between sessions, expect more variability re-testing a model's performance there than you would moving between two Sparks, which are identical by design.
When io.net is the right choice
- You want access to a large, price-flexible pool of GPU capacity and are comfortable with a decentralized, multi-operator model.
- Raw price and scale matter more than knowing exactly which company operates the hardware you land on.
- Data residency and a single accountable operator with a standard DPA aren't hard requirements.
When a dedicated Spark is the right choice
- You need to know exactly who operates the hardware, under one contract, with one company accountable.
- A consistent specification across every node matters more than marketplace scale.
- EU data residency and a standard GDPR DPA are requirements, not nice-to-haves, and a decentralized network of unidentified operators doesn't fit your compliance posture.
FAQ
Is io.net a good alternative to GPUwerk for LLM hosting?
io.net is a decentralized GPU marketplace: it aggregates compute from many independent operators worldwide rather than running its own data centers. That structure gives access to a large pool of GPUs, but who actually operates the machine you land on, and where it sits, varies by allocation. GPUwerk operates its own DGX Spark fleet directly, so every node is the same known hardware in the same EU location under one company's direct control. These are different models, not a straight price comparison: io.net trades known provenance for scale and price flexibility, GPUwerk trades scale for a single, verifiable operator.
What's the difference between a DGX Spark and an io.net allocation?
An io.net allocation routes your workload to compute contributed by an independent operator somewhere in io.net's network; specs, location, and who actually runs the hardware vary by allocation, so check io.net's current documentation for what you'd actually get. 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, with GPUwerk as the sole operator you're contracting with.
Is a DGX Spark cheaper than io.net?
It depends heavily on which allocation and GPU class you'd compare against, since io.net pricing is set by network supply and demand across independent operators and changes accordingly. A dedicated Spark costs $0.79/hour flat, a single known number from a single known operator. Check io.net's current marketplace rates for the GPU class you'd actually need before comparing, and weigh the price against the fact that you're renting from an unknown third-party operator rather than a single company you can hold accountable.
Why choose a DGX Spark over io.net?
Mainly provenance, consistency, and accountability. Every Spark GPUwerk rents out is hardware we operate directly, in a data center we control in Prague, at one published rate, under one standard DPA. An io.net allocation runs on hardware contributed by an independent operator you likely can't identify or vet in advance, which makes data handling and reliability guarantees harder to pin down. If a specific, known, EU-hosted machine under one accountable operator matters more than a decentralized marketplace's scale and pricing, the Spark fits better.
GPUwerk did not find a single io.net price that fairly represents its network of independent operators; check io.net's own site for current marketplace rates. GPUwerk's own figures ($0.79/hour, $0.59/hour, $576.70/month) come from our published pricing.