Atlas Cloud
Specialist CloudAtlas Cloud is an Iceland-based GPU cloud offering H100 and A100 instances powered by 100% renewable geothermal energy, combining high-performance AI compute with a genuinely carbon-neutral infrastructure footprint. Competitive on-demand pricing and low-latency connectivity to Europe make it an attractive sustainable GPU cloud for EU-based AI training and inference workloads. A top choice for sustainability-focused teams that want green GPU compute without sacrificing performance.
Cheapest On-Demand
$0.810/hr
Cheapest Spot
—
GPU Listings
3
Billing
On-demand
Performance Benchmarks
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Provider Info
Headquarters
Reykjavik, Iceland
Founded
2022
Regions
IS
Min Commitment
None
Support
Standard
Strengths
- ▸100% renewable energy
- ▸Competitive H100 pricing
- ▸Low latency to Europe
Limitations
- ▸Smaller provider — limited scale vs hyperscalers
- ▸Fewer regions than major cloud providers
- ▸Less mature ecosystem and fewer integrations
Best For
Full GPU Catalog
| GPU Model | vRAM | On-Demand | Spot | Availability | Region |
|---|---|---|---|---|---|
| RTX A6000 | 48 GB | $0.810 | — | High | IS |
| A100 80GB | 80 GB | $1.63 | — | Med | IS |
| H100 80GB | 80 GB | $2.96 | — | Med | IS |
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Atlas Cloud GPU pricing overview
Atlas Cloud is a specialist GPU cloud provider headquartered in Reykjavik, Iceland. Atlas Cloud is an Iceland-based GPU cloud offering H100 and A100 instances powered by 100% renewable geothermal energy, combining high-performance AI compute with a genuinely carbon-neutral infrastructure footprint. Competitive on-demand pricing and low-latency connectivity to Europe make it an attractive sustainable GPU cloud for EU-based AI training and inference workloads. A top choice for sustainability-focused teams that want green GPU compute without sacrificing performance. Billing is On-demand with a minimum commitment of None. Available regions include IS. On-demand GPU instances can be provisioned in minutes with no upfront cost, making Atlas Cloud suitable for both short-duration experiments and sustained production workloads.
Atlas Cloud vs other GPU providers
Atlas Cloud 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: 100% renewable energy; Competitive H100 pricing; Low latency to Europe. Use the side-by-side comparison tool above to see Atlas Cloud pricing against any other provider across shared GPU models. For a broader market view, the live GPU prices table shows all 3 Atlas Cloud listings alongside 94+ providers in a single sortable view.
Best use cases for Atlas Cloud
Atlas Cloud is best suited for: Sustainability-focused teams, European AI workloads, Training runs. Support tiers range from Standard, making it viable for both individual researchers and enterprise teams with SLA requirements. There are currently 3 active GPU listings on Atlas Cloud, covering H100 80GB, A100 80GB, RTX A6000. 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.
Atlas Cloud billing model and cost structure
Atlas Cloud uses On-demand 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 Atlas Cloud
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 Atlas Cloud pricing data is collected
Prices shown are sourced from Atlas Cloud'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.
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Launch your first GPU on Atlas Cloud
On-demand from $0.810/hr — 3 GPU configurations available. On-demand billing.