Private LLM hosting vs TensorDock
We rent DGX Sparks, so weigh that against everything below. TensorDock is a GPU marketplace: individual hosts list spare or dedicated GPU capacity, and renters book time on whichever listing fits their needs and budget. That structure gives access to a wide range of cards at prices set by supply and demand, but the machine you get, and who operates it, varies by listing. A DGX Spark is the opposite: GPUwerk owns and operates the hardware directly, one specification, one location, one published rate.
Side by side
| TensorDock | Dedicated DGX Spark (GPUwerk) | |
|---|---|---|
| What it is | A marketplace connecting GPU renters to individual hosts, mostly consumer and prosumer cards, some data-center-grade listings. | A single-purpose GPU host: one dedicated DGX Spark, rented by the hour, built around unified memory for inference. |
| Who operates the hardware | The individual host who listed it, not TensorDock itself. Reliability and support depend on that host. | GPUwerk directly. The company you're paying is the company running the machine. |
| Region | Wherever the specific host's hardware sits; check the listing for its stated location, which can be outside the EU. | EU-Central (Prague), exclusively. One location, no region selection to get wrong. |
| Who can see your data | Governed by TensorDock's terms and, in practice, whatever access the underlying host's hardware and network setup allow. Worth reading their current policy closely for a marketplace model. | 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 listing to listing: GPU model, RAM, storage, and network all depend on what a given host has posted. | Every node is the same: 128GB unified memory, NVIDIA's DGX Spark architecture, identical specification across the fleet. |
| Speed and concurrency | Depends on the specific listing's GPU and the host's network, neither of which GPUwerk can quote on TensorDock's behalf. | A single Spark, single-stream: gpt-oss-120b decodes at 33.5 tok/s and reaches 862.8 tok/s aggregate at 256 concurrent requests, per a third-party concurrency benchmark on comparable Spark hardware (Dendro Logic, not run by GPUwerk). Methodology and sourcing on our benchmarks page. |
| Pricing model | Set by each host, varying by GPU, demand, and listing; published on TensorDock's own marketplace pages and changing over time. | 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. |
| Worked cost example | See the full arithmetic below the table. | |
| Contracts and DPA | Governed by TensorDock's marketplace terms, layered on whatever the individual host's arrangement covers; check current terms closely. | 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 rented 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:
TensorDock. Priced per listing, set by each host, published on TensorDock's own marketplace pages. A fair dollar comparison needs a specific listing and its actual hourly rate, both of which vary by GPU and demand, so GPUwerk didn't invent a blended figure here. Check TensorDock's current listings for the GPU class you'd actually need for a real number.
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 operator. If you hold the reservation instead of running it, the held rate drops that to 730 × $0.59 = $430.70.
A marketplace can beat a fixed rate on price for the right listing at the right time, that's the point of a marketplace. What it can't offer is the certainty of who's running the hardware and where. Weigh the two against each other for your own risk tolerance, and look past the headline rate.
Migration path
Both are bare GPU instances, so migration is mostly re-deploying your own stack. Serve a model through vLLM on a Spark and a similarly configured TensorDock listing and both expose an OpenAI-compatible endpoint from vLLM itself, not from the underlying hardware provider. Put LiteLLM in front of either for request logging and key management. A model tuned for a specific card's VRAM layout on TensorDock may need re-testing on the Spark's unified 128GB pool, and vice versa.
When TensorDock is the right choice
- You want access to a wide range of GPU models and are comfortable evaluating individual listings.
- Marketplace pricing on the specific card you need beats a fixed EU-hosted rate.
- Data residency and knowing exactly which company operates the hardware aren't hard requirements.
When a dedicated Spark is the right choice
- You want to know exactly who operates the hardware and where it physically sits.
- A consistent specification across every node matters more than marketplace price variety.
- EU data residency and a standard GDPR DPA are requirements, not nice-to-haves.
FAQ
Is TensorDock a good alternative to GPUwerk for LLM hosting?
TensorDock is a GPU marketplace, matching renters to individual hosts' hardware, mostly consumer and prosumer cards rather than data-center-grade machines. Availability and specific GPU models vary by what hosts have listed at any given time. GPUwerk operates its own DGX Spark fleet directly, so every node is the same known hardware in the same EU location. If marketplace pricing and variety matter more to you than provenance, TensorDock is worth checking directly; if you want to know exactly whose hardware you're on and where it sits, that's the trade GPUwerk makes instead.
What's the difference between a DGX Spark and a TensorDock listing?
A TensorDock listing is a specific host's machine, with specs, location, and reliability that vary listing to listing; check the individual listing 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.
Is a DGX Spark cheaper than TensorDock?
It depends entirely on which listing you'd compare against, since TensorDock pricing is set by individual hosts and varies by GPU model, host, and demand at the time. A dedicated Spark costs $0.79/hour flat, a single known number. Marketplace listings can undercut that for lower-end cards or beat it during low-demand periods; check TensorDock's current listings for the GPU class you'd actually need before comparing.
Why choose a DGX Spark over TensorDock?
Mainly provenance and consistency. Every Spark GPUwerk rents out is hardware we operate directly, in a data center we control in Prague, at one published rate. A TensorDock listing is another party's machine, and the marketplace model means specs, uptime history, and support quality can differ listing to listing. If a specific, known, EU-hosted machine at a fixed rate matters more than marketplace price variety, the Spark fits better.
GPUwerk did not find a single TensorDock price that fairly represents every listing on their marketplace; check TensorDock's own site for the specific listing you'd rent. Spark throughput figures come from a third-party concurrency benchmark (Dendro Logic, not run by GPUwerk), detailed on our benchmarks page.