Private LLM hosting vs Latitude.sh
We rent DGX Sparks, so weigh that against everything below. Latitude.sh is a bare metal cloud provider: rack-mounted servers you provision on demand, with GPU-equipped tiers alongside their standard compute and storage lineup. It's built for teams that want bare metal performance without owning the hardware, across a range of workloads, not inference specifically. A DGX Spark is narrower by design, one machine, 128GB unified memory, built around running LLM inference.
Side by side
| Latitude.sh GPU servers | Dedicated DGX Spark (GPUwerk) | |
|---|---|---|
| What it is | General-purpose bare metal cloud, with GPU-equipped server tiers as part of a much broader catalogue. | A single-purpose GPU host: one dedicated DGX Spark, rented by the hour, built around unified memory for inference. |
| Region | Multiple data centers across the Americas and Europe, per Latitude.sh's own current location list; check their site for GPU availability by region. | EU-Central (Prague), exclusively. One location, no region selection to get wrong. |
| Memory architecture | Conventional discrete GPU cards on bare metal servers; exact configuration varies by server type, check Latitude.sh's current specs. | 128GB unified memory shared between CPU and GPU, NVIDIA's DGX Spark architecture, suited to large quantized models without splitting memory across pools. |
| Who can see your data | Governed by Latitude.sh's own terms and data processing addendum; check their legal pages for the current text. | 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 | Whatever you deploy, unrestricted by the provider. Servers with more raw VRAM per card can suit models tuned for that memory layout. | Whatever you deploy too, bounded by 128GB unified memory: gpt-oss-120b, Llama 3.3 70B, Qwen3-Coder 30B, DeepSeek, and similar fit. |
| Speed and concurrency | Depends on the GPU card and server tier rented; Latitude.sh publishes server specs but tok/s for a given model is yours to benchmark. | 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 | Typically monthly for bare metal servers, sometimes hourly for on-demand tiers, varying by card and configuration, published on Latitude.sh's own pricing page. | 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 Latitude.sh's own customer agreement and data processing terms; check the specifics against your own requirements. | 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 server: OS, model server, monitoring, backups. Latitude.sh manages the physical hardware and network underneath. | 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:
Latitude.sh. Bare metal GPU server pricing usually runs monthly, sometimes hourly for on-demand tiers, varying by card, published on Latitude.sh's own pricing page. A fair comparison needs a specific server type and its measured tok/s for the model you'd run, both of which depend on your choice, so GPUwerk did not invent a blended figure here. Pull the current rate for the server you'd actually use from Latitude.sh's site and benchmark your model on it for a real number.
Dedicated Spark. At $0.79/hour, running continuously for a 730-hour month costs 730 × $0.79 = $576.70. If you hold the reservation instead of running it, the held rate drops that to 730 × $0.59 = $430.70.
Bare metal monthly pricing can undercut an hourly rate for a workload that runs the full month regardless, since providers often price monthly commitments below the equivalent hourly-times-hours math. The Spark's advantage is billing per minute rather than committing to a term: if your workload is intermittent, you're not paying for a full month you don't use. Compare the actual server tier's monthly rate against your expected usage pattern before deciding.
Migration path
Both are bare metal or bare-instance GPU machines, so migration is mostly re-deploying your own stack. A model served through vLLM on a Spark and the same setup on a Latitude.sh server expose an identical API shape if you run the same serving software on both, the OpenAI-compatible endpoint comes from vLLM, not from the underlying hardware provider. Put LiteLLM in front of either for request logging and key management. What changes is the memory layout underneath: a model tuned for a discrete-GPU card's VRAM may behave differently on the Spark's unified 128GB pool, and vice versa, so re-test throughput after moving either direction.
When Latitude.sh is the right choice
- You need bare metal for workloads beyond inference and want one provider for all of it.
- A specific discrete GPU card with more raw VRAM per card suits your model better than unified memory.
- Your workload runs steadily enough that a monthly bare metal commitment beats hourly billing.
When a dedicated Spark is the right choice
- Your workload is LLM inference and a large unified-memory pool fits how you'd quantize and serve the model.
- You want per-minute billing with no monthly term to commit to.
- Your model fits comfortably in 128GB and you'd rather not configure a general-purpose server for a specific job.
FAQ
Is Latitude.sh a good alternative to GPUwerk for LLM hosting?
Latitude.sh is a bare metal cloud provider with GPU server options among its broader server catalogue, so it's a reasonable option if you want general-purpose bare metal with a GPU attached and are comfortable choosing the server spec yourself. GPUwerk offers one product built specifically for LLM inference: a dedicated DGX Spark with 128GB unified memory. If you need bare metal for a range of workloads and GPU inference is just one of them, Latitude.sh's catalogue may fit better; if inference is the whole job, the Spark is purpose-built for it.
What's the difference between a DGX Spark and a Latitude.sh GPU server?
Latitude.sh's GPU servers are general bare metal machines with a discrete GPU card attached, check their current lineup for available models and specs. A DGX Spark is a purpose-built machine with 128GB unified memory shared between CPU and GPU, NVIDIA's architecture aimed specifically at inference on large quantized models. Both give you full control of the machine; the memory architecture and the target workload differ.
Is a DGX Spark cheaper than Latitude.sh?
It depends on the server configuration you'd compare it against. Latitude.sh publishes its own current pricing by server type on its site; a dedicated Spark costs $0.79/hour flat, one known number regardless of workload. Pull the current monthly or hourly rate for the specific GPU server tier you'd actually use before comparing directly, bare metal pricing usually runs monthly rather than hourly.
Why choose a DGX Spark over Latitude.sh?
Mainly the memory architecture and per-hour billing. If your workload is inference on a model that fits 128GB unified memory, the Spark is built for exactly that, and you pay by the hour rather than committing to a monthly bare metal term. If you need a different GPU class, more raw VRAM, or want a general-purpose bare metal server for workloads beyond inference, Latitude.sh's broader catalogue may serve you better.
GPUwerk did not find a single Latitude.sh GPU server price that fairly represents every card in their catalogue; check Latitude.sh's own pricing page for the server you'd run. Spark throughput figures come from Dendro Logic's concurrency benchmark, detailed on our benchmarks page.