Nscale

Nscale GPU pricing overview

Nscale is a specialist GPU cloud provider headquartered in London, UK. Nscale filed for IPO in September 2026 and is building full-stack AI infrastructure powering the world's most powerful AI systems. Data centers: Glomfjord Norway, Narvik Norway, Loughton UK, Texas US, plus partner-run sites in Sines Portugal, Keflavik Iceland, Stavanger Norway, Oslo Norway, Blönduós Iceland, Hayes UK, North Carolina US, and West Virginia US. Products: Nscale Cloud (managed platform with inference endpoints, fine-tuning, Kubernetes, Slurm, IAM), Nscale Infrastructure (dedicated GPU/CPU, fast networking, storage), Nscale Data Centers (modular, liquid-cooled, low PUE), and Nscale Energy (behind-the-meter power, microgrid islands). Partnerships: Figure (physical AI), Aker ASA, G Squared. UK AI Minister endorsement. Fidji Simo joined board September 2026. Billing is Enterprise contracts (contact sales) with a minimum commitment of Enterprise minimum. Available regions include EU-North (Norway), EU-West (UK), US-South (Texas), EU-North (Iceland). GPU instances can be provisioned on-demand, making Nscale suitable for both short-duration experiments and sustained production workloads.

Nscale vs other GPU providers

Nscale 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: IPO filing Sept 2026 — rapidly scaling AI infrastructure company; Data centers in Norway, UK, Texas, Iceland, Portugal — renewable energy focus; Full-stack: inference, fine-tuning, Kubernetes, Slurm, IAM in one platform. Use the side-by-side comparison tool above to see Nscale pricing against any other provider across shared GPU models. For a broader market view, the live GPU prices table shows all 5 Nscale listings alongside 91+ providers in a single sortable view.

Best use cases for Nscale

Nscale is best suited for: Enterprise AI teams needing dedicated GPU infrastructure, UK/EU organizations with data sovereignty requirements, Physical AI and robotics workloads, Teams wanting full-stack AI cloud with managed services. Support tiers range from Standard → Enterprise, making it viable for both individual researchers and enterprise teams with SLA requirements. There are currently 5 active GPU listings on Nscale, covering H100 80GB, H200 141GB, A100 80GB, L40S and more. H100 SXM5 instances with NVLink interconnect are commonly used for large-scale training workloads. A100 40GB and RTX 4090 instances are frequently used for fine-tuning and inference of smaller models.

Nscale billing model and cost structure

Nscale uses Enterprise contracts (contact sales) pricing. Spot (interruptible) instances are available from $0.38/hr — interruptible instances are typically lower-cost than on-demand, suitable for fault-tolerant training jobs with checkpointing. Reserved instance pricing may be available for predictable long-running workloads — check the provider's pricing page for current committed-use rates. 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 Nscale

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 — high HBM3 bandwidth and FP16 throughput make them well-suited for large transformer workloads. For inference of 7B–13B models in FP16 or BF16, A100 40GB is a common choice on many providers. RTX 4090 instances are widely used 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 Nscale pricing data is collected

Prices shown are sourced from Nscale'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 kept in our history archive 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 many other providers. The cheapest GPU cloud guide covers the break-even analysis between self-hosted and managed inference at different request volumes.