Private LLM hosting vs Cerebras
We rent DGX Sparks, so weigh that against everything below. Cerebras isn't a GPU rental company either: it builds its own wafer-scale chip, the CS system, and mostly sells inference speed on a set of supported open-weight models through an API, not a rented machine you configure yourself. That makes this a comparison between a GPU you operate and an API you call, both aimed at running LLMs fast, but built on completely different premises about who runs the hardware.
Side by side
| Cerebras | Dedicated DGX Spark (GPUwerk) | |
|---|---|---|
| What it is | A managed inference API running on Cerebras's own wafer-scale accelerator, not rented general-purpose GPU hardware. | A dedicated NVIDIA DGX Spark you rent by the hour and access with root SSH. |
| What you control | Whichever models Cerebras exposes through its API; check their current site for the supported model list, since this changes over time. | Everything on the machine: OS, model server, any open-weight model or your own fine-tune, monitoring, backups. |
| Where it runs | Cerebras's own data centers; confirm current facility locations and any EU presence directly on their site. | EU-Central (Prague), on one dedicated machine, no region choice because there's only the one. |
| Who can see your data | Governed by Cerebras's own API terms and data handling policy for requests sent through their service; review those directly. | 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. |
| Pricing model | Typically priced per token processed through the API; published rates change over time, check Cerebras's current pricing page. | One flat hourly rate regardless of request volume: $0.79/hour on-demand, $0.59/hour held. Per pricing. |
| Raw inference speed | Cerebras's wafer-scale architecture is built specifically for very high single-request token throughput on supported models; check their own published benchmarks for current figures, since GPUwerk hasn't independently tested Cerebras hardware. | A single Spark, single-stream: gpt-oss-120b decodes at 33.5 tok/s per a third-party concurrency benchmark on comparable hardware (Dendro Logic, not run by GPUwerk). See benchmarks. Not built to compete on raw single-request speed against purpose-built inference silicon. |
| Operational overhead | Minimal: call the API, get a response, no infrastructure to run. | You run the model server, updates, and monitoring yourself. More control, more responsibility. |
The cost shape, not a head-to-head number
Because Cerebras prices by tokens and a Spark prices by the hour, there's no single fair dollar figure, so we're not inventing one. A Spark running spark-1x continuously for a full month costs 730 hours × $0.79/hour = $576.70, flat, per pricing, regardless of tokens processed. That favors steady, high-volume usage. Token-metered pricing like Cerebras's favors bursty or low-volume usage, or workloads where raw per-request speed on a supported model matters more than owning the machine. Check Cerebras's current per-token rate for the model you'd use and run your own expected volume through both before deciding.
Migration path
There's no direct hardware migration here, since Cerebras doesn't expose the underlying wafer-scale chip for arbitrary deployments. What moves is the application layer: if your app calls an OpenAI-compatible endpoint, pointing it at a Spark running vLLM instead of Cerebras's API is largely a base-URL and model-name change, assuming Cerebras's API is OpenAI-compatible for your use case, which you should confirm on their docs.
When Cerebras is the right choice
- Raw per-request inference speed on a supported open-weight model matters more than owning the hardware.
- You want to call an API rather than operate any infrastructure at all.
- Your usage is bursty or unpredictable, so per-token pricing suits your traffic better than a flat hourly rate.
When a dedicated Spark is the right choice
- You want to run your own open-weight model or fine-tune, not a fixed menu of hosted models.
- You want root access to the machine and full control over the stack, not an API you call.
- Your usage is steady enough that a flat hourly rate beats per-token pricing, or EU data residency and a standard DPA are requirements.
FAQ
Is Cerebras a GPU rental service like GPUwerk?
No. Cerebras designs its own wafer-scale chips, the CS system, and mainly sells fast inference on a set of supported models as a managed API rather than renting general-purpose GPU hardware you control. GPUwerk rents a dedicated NVIDIA DGX Spark you access with root SSH. Both end up serving LLM requests but they're structurally different products.
Can I deploy my own model on Cerebras hardware?
Cerebras's inference API has historically focused on a defined set of supported open-weight models rather than arbitrary customer deployments; check their current site for exactly what's on offer, since this changes. On a Spark, you have root access and can run any open-weight model or your own fine-tune.
Is Cerebras cheaper than a Spark?
The two are priced on different bases, so a direct comparison is misleading. Cerebras has generally priced its inference API by tokens processed; check their current pricing page for a number. A Spark is a flat $0.79/hour regardless of token volume, which can be cheaper at high, steady request volume and more expensive if usage is light.
Why would I choose a dedicated Spark over Cerebras's API?
Mainly control and data handling. A Spark is hardware you operate yourself, with root access, in an EU data center, running whatever model and stack you choose. Cerebras is a managed API on proprietary wafer-scale silicon: very fast for supported models, but you're calling their service rather than running your own machine.
GPUwerk's own figures on this page ($0.79/hour, $0.59/hour, $576.70/month, 33.5 tok/s) come from our published pricing and a third-party concurrency benchmark on comparable Spark hardware (Dendro Logic, not run by GPUwerk), detailed on our benchmarks page. Cerebras's per-token pricing and throughput figures are not something GPUwerk can verify or restate; check Cerebras's own site directly.