Verda

Verda GPU pricing overview

Verda is a specialist GPU cloud provider headquartered in Oslo, Norway. Verda is a full-stack European AI cloud built for the full AI lifecycle — from rapid prototyping to foundation training and scalable inference. GPU catalog: GB300 SXM6, B300 SXM6, B200 SXM6, H200 SXM5, H100 SXM5, A100 SXM4, RTX PRO 6000, L40S — on-demand and spot. Instant clusters with InfiniBand interconnect up to 144× GPUs self-service. Serverless containers with scale-to-zero. GB300 NVL72 available (NVLink v5). $155M funding (NIB joined July 2026). SOC 2 Type II, ISO 27001/27017/27018/27701, GDPR, C5 certified. In-house AI Lab co-engineering with customers (1X World Model, SGLang, vLLM). 99.9% uptime. Billing is On-demand, Spot, Reserved (1mo–2yr) with a minimum commitment of None. Available regions include EU-North, EU-West. GPU instances can be provisioned on-demand, making Verda suitable for both short-duration experiments and sustained production workloads.

Verda vs other GPU providers

Verda 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: GB300, B300, B200 — competitive Blackwell pricing with spot options; Spot pricing available across all GPU tiers; Instant clusters up to 144× GPUs with InfiniBand, self-service. Use the side-by-side comparison tool above to see Verda pricing against any other provider across shared GPU models. For a broader market view, the live GPU prices table shows all 12 Verda listings alongside 91+ providers in a single sortable view.

Best use cases for Verda

Verda is best suited for: EU AI teams needing GDPR and ISO 27001 compliance, Full AI lifecycle — prototyping through foundation training, Teams wanting co-engineering support from AI Lab, Confidential computing workloads (hardware-attested inference). Support tiers range from Standard → Enterprise (ML craftsmen + infra engineers), making it viable for both individual researchers and enterprise teams with SLA requirements. There are currently 12 active GPU listings on Verda, covering V100 16GB, RTX A6000, RTX 6000 Ada, A100 40GB 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.

Verda billing model and cost structure

Verda uses On-demand, Spot, Reserved (1mo–2yr) pricing. Spot (interruptible) instances are available from $4.18/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 Verda

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 Verda pricing data is collected

Prices shown are sourced from Verda'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.