Vultr

Vultr GPU pricing overview

Vultr is a specialist GPU cloud provider headquartered in Matawan, NJ. Vultr offers H100, A100, and L40S GPU instances across 32 global locations with simple hourly GPU rental pricing and no long-term commitment required. A developer-friendly GPU cloud with a clean API, straightforward billing, and broad geographic coverage for teams needing AI inference or training capacity close to their users. A solid choice for global deployment of AI workloads without the complexity of hyperscaler pricing models. Billing is On-demand (hourly) with a minimum commitment of None. Available regions include US, EU, APAC, AU and 1 more. On-demand GPU instances can be provisioned in minutes with no upfront cost, making Vultr suitable for both short-duration experiments and sustained production workloads.

Vultr vs other GPU providers

Vultr 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: 32 global locations; Simple pricing; Hourly billing. Use the side-by-side comparison tool above to see Vultr pricing against any other provider across shared GPU models. For a broader market view, the live GPU prices table shows all 4 Vultr listings alongside 94+ providers in a single sortable view.

Best use cases for Vultr

Vultr is best suited for: Global deployment, Simple workloads, Developer-friendly teams. Support tiers range from Basic → Enterprise, making it viable for both individual researchers and enterprise teams with SLA requirements. There are currently 4 active GPU listings on Vultr, covering A100 80GB, A100 40GB, RTX 4090, RTX 5080. 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.

Vultr billing model and cost structure

Vultr uses On-demand (hourly) 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 Vultr

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 Vultr pricing data is collected

Prices shown are sourced from Vultr'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.