Private LLM hosting vs self-managing Ollama on your own hardware
We rent DGX Sparks, so weigh that against everything below. This page is different from the rest of this series: it's not comparing GPUwerk to another vendor's API, it's comparing GPUwerk to not using a vendor at all. If you already own or can buy GPU hardware and you're willing to run Ollama on it yourself, that setup is exactly as private as a Spark. Nobody outside your organisation touches the data either way. The honest reason to rent from GPUwerk instead isn't privacy, it's that we carry the operational burden you'd otherwise carry yourself.
Side by side
| Self-managed Ollama, your hardware | Dedicated DGX Spark (GPUwerk) | |
|---|---|---|
| Who can see your data | Nobody outside your organisation. The machine is yours, on your premises or your own cloud account. | 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." Equivalent outcome, different mechanism: you get it by ownership, a Spark gets it by contract. |
| Where data lives | Wherever you put the machine. Full control, no vendor's region choices involved. | EU-Central (Prague). Fixed to that region; you don't choose, but you also don't have to build the location out yourself. |
| Upfront cost | Buying hardware capable of running a 70B-class model comfortably is a real capital outlay; check current prices for the GPU and memory configuration you'd need. | None. $0.79/hour on-demand, no hardware purchase, per pricing. |
| Driver and firmware updates | Your responsibility, on your schedule, and a broken driver update on a production box is your outage to fix. | GPUwerk's responsibility for the underlying node; your container's software stack is still yours to maintain. |
| Uptime and hardware failure | If the GPU dies, the fan fails, or the power supply goes, that's your hardware to diagnose, source a replacement for, and physically service, on your timeline. | GPUwerk's fleet responsibility. A failed node is GPUwerk's problem to replace, not yours to physically fix. |
| Elastic scaling | Bounded by what you've bought. Scaling up means buying more hardware and waiting for it to arrive. | Rent a second Spark, or a two-node 256 GB cluster at $1.79/hour, in minutes, no procurement lead time. |
| Speed | Depends entirely on the hardware you buy; could be faster or slower than a Spark depending on your budget and choices. | Known, published numbers on the fixed Spark hardware: gpt-oss-120b decodes at 33.5 tok/s single-stream, Qwen3-Coder 30B-A3B AWQ at 80.9 tok/s. Full methodology on our benchmarks page. |
| Ongoing cost past the hardware | Power, cooling, physical space, and your own time. Real costs that don't show up on a spec sheet and that GPUwerk has not modelled generally since they vary by setup. | All bundled into the hourly rate. No separate power bill or facilities line item to track. |
The part most vendor comparisons skip
Every other page in this series argues that a dedicated Spark beats a shared API because nobody else sees your prompts. That argument doesn't work here, because self-managed Ollama on hardware you own already has nobody else in the loop. If privacy alone is the deciding factor and you have the capacity to run GPU hardware yourself, this is the option that gets you there without paying anyone, GPUwerk included.
What you're actually buying from GPUwerk in this comparison is not privacy, it's the removal of operational burden: someone else deals with driver updates, someone else replaces a dead GPU, someone else sits on call for a failed node. That's a real trade with a real cost, $0.79 an hour adds up, and it's worth being upfront that plenty of teams with existing ops capacity and a GPU budget would come out ahead buying and running the hardware themselves rather than renting indefinitely. If you're already staffed to run production infrastructure and this would just be one more box in the rack, buying is a legitimate answer.
Where renting tends to win is teams without that spare ops capacity, or without certainty about how long they'll need the capability, since a Spark can be started and stopped without a hardware decommissioning problem at the end.
A rough cost framing
This comparison doesn't reduce to a single worked number the way the API comparisons on this site do, because the self-managed side depends on hardware you might already own, a purchase you're weighing, and how much ops time you're willing to spend, none of which GPUwerk can price for you. The useful framing is a break-even question instead: at $0.79/hour, a Spark costs roughly $576 a month run continuously (730 × $0.79), or far less if you only run it while working. Compare that monthly figure against your own hardware's amortised purchase cost plus power, cooling, and the value of the ops time you'd spend keeping it healthy, over however long you expect to need the capability. Short or uncertain timelines favour renting; long, well-staffed timelines favour buying.
Migration path
This is the most portable comparison on this site in both directions, because Ollama itself runs the same way on a Spark as it does on your own hardware. GPUwerk's setup guide covers installing Ollama on a Spark; the same commands and the same model files work identically on hardware you own. Moving from one to the other is mostly copying model files and pointing your application at a new address, not rewriting anything.
When self-managing your own hardware is the right choice
- You already run GPU infrastructure and have the ops capacity to maintain more of it without meaningful extra strain.
- Your need for the capability is long-term and stable enough to justify a capital purchase.
- You want zero recurring vendor relationship of any kind, including GPUwerk's.
When a dedicated Spark is the right choice
- You want the privacy outcome without the driver updates, hardware failures, and on-call burden that come with owning the box.
- Your need is uncertain in duration, or you want to test a workload before committing capital.
- You want elastic capacity, a second Spark or a cluster, without a procurement cycle.
FAQ
Is running Ollama on my own hardware more private than renting a Spark?
On the narrow question of who else can access the data, they're equivalent: neither has a third party in the loop while the model runs, since you either own the box or GPUwerk's terms commit to not accessing your instance's content. The difference is not privacy, it's who does the operational work of keeping the machine running, patched, and available.
What hardware do I need to self-host Ollama for a 70B model?
Enough memory to hold the model's weights plus context, which for a 70B model at typical quantisation is in the range GPUwerk's own DGX Spark targets (128 GB unified memory). Consumer GPUs with 24 GB of VRAM generally can't hold a 70B model without heavy quantisation or CPU offload; see /dgx-spark/ for the specs GPUwerk uses as its own baseline, and check current requirements against whichever quantised build you plan to run.
What's the actual ongoing cost of self-managing GPU hardware?
Beyond the upfront hardware purchase, it's driver and firmware updates, power and cooling, physical space, monitoring for failures, and your own time when something breaks at 2am. None of that shows up on a spec sheet, and GPUwerk has not built a general cost model for it since it varies enormously by how much of that work you already do for other systems.
Why would I rent a Spark instead of just buying the hardware?
Mainly to avoid the operational burden: driver updates, uptime, hardware failure, and no elastic scaling are all GPUwerk's problem instead of yours on a rented Spark. If you already run GPU hardware for other purposes and have the ops capacity, buying may work out cheaper over time; run the comparison against your own numbers rather than assuming either answer.
Spark figures come from our benchmarks page and our pricing page. No general cost model exists for self-managed hardware because it depends entirely on what you already own and staff; the framing above is deliberately a starting question, not a fixed number.