How does serverless GPU work at Vultr?
💡 Answer
Does Vultr offer serverless? 1
Serverless GPU eliminates the need to manage infrastructure for inference workloads. Instead of provisioning dedicated instances, your model endpoint automatically handles incoming requests and charges only for active compute time. This approach is ideal for APIs serving ML predictions, chatbot backends, and image generation endpoints.
Base GPU pricing: $0.47/hr.
Try the serverless inference API at Vultr official website.
More FAQs about Vultr
- What makes Vultr different from other cloud GPU providers?
- How many Trustpilot reviews does Vultr have, and what is its score?
- Does Vultr support Hugging Face, vLLM, or other inference frameworks?
- Can I SSH into GPU instances at Vultr?
- How reliable is Vultr infrastructure?
- Does Vultr support multi-node GPU clusters?
- Does Vultr provide interruptible GPU instances at lower prices?
- What are the data transfer and storage fees at Vultr?
- What free credits or promotional offers does Vultr provide?
- What GPU hardware can I rent from Vultr?
- What does it cost to rent a GPU from Vultr?
Guides Where Vultr Is Featured
- Best Cloud GPU Providers with NVIDIA A100
- Best Cloud GPUs for Inference & Model Serving
- Cheapest Cloud GPUs Under $0.50/hr
- Cloud GPU Providers with API & CLI Management
- Cloud GPU Providers with Docker & Custom Images
- Cloud GPU Providers with Free Credits
- Cloud GPU Providers with Jupyter Notebook Support
- Cloud GPU Providers with Kubernetes Support
- Cloud GPU Providers with Multi-Node GPU Clusters
- Cloud GPU Providers with NVLink or InfiniBand
- Cloud GPU Providers with Per-Second Billing
- Cloud GPU Providers with Persistent Storage
- Cloud GPU Providers with Serverless GPU Inference
- Cloud GPU Providers with Spot / Preemptible Instances
- Cloud GPU Providers with SSH Access
- Cloud GPU Providers with Zero Egress Fees
These guides include Vultr alongside other cloud GPU providers, grouped by hardware, pricing, features, and infrastructure.
Vultr vs Massed Compute vs DigitalOcean - GPU Provider Comparison (April 2026)
Side-by-side comparison of Vultr vs Massed Compute vs DigitalOcean. Quickly scan maximum funding, profit splits, risk rules, leverage, platforms, instruments, payout schedules, payment options, trading permissions and KYC restrictions to narrow down your prop trading firm shortlist. Data updated April 2026.
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Vultr
High-performance cloud GPU across 32 global regions
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Massed Compute
GPU cloud with direct engineer support
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DigitalOcean
Simple, scalable GPU cloud for AI/ML
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|---|---|---|---|
| Overview | |||
| Trustpilot Rating | 1.8 | 0 | 4.6 |
| Headquarters | United States | United States | United States |
| Provider Type | Multi-Cloud | GPU-Focused | N/A |
| Best For | AI training inference video rendering HPC Stable Diffusion game development generative AI fine-tuning research | AI training inference VFX rendering generative AI fine-tuning HPC Stable Diffusion research | AI training inference fine-tuning LLM deployment LLM serving computer vision startups generative AI research |
| GPU Hardware | |||
| GPU Models | A16 A40 L40S A100 PCIe GH200 A100 SXM H100 SXM B200 B300 MI300X MI325X MI355X | A30 RTX A5000 RTX A6000 L40S A100 SXM H100 PCIe H100 SXM H100 NVL RTX PRO 6000 H200 NVL | RTX 4000 Ada RTX 6000 Ada L40S MI300X H100 SXM H200 |
| Max VRAM (GB) | 288 | 141 | 192 |
| Max GPUs/Instance | 16 | 8 | 8 |
| Interconnect | NVLink | NVLink | NVLink |
| Pricing | |||
| Starting Price ($/hr) | $0.47/hr | $0.35/hr | $0.76/hr |
| Billing Granularity | Per-hour | Per-minute | Per-second |
| Spot/Preemptible | 1 | 0 | 0 |
| Reserved Discounts | N/A | N/A | N/A |
| Free Credits | Up to $300 free credit for 30 days | None | $200 free credit for 60 days |
| Egress Fees | Standard (varies by plan) | None | None (included in plan) |
| Storage | 350 GB - 61 TB NVMe (included), Block Storage at $0.10/GB/mo, S3-compatible Object Storage | Local NVMe included with instances | 500-720 GiB NVMe boot (included), 5 TiB NVMe scratch on larger configs, Volumes at $0.10/GiB/mo |
| Infrastructure | |||
| Regions | 32 regions across 6 continents (Americas, Europe, Asia, Australia, Africa) | United States (Tier III data centers) | New York (NYC2), Toronto (TOR1), Atlanta (ATL1), Richmond (RIC1), Amsterdam (AMS3) |
| Uptime SLA | 100% | Tier III (99.98% design) | 99% |
| Developer Experience | |||
| Frameworks | PyTorch TensorFlow CUDA cuDNN ROCm Hugging Face NVIDIA NGC | PyTorch TensorFlow CUDA cuDNN ComfyUI pre-configured ML templates | PyTorch TensorFlow Jupyter Miniconda CUDA ROCm Hugging Face |
| Docker Support | 1 | 1 | 1 |
| SSH Access | 1 | 1 | 1 |
| Jupyter Notebooks | 1 | 0 | 1 |
| API / CLI | 1 | 1 | 1 |
| Setup Time | Minutes | Minutes | Minutes |
| Kubernetes Support | 1 | 0 | 1 |
| Business Terms | |||
| Min Commitment | None | None | None |
| Compliance | SOC 2+ (HIPAA) PCI ISO 27001 ISO 27017 ISO 27018 ISO 20000-1 CSA STAR Level 1 | SOC 2 Type II HIPAA | SOC 2 Type II SOC 3 HIPAA (with BAA) CSA STAR Level 1 |