Private LLM hosting vs Lambda Labs
We rent DGX Sparks, so weigh that against everything below. Of every platform we've compared ourselves against, Lambda Labs is the closest to GPUwerk in shape: both are GPU rental, billed by the hour, no per-token pricing and no managed platform layer on top. What actually separates them is region, hardware range, and how simple the billing is once you've picked something. Lambda gives you a catalogue of GPU types across several locations; GPUwerk gives you one machine, one region, one rate.
Side by side
| Lambda Labs | Dedicated DGX Spark (GPUwerk) | |
|---|---|---|
| What it is | A GPU cloud provider: on-demand and reserved instances across a range of NVIDIA GPU types, rented by the hour or by longer-term reservation. | A GPU cloud provider too, but with one hardware option: a dedicated DGX Spark, rented by the hour. |
| Region | Multiple data centers; check Lambda's own region list for current locations and availability, which changes as they add capacity. | EU-Central (Prague), exclusively. One location, no region selection to get wrong. |
| Hardware choice | A range of NVIDIA GPU instance types and sizes, from single-GPU to multi-GPU clusters, per Lambda's own catalogue. | One machine: the DGX Spark, 128GB unified memory, with an optional two-node cluster for larger workloads. |
| Who can see your data | Governed by whichever terms and data processing addendum Lambda offers for your account and region; check Lambda's own 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. Larger multi-GPU instances can run bigger models or serve higher concurrency than a single Spark. | Whatever you deploy too, but bounded by 128GB unified memory: gpt-oss-120b, Llama 3.3 70B, Qwen3-Coder 30B, DeepSeek, and similar fit; models that need substantially more memory or multi-node sharding don't, without moving to the two-node cluster. |
| Speed and concurrency | Depends entirely on the GPU type and count you rent; Lambda publishes instance specs but the achievable 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 | Per hour, varying by GPU type and instance size, published on Lambda's own pricing page. Reserved and longer-term rates typically run lower than on-demand. | 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 Lambda's own customer agreement and data processing terms; check Lambda's current legal pages for the specifics that apply to your account. | 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: OS, model server, monitoring, backups. Lambda manages the physical hardware and network underneath. | The same: root SSH access to your own container, the model server, any RAG or agent layer, monitoring, and 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 generates 500 million output tokens a month, roughly what a busy internal support bot or a document-processing pipeline running continuously might produce. Two ways to serve it:
Lambda Labs. Instance pricing runs per GPU-hour and varies by card, from smaller single-GPU instances up to multi-GPU clusters, published on Lambda's own pricing page. A fair comparison needs a specific instance 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 hourly rate for the instance you'd actually use from Lambda's site and benchmark your model on it for a real number.
Dedicated Spark. gpt-oss-120b reaches 862.8 tok/s aggregate at 256 concurrent requests, per Dendro Logic's concurrency benchmark on a single Spark (cited on our benchmarks page). That is 862.8 × 3,600 = 3,106,080 tokens per hour if the node stays saturated at that concurrency the whole time. 500,000,000 ÷ 3,106,080 ≈ 161 hours. At $0.79/hour, 161 × $0.79 = $127.19, before tax.
That Spark figure only holds under the assumption stated: the node is busy at 256 concurrent requests for the full 161 hours, back to back. A larger multi-GPU Lambda instance could plausibly clear the same 500 million tokens in fewer wall-clock hours at a higher hourly rate, so whether it's cheaper overall depends on the instance's price-per-token-per-hour relative to a Spark's, not just its sticker price. Run both through your own model and traffic pattern; a raw GPU-hour comparison without a specific instance type and a specific tok/s figure isn't one either provider can hand you.
Migration path
Both are bare GPU instances, so migration is mostly re-deploying your own stack. A model served through vLLM on a Spark and the same setup on a Lambda instance 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 hardware spec underneath: a model tuned to a multi-GPU Lambda instance's memory and bandwidth may need re-quantizing or a smaller variant to fit a single Spark's 128GB, and vice versa if you're moving up from a Spark to a larger Lambda cluster.
When Lambda Labs is the right choice
- You need a GPU type or memory footprint beyond a single Spark's 128GB, larger models, higher-precision training, or a multi-GPU cluster.
- Your team is US-based or doesn't need EU data residency as a default, and Lambda's region and instance range fit your infrastructure better.
- You want the flexibility to switch GPU types between workloads rather than committing to one machine spec.
When a dedicated Spark is the right choice
- EU data residency by default matters more than hardware flexibility, one region, no configuration to get wrong.
- You want a single, fixed hourly rate and don't want to compare instance types and their per-GPU pricing before you can even start.
- Your model fits comfortably in 128GB and you'd rather not pay for headroom you won't use.
FAQ
Is Lambda Labs similar to GPUwerk?
Yes, of the platforms compared on this site, Lambda Labs is the closest in shape. Both rent GPU hardware by the hour rather than selling a per-token API or a managed platform. The differences are region (Lambda's cloud instances run in US and select international data centers; GPUwerk runs one location, EU-Central in Prague), hardware (Lambda offers a range of NVIDIA GPU instance types; GPUwerk offers one machine, the DGX Spark), and contract terms (GPUwerk's DPA and terms are written for EU/GDPR customers by default).
Does Lambda Labs have an EU region?
Check Lambda's own region list on their site for current availability, it changes as they add capacity. Where Lambda does offer non-US regions, GDPR obligations still depend on the specific data processing terms Lambda offers for that region and your own assessment of them, the same as with any provider, US or EU headquartered. GPUwerk's Spark fleet runs exclusively from EU-Central (Prague), so there's no region selection to get right or wrong.
What models can I run on Lambda Labs vs a Spark?
Both are raw GPU rental, so the model is entirely up to you, whatever fits the hardware and its memory. Lambda's larger instance types (multi-GPU H100 or similar) can run bigger models or serve more concurrent requests than a single Spark's 128GB unified memory allows. A Spark's ceiling is fixed at 128GB; Lambda's ceiling depends on which instance type you rent.
Is a DGX Spark cheaper than Lambda Labs?
It depends on the instance type and region you compare. In the worked example on this page, 500 million generated tokens a month costs about $127 in Spark compute at $0.79/hour, assuming the node sustains 256 concurrent requests the entire time. Lambda Labs publishes hourly rates by GPU type on its own pricing page; those vary by card and instance size, so check the current rate for the instance you'd actually use before comparing directly.
Why choose a Spark over a Lambda GPU instance if they're the same shape of product?
Mainly region and simplicity. If EU data residency and a GDPR-native DPA matter more than instance-type flexibility, a Spark's fixed EU-Central location and one flat rate remove a decision Lambda's broader instance catalogue requires you to make. If you need a specific GPU type, more memory than 128GB, or a US region, Lambda's range is the better fit.
GPUwerk did not find a single Lambda Labs instance-hour price that fairly represents every GPU type in their catalogue; check Lambda's own pricing page for the instance you'd run. Spark throughput figures come from Dendro Logic's concurrency benchmark, detailed on our benchmarks page.