Private LLM hosting vs Novita AI
We rent DGX Sparks, so weigh that against everything below. Novita AI runs both a per-token model API for popular open-weight models and a GPU cloud where you rent discrete GPU instances by the hour and manage them yourself. That dual structure means Novita can sit on either side of a GPUwerk comparison: as a metered API alternative, or as a dedicated-compute alternative. A DGX Spark is a single product, one dedicated machine with 128GB unified memory, rented by the hour with no card or plan to choose.
Side by side
| Novita AI | Dedicated DGX Spark (GPUwerk) | |
|---|---|---|
| What it is | A combined platform: a per-token API for hosted open models, plus a rentable GPU cloud for self-managed instances. | A single-purpose GPU host: one dedicated DGX Spark, rented by the hour, built around unified memory for inference. |
| Region | Determined by Novita's own data center footprint; check their site for current availability by product. | EU-Central (Prague), exclusively. One location, no region selection to get wrong. |
| Infrastructure model | Shared for the API tier, since requests hit hosted model endpoints; dedicated for the GPU cloud tier, where you rent the whole instance. | Always single-tenant. One Spark, allocated to one customer, regardless of which product you'd otherwise compare it to. |
| Who can see your data | Governed by Novita's own terms and privacy policy; check their current legal pages for retention and processing details, especially for the API tier. | 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." |
| Model choice | API tier limited to Novita's hosted catalogue; GPU cloud tier unrestricted, whatever you deploy on the rented instance. | Whatever you deploy, bounded by 128GB unified memory: gpt-oss-120b, Llama 3.3 70B, Qwen3-Coder 30B, DeepSeek, and similar fit. |
| Speed and concurrency | API tier throughput depends on Novita's own serving capacity; GPU cloud throughput depends on the card rented. Neither is something GPUwerk can quote on Novita'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 (Dendro Logic's concurrency benchmark, run on their own Spark). Raw logs and methodology on our benchmarks page. |
| Pricing model | Per million tokens for the API tier, per GPU-hour for the cloud tier, both published on Novita's own pricing page and varying by model or card. | 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 Novita's own customer agreement; check their legal pages for jurisdiction and data-processing 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 | Nothing on the API tier; everything on the GPU cloud tier, same as any rented instance. | Everything: 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:
Novita AI. On the API tier, billed per token, priced per model on Novita's own site; on the GPU cloud tier, billed per hour, priced per card. A fair dollar comparison needs a specific product choice, model, or card, and the rate that applies changes over time, so GPUwerk didn't invent a blended figure here. Pull the current rate for the product and model or card you'd actually use from Novita's pricing page for a real number.
Dedicated Spark. At $0.79/hour, running continuously for a 730-hour month costs 730 × $0.79 = $576.70, flat, regardless of model, card, or token volume. If you hold the reservation instead of running it, the held rate drops that to 730 × $0.59 = $430.70.
Which side wins depends entirely on which Novita product you'd otherwise pick and how continuously you'd run it. A metered API tends to suit low or spiky volume; a rented GPU cloud instance competes on hourly rate and throughput for the specific card. Compare against the specific Novita product you'd actually use, not a blended average.
Migration path
If you're moving off Novita's API tier, deploy the same open model behind vLLM on a Spark; the OpenAI-compatible endpoint shape is usually close enough that client code needs only a base-URL change. If you're moving off Novita's GPU cloud tier, migration is mostly re-deploying your existing serving stack onto the Spark, since both are bare rented instances you operate yourself. Put LiteLLM in front of either path for request logging and key management. A model tuned for a discrete-GPU card's VRAM layout may behave differently on the Spark's unified 128GB pool, so re-test throughput after moving.
When Novita AI is the right choice
- You want to try multiple GPU classes or model sizes without committing to one hardware type.
- Low or spiky volume makes a per-token API tier cheaper than a flat hourly rate.
- You need a wider catalogue of hosted models than a single dedicated node would run.
When a dedicated Spark is the right choice
- You want one product, one flat rate, and no card or plan decision to make first.
- Your workload is steady enough that a dedicated node beats metered pricing.
- Your data can't sit on shared infrastructure at any point in the pipeline, even for an API call.
FAQ
Is Novita AI a good alternative to GPUwerk for LLM hosting?
It depends on which part of Novita you'd use. Novita offers both a per-token model API and rentable GPU cloud instances, so it can compete with GPUwerk on either a shared-API basis or a dedicated-compute basis depending on what you pick. GPUwerk offers only the dedicated path: one physical DGX Spark, rented by the hour, that nobody else touches. If you want a rented GPU instance you configure yourself, compare Novita's GPU cloud tier against a Spark directly; if you want an API, compare against Novita's model endpoints instead.
What's the difference between a DGX Spark and Novita's GPU cloud instances?
Novita's GPU cloud rents conventional discrete GPU cards, check their current lineup for available models and VRAM. A DGX Spark uses NVIDIA's unified memory architecture, 128GB shared between CPU and GPU, purpose-built for LLM inference rather than general GPU compute. Both are dedicated-instance products in the sense that you get exclusive use of the rented hardware; the memory architecture and the target workload differ.
Is a DGX Spark cheaper than Novita AI?
It depends on which Novita product and GPU tier you'd compare against, and Novita publishes its own current rates for both the API and the GPU cloud on its pricing page. A dedicated Spark costs $0.79/hour flat regardless of model or GPU class chosen; Novita's GPU instances are priced per card and per-token API pricing is priced per model. Pull the current numbers for the specific product you'd use before comparing directly.
Why choose a DGX Spark over Novita AI?
Mainly a simpler decision and EU-only hosting. GPUwerk has one product: a dedicated Spark at one flat rate, hosted exclusively in Prague. Novita spans multiple products and GPU tiers with pricing and availability that vary by choice. If you want a single straightforward option with no card selection and EU data residency, the Spark fits; if you need a wider range of GPU classes or want to stay on a per-token API, Novita's broader catalogue may serve you better.
GPUwerk did not find a single Novita AI price that fairly represents every model or GPU tier in their catalogue; check Novita's own pricing page for the product you'd use. Spark throughput figures come from Dendro Logic's concurrency benchmark, detailed on our benchmarks page.