Cirrascale

Cirrascale GPU pricing overview

Cirrascale is a specialist GPU cloud provider headquartered in San Diego, CA. Cirrascale Cloud Services provides enterprise-grade AI infrastructure featuring H100 NVL, H100 SXM5, and H200 GPU clusters with InfiniBand networking for high-throughput distributed LLM training and large-scale AI workloads. Dedicated cluster deployments and reserved configurations give enterprises full control over their GPU infrastructure without shared-tenancy concerns. A specialist provider for AI labs and enterprises that need dedicated H100 or H200 cluster capacity at scale. Billing is On-demand, Reserved with a minimum commitment of None. Available regions include US. On-demand GPU instances can be provisioned in minutes with no upfront cost, making Cirrascale suitable for both short-duration experiments and sustained production workloads.

Cirrascale vs other GPU providers

Cirrascale competes with providers including Lambda Labs, CoreWeave, RunPod, Paperspace, Vast.ai, and the major hyperscalers (AWS, Google Cloud, Azure) for GPU compute workloads spanning LLM training, fine-tuning, and inference serving. Key differentiators include: H100/H200 cluster focus; InfiniBand networking; Dedicated deployments. Use the side-by-side comparison tool above to see Cirrascale pricing against any other provider across shared GPU models. For a broader market view, the live GPU prices table shows all active listings alongside 94+ providers in a single sortable view.

Best use cases for Cirrascale

Cirrascale is best suited for: Large-scale AI training, Enterprise LLM workloads, Dedicated cluster users. Support tiers range from Standard → Enterprise, making it viable for both individual researchers and enterprise teams with SLA requirements. For workloads requiring the highest single-GPU throughput, H100 SXM5 instances with NVLink interconnect deliver the best performance per dollar at scale. For cost-sensitive fine-tuning or inference of models up to 13B parameters, A100 40GB or RTX 4090 instances typically offer the best value.

Cirrascale billing model and cost structure

Cirrascale uses On-demand, Reserved pricing. On-demand instances are billed per second or per hour depending on the instance type, with no termination fees. Spot pricing is not currently available on this provider — all instances are on-demand. Reserved instance pricing, where available, can reduce costs by 30–60% for predictable long-running workloads. Always compare the effective hourly rate including egress, storage, and networking costs when evaluating total cost of ownership across providers.

Choosing the right GPU on Cirrascale

GPU selection depends on model size, precision, and whether your workload is compute-bound or memory-bandwidth-bound. For LLM training above 30B parameters, H100 80GB SXM5 instances with NVLink are the standard choice — the 3,350 GB/s HBM3 bandwidth and 989 TFLOPS FP16 throughput make them 2–2.5× faster than A100 for transformer workloads. For inference of 7B–13B models in FP16 or BF16, A100 40GB offers the best cost-per-token on most providers. RTX 4090 instances are ideal for fine-tuning, prototyping, and quantized inference (INT4/INT8) of models up to 70B. Read the H100 vs A100 guide or the GPU benchmarks for ML guide for a full breakdown.

How Cirrascale pricing data is collected

Prices shown are sourced from Cirrascale's public pricing API or pricing page and refreshed every 15 minutes. On-demand rates reflect the current list price for a single GPU instance in the cheapest available region. Spot prices, where available, reflect interruptible instance rates at the time of the last snapshot. All prices are in USD per hour. Daily snapshots are retained for 90 days and visualised in the GPU price history charts — useful for identifying seasonal pricing patterns and evaluating whether current rates are above or below the 30-day average.

Evaluating managed LLM inference APIs as an alternative to self-hosted GPU compute? Compare live LLM token prices across OpenAI, Anthropic, Google, Groq, and 14+ other providers. The cheapest GPU cloud guide covers the break-even analysis between self-hosted and managed inference at different request volumes.