Private LLM hosting vs JarvisLabs
We rent DGX Sparks, so weigh that against everything below. JarvisLabs operates its own GPU instances directly, which makes it a single-fleet provider in the same category as GPUwerk rather than a marketplace of third-party operators. The difference is what each is built for. JarvisLabs is aimed at individual ML practitioners and small teams who want a GPU notebook or training instance up in seconds for an experiment, then torn down. GPUwerk rents a single dedicated Spark per customer, meant to run continuously as a private LLM host with EU data residency and a formal DPA as the default rather than an add-on.
Side by side
| JarvisLabs | Dedicated DGX Spark (GPUwerk) | |
|---|---|---|
| What it is | A GPU cloud operating its own instances directly, built around fast spin-up for notebooks, fine-tuning, and individual experimentation. | A single-purpose GPU host: one dedicated DGX Spark, owned and operated by GPUwerk, rented by the hour. |
| Who operates the hardware | JarvisLabs directly; it's a single-fleet operator, not a marketplace. | GPUwerk directly. The company you're paying is the company running the machine. |
| Primary use case | Short to medium-length sessions: notebooks, fine-tuning jobs, individual experimentation with fast start and stop. | Continuous, dedicated hosting for a private LLM stack a team runs day to day. |
| Region | Check JarvisLabs' current site for available data center regions; confirm EU availability directly if data residency matters to you. | EU-Central (Prague), exclusively. One location, no region selection to get wrong. |
| Who can see your data | Governed by JarvisLabs' own published terms and privacy policy; review their current documents for specifics. | 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 GPU model and instance size you select at launch. | Every node is the same: 128GB unified memory, NVIDIA's DGX Spark architecture, identical specification across the fleet. |
| Pricing model | Per GPU model, billed by the hour or minute; check JarvisLabs' current pricing page for the instance type 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 | Review JarvisLabs' current published terms for whether a formal DPA is offered by default or on request. | 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: environment, model server, monitoring, backups, typical of a self-managed 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 private inference workload that needs to run continuously across a full month, not a few hours of experimentation. Two ways to serve it:
JarvisLabs. Priced per GPU model and billed by usage, published on JarvisLabs' own pricing page and varying by the instance type you'd choose. A fair comparison needs their current rate for a comparably specced instance, so GPUwerk didn't invent a blended figure here. Check JarvisLabs' current site for that number, and note that JarvisLabs is optimized for short sessions rather than always-on hosting, so per-hour economics for month-long usage may not be its strongest fit.
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 quick, disposable experimentation, JarvisLabs' fast spin-up is a real advantage a fixed dedicated machine doesn't try to match. For a workload that stays up all month as a private hosting endpoint, a fixed rate and a formal EU DPA by default are the more relevant differences.
Migration path
Both expose a Linux GPU instance, so migration is mostly re-deploying your own stack. Serve a model through vLLM on a Spark and on a JarvisLabs instance, 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. Moving from a notebook-style JarvisLabs workflow to a Spark mostly means switching from an interactive session to a long-running service process.
When JarvisLabs is the right choice
- You want a GPU instance up in seconds for a notebook, fine-tuning job, or short experiment.
- Your usage pattern is bursty rather than continuous, and per-session flexibility matters more than a fixed monthly cost.
- A formal EU DPA and dedicated always-on hosting aren't requirements for this particular workload.
When a dedicated Spark is the right choice
- Your workload runs continuously as a private hosting endpoint rather than a short session.
- EU data residency and a standard GDPR DPA by default are requirements.
- You want one fixed, published hourly rate rather than picking among GPU model and instance size options.
FAQ
Is JarvisLabs a good alternative to GPUwerk for LLM hosting?
JarvisLabs operates its own GPU instances directly, so it's a single-fleet provider like GPUwerk rather than a marketplace. It's built primarily for individual ML practitioners and small teams running notebooks, fine-tuning jobs, and short-lived experiments, with fast spin-up as the main selling point. GPUwerk is built around a single dedicated machine per customer for ongoing private LLM hosting, with EU-only data residency and a standard DPA as the default. If you want a fast, disposable GPU for an afternoon of experimentation, JarvisLabs fits that use case well. If you want one dedicated, EU-hosted machine you keep running for a private inference workload with a formal DPA, GPUwerk fits better.
What's the difference between a DGX Spark and a JarvisLabs instance?
A JarvisLabs instance is a GPU VM sized and priced per GPU model, aimed at quick notebook and training sessions; check JarvisLabs' current site for available GPU types 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, intended to run continuously as a dedicated private LLM host rather than a short session.
Is a DGX Spark cheaper than JarvisLabs?
It depends on GPU model and how long you actually run the instance, since JarvisLabs prices per GPU type and JarvisLabs' own site has the current numbers. A dedicated Spark costs $0.79/hour flat, one known number for one fixed specification. For a workload that runs most of the month rather than a few hours here and there, compare that flat rate against JarvisLabs' published per-GPU rate for a similarly sized instance.
Why choose a DGX Spark over JarvisLabs?
Mainly data residency, a formal DPA, and fit for an always-on private hosting workload rather than short experimentation sessions. A Spark runs in Prague under one published rate and a standard GDPR Article 28 DPA available without negotiation. JarvisLabs is optimized for fast individual iteration; if your priority is a dedicated EU machine you run continuously for a team's private AI stack, a Spark is the better fit.
GPUwerk did not find a single JarvisLabs price representative of every GPU model and instance size; check JarvisLabs' own pricing page. GPUwerk's own figures ($0.79/hour, $0.59/hour, $576.70/month) come from our published pricing.