Cirrascale Cloud Services
Cirrascale Cloud Services (Cirrascale Corporation) · Coding
Cirrascale Cloud Services is a private AI cloud that rents dedicated GPU servers, from a single Qualcomm card to eight NVIDIA B200s, on flat monthly terms with no data transfer fees.
About Cirrascale Cloud Services
What Is Cirrascale Cloud Services
Cirrascale Cloud Services sells dedicated accelerator capacity to teams that need AI training infrastructure, fine-tuning capacity, or model serving without a hardware purchase. It's the cloud arm of Cirrascale Corporation, a company that spent years building multi-GPU blade and rackmount systems before it started renting them out by the month.
The pitch is control. You get a whole server with root access, not a slice of a shared machine, and the hardware spans NVIDIA, AMD, Qualcomm, and Tenstorrent. That matters when a workload is tied to one vendor's toolchain or when a buyer wants more room to negotiate on price.
The tradeoff is commitment. Cirrascale doesn't sell GPU time by the hour, so you're renting a machine for at least a month. Teams that need a two-hour experiment should look at per-minute clouds instead.
The company also runs an inference business: a managed AI inference platform, a private Gemini endpoint delivered on Google Distributed Cloud, and hosted endpoints for Ai2 open models. Public sector buyers get access through Google GPAR services.
Getting Started
- Submit a request through the Get Started form or contact sales with your model size, framework, and target timeline.
- Pick an accelerator and a term length. Monthly, 3-month, 6-month, and annual pricing are published per server.
- Approve the quote and complete onboarding, including any security review your organization requires.
- Provision the instance, attach NVMe or object storage, and connect over the network or InfiniBand for clustered jobs.
- Move your training or inference workload onto the machine and manage it with your own stack, or hand operations to Cirrascale's managed services team.
Product Information
A quick look at Cirrascale Cloud Services's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- ML teams with a multi-month training roadmap
- Startups that need eight high-end GPUs without a capital purchase
- Enterprises with data residency rules
- Inference teams serving steady traffic
Tasks
- Large model pretraining and fine-tuning
- Model serving and retrieval pipelines
- Framework portability testing
- Regulated inference rollouts
Scenarios
- A research group that needs 16 or more H100 nodes for a two-month run and wants the cluster online without building it.
- A computer vision company storing millions of small image files and hitting storage bottlenecks on general purpose cloud file systems.
- A team comparing GPU cloud pricing across vendors before it signs an annual capacity deal.
Key features
Bare Metal GPU Servers Across Four Chip Vendors
Every instance is a dedicated server, not a virtual slice, and the catalog runs from 8x NVIDIA RTX A4000 at the low end to 8x NVIDIA B200, 8x AMD MI300X, and Qualcomm Cloud AI 100 configurations. You pick the silicon that fits your framework instead of bending the workload to one vendor. Tenstorrent Galaxy nodes round out the list for teams testing non-GPU accelerators.
Clustered Networking for Multi-Node Training
Any NVIDIA H100 rental starts with a single node on 25Gb bonded networking, and clustered configurations move to 3200Gb InfiniBand with up to 3200Gb of available bandwidth. Cirrascale quotes clustered H100 capacity at as low as $2.49 per GPU hour for 16-node and larger deployments. That's the part of a GPU cloud that decides whether a 70B parameter run finishes in weeks or stalls on interconnect.
Flat Monthly Billing Without Transfer Fees
GPU cloud pricing here is per server, per month, with 3-month, 6-month, and annual terms that cut the effective rate. There are no ingress or egress charges, which changes the math for teams moving terabyte-scale datasets or serving heavy output. Cirrascale calls the model No Surprises billing and states there are no hidden fees.
Private Gemini and Managed Inference Endpoints
The AI inference platform covers hosted endpoints for open models, a Qualcomm inference cloud, and Ai2 model endpoints. The private Gemini endpoint runs on Google Distributed Cloud and is listed on Google Cloud Marketplace, so enterprises and public sector agencies can use a managed Gemini model while keeping prompts and responses inside their own infrastructure.
Tiered Storage From Hot NVMe to Object
NVMe hot-tier storage is priced separately from object storage, and object rates drop as volume climbs through the 50TB, 2PB, and larger tiers. The storage layer is built for training sets made of millions of small files, which is where naive object storage setups usually fall over. Dell hardware and Weka's parallel file system underpin the offering.
Managed Services for Teams Without a Platform Crew
Cirrascale offers professional and managed services aimed at zero DevOps, so a data science team can hand off provisioning, monitoring, and cluster upkeep. For a group without a dedicated infrastructure engineer, that closes the gap between renting a server and actually running training jobs on it.
Pros and cons
Pros
- Server-level pricing makes multi-month AI budgets easy to forecast, and longer terms lower the per-GPU rate.
- No ingress or egress fees removes a cost that quietly grows on other GPU clouds when datasets are large.
- The accelerator catalog covers NVIDIA, AMD, Qualcomm, and Tenstorrent, which gives buyers real negotiating room.
- Clustered H100 with InfiniBand reaches $2.49 per GPU hour at 16 nodes and up, competitive for large training runs.
- Dell Platinum Partner status and Weka storage back the hardware story with named vendors you can verify.
Cons
- There's no hourly or per-second billing, so a short experiment still costs a full month of server time.
- Onboarding runs through sales, which means you can't spin up a node with a credit card in five minutes.
- No free plan or trial means evaluating the platform requires a paid commitment.
- Published prices are per server, so comparing against per-hour clouds takes manual math.
Frequently asked questions
It provides dedicated GPU and accelerator servers for AI training, fine-tuning, and inference, plus managed inference endpoints. Teams rent whole machines for a month or longer and run their own stack on top.
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