Vultr

Who is Vultr best for? AI training, inference, video rendering, HPC, Stable Diffusion, game development, generative AI, fine-tuning, research

Vultr is categorized as a Multi-Cloud cloud GPU provider. The platform offers GPU models including A16, A40, L40S, A100 PCIe, GH200, A100 SXM, H100 SXM, B200, B300, MI300X, MI325X, MI355X with entry-level pricing at $0.47/hr.

Whether you are fine-tuning a language model, running inference at scale, or training computer vision models, the right fit depends on your specific requirements for GPU type, VRAM, interconnect, and budget..

Try Vultr with a free trial — sign up at their official website.

The current Trustpilot rating for Vultr is 1.7 out of 5.0, based on 557 total reviews as of July 1, 2026. Vultr was founded in 2014.

You can read all user reviews directly on the Trustpilot page for Vultr. Trustpilot ratings reflect real user experiences with GPU provisioning speed, pricing accuracy, support responsiveness, and overall platform reliability.

See how Vultr compares to alternatives and explore their current offerings at Vultr official website.

Pre-installed frameworks at Vultr: PyTorch, TensorFlow, CUDA, cuDNN, ROCm, Hugging Face, NVIDIA NGC

Custom images: Yes — bring your own Docker container with any framework, library, or CUDA version you need.
Jupyter: Yes — interactive development environment for experimentation.
Persistent storage: Yes — keep datasets and checkpoints across sessions.

This combination lets you work with any ML stack, from standard PyTorch/TensorFlow workflows to specialized inference frameworks, with the flexibility to customize your environment.

For environment setup guides and CUDA compatibility, visit Vultr official website.

Here is the developer experience at Vultr:

Setup time: Minutes — this is how quickly you can provision and access a GPU instance after initiating the request.

Available tools:
- Docker containers: Yes
- Direct SSH access: Yes
- Jupyter notebooks: Yes
- Programmatic API/CLI: Yes
- Custom Docker images: Yes

This combination of tools makes Vultr suitable for both exploratory research (Jupyter) and production MLOps pipelines (API + Docker + SSH).

See the full setup documentation and API reference on Vultr official website.

Does Vultr offer serverless? Yes

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.

Infrastructure overview for Vultr:

- Headquarters: United States
- GPU regions: 32 regions across 6 continents (Americas, Europe, Asia, Australia, Africa)
- Uptime SLA: 100%
- Private networking: Yes

Multi-region availability allows you to deploy models closer to end users, reducing inference latency. It also provides redundancy options for critical workloads.

View the full list of data center regions at Vultr official website.

Distributed training support at Vultr:

NVLink interconnect with up to 16 GPUs per instance. Multi-node training: Yes.

For context, training a 70B parameter model typically requires 8+ GPUs with high-bandwidth interconnect. The available GPU models at Vultr include:

A16, A40, L40S, A100 PCIe, GH200, A100 SXM, H100 SXM, B200, B300, MI300X, MI325X, MI355X

Visit the to see multi-GPU instance configurations and pricing.

See how Vultr handles distributed training infrastructure at their official website.

Vultr spot instance availability: Yes

For workloads that can handle occasional interruptions — such as large-scale model training with regular checkpointing or batch processing jobs — spot instances provide substantial cost savings compared to on-demand pricing. Regular on-demand instances at Vultr start at $0.47/hr.

See live spot pricing and interruption rates on Vultr official website.

When evaluating Vultr, it is important to understand their data transfer policy: Standard (varies by plan)

Egress charges are often an overlooked cost in cloud GPU budgeting. A provider with zero egress fees allows you to freely download model outputs, move datasets, and serve inference results without unexpected bandwidth bills.

Vultr storage options: 350 GB - 61 TB NVMe (included), Block Storage at $0.10/GB/mo, S3-compatible Object Storage

See how data transfer costs scale with volume at Vultr official website.

Here is what Vultr currently offers for new users looking to evaluate the platform:

Up to $300 free credit for 30 days

Given that the cheapest GPU option at Vultr costs $0.47/hr, free credits provide a practical opportunity to run real workloads and compare Vultr against other cloud GPU providers before committing financially.

For current credit offers and eligibility, visit Vultr official website.

The GPU fleet at Vultr includes both data center and workstation-class accelerators:

A16, A40, L40S, A100 PCIe, GH200, A100 SXM, H100 SXM, B200, B300, MI300X, MI325X, MI355X

Maximum VRAM per GPU: 288 GB
Maximum GPUs per instance: 16
Interconnect: NVLink

This hardware selection covers use cases from cost-effective inference on consumer GPUs to large-scale distributed training on enterprise accelerators.

For detailed GPU specs, VRAM configurations, and multi-GPU options, check Vultr official website.

GPU compute at Vultr is billed on a Per-hour basis with rates beginning at $0.47/hr for the most affordable GPU option. This billing granularity is particularly useful for short training runs, experimentation, and inference tasks where you may only need a GPU for minutes at a time.

Does Vultr offer spot instances? Yes
Are reserved discounts available?

Payment methods: Credit/Debit Cards, PayPal, Cryptocurrency (BitPay), Alipay, UnionPay, ACH, Wire Transfer.

View the complete GPU pricing calculator at Vultr official website.