Overview

Vultr has evolved into a global GPU cloud provider, offering on-demand access to the latest NVIDIA accelerators and announcing AMD options on the roadmap. Workloads span AI and machine learning, high-performance computing, graphics rendering, and virtual desktops. With a presence in dozens of data centers worldwide, Vultr’s platform aims to make advanced GPU hardware accessible to AI researchers, startup engineers, IT decision-makers, and data scientists without the complexity or upfront expense of managing physical servers.

This Vultr Cloud GPU review provides a non-biased look at Vultr’s current GPU cloud portfolio: the available GPU models, their specifications, pricing models, key use cases, and the overall value proposition, presented with both technical and business readers in mind.

GPU Hardware Options

As of 2025, Vultr’s GPU lineup features several NVIDIA models spanning Ampere, Ada Lovelace, and Hopper generations, covering a range of use cases. Each GPU option differs in memory size, architecture, and whether it is offered as a virtual shared GPU or a dedicated passthrough device. Below is a summary of GPUs currently available on Vultr and their core specs:

NVIDIA A16 (Ampere architecture): 64 GB total VRAM (vGPU shared), 5,120 CUDA cores, 160 Tensor Cores, approximately 72 TFLOPS (TF32 with sparsity). Built for virtual desktop infrastructure and lightweight AI inference. The card effectively packs four GPUs on one board for high-density remote desktop deployments.

NVIDIA A40 (Ampere): 48 GB VRAM (vGPU), 10,752 CUDA cores, 336 Tensor Cores, approximately 149.6 TFLOPS (TF32 with sparsity). A workstation-class GPU for professional visualization, including 3D rendering, CAD, video production, and scientific simulation workloads that demand larger memory and strong FP32 performance.

NVIDIA A100 80 GB (Ampere Tensor Core): 80 GB VRAM (vGPU), 6,912 CUDA cores, 432 Tensor Cores, approximately 312 TFLOPS (TF32 with sparsity). A data-center GPU for AI training and HPC. Vultr’s A100 instances can leverage NVIDIA Multi-Instance GPU to partition the device for smaller workloads when needed.

NVIDIA L40S (Ada Lovelace): 48 GB VRAM (dedicated passthrough), 18,716 CUDA cores, 568 Tensor Cores, approximately 366 TFLOPS (TF32 with sparsity). Optimized for mixed workloads across LLM inference, real-time graphics, and video processing. Passthrough assignment provides the full card to a single VM.

NVIDIA H100 (Hopper): 80 GB HBM2e per GPU, typically offered in multi-GPU configurations. Vultr provides H100 in NVIDIA HGX H100 clusters for extreme compute needs. An 8×H100 server with 640 GB total GPU memory is available, with fourth-generation Tensor Cores and NVLink connectivity. H100 targets cutting-edge AI training and can deliver significant throughput gains on large language model inference compared to prior generations.

NVIDIA GH200 “Grace Hopper” Superchip: Combines a Hopper GPU with an integrated Grace CPU and HBM3e memory. Each GH200 in Vultr’s cloud includes 144 CPU cores, 282 GB of HBM3e GPU memory, and roughly 8 petaFLOPS of AI performance. Tuned for giant-scale AI and often positioned as a leading option for AI inference, suitable for serving large models and HPC workloads with minimal latency. Vultr has announced broad GH200 availability across its locations.

Upcoming AMD Options: Vultr has announced plans to support AMD Instinct GPUs. Next-generation accelerators such as the AMD Instinct MI355X, based on CDNA 4 with up to 288 GB of HBM3E memory, are slated to join the platform, complementing the lineup and providing an AMD stack from CPU to GPU blogs.vultr.com. As of mid-2025, AMD GPUs were listed as coming soon.

Pricing Structure: On-Demand and Reserved

Vultr’s pricing supports both on-demand flexibility and longer-term cost efficiency. On-demand usage lets you launch GPU instances by the hour and pay only for what you use. Hourly rates vary by GPU model and instance size.

For example, a single NVIDIA L40S VM is about $1.67 per hour on demand, an A100 80 GB starts around $1.29 per hour, and an A40 is roughly $1.71 per hour for a 48 GB card. High-end multi-GPU configurations scale accordingly. One published example cites an 8×H100 cluster at about $23.9 per hour, approximately $2.99 per GPU per hour, for on-demand consumption. These rates place Vultr in a mid-market range for cloud GPUs.

For steady or large-scale needs, reserved instances and multi-year contracts can significantly reduce costs. Vultr offers discounts for 1-year and multi-year prepaid commitments and for bulk GPU cluster reservations.

For instance, a three-year prepaid L40S commitment is listed near $0.85 per GPU per hour, which is about half of the on-demand price. Published figures also reference reserved multi-GPU servers at material savings, such as approximately $1.49 per GPU per hour on a 36-month term. Teams can prototype or burst on demand, then shift to reserved capacity for production to optimize budget.

Vultr also enables fractional GPU usage in certain cases, which lowers the entry cost for GPU computing. Using NVIDIA MIG or vGPU technology, smaller portions of a GPU can be rented for light workloads or VDI desktops. Vultr advertises cloud VDI instances starting at $21.50 per month (about $0.032 per hour) for a basic accelerated desktop.

Fractional plans make GPU acceleration accessible for small-scale needs, which can be a good fit when only a modest amount of AI or graphics capability is required. Enterprise customers building large clusters can negotiate custom deals for significant scale, with the goal of cost-effective training farms or rendering clusters.

Key Use Cases and Workload Fit

AI training and HPC: For training deep learning models and running complex simulations, NVIDIA A100 and H100 are the primary choices. The A100 80 GB supports large-scale training jobs and data processing. Hopper-based H100 extends that capability with Tensor Core and Transformer Engine features that can accelerate training and large language model throughput. Vultr’s H100 clusters offer the performance profile of a modern multi-GPU supercomputer node. Typical applications include training vision and NLP models and running MPI-based HPC workloads such as genomics or fluid dynamics.

AI inference and real-time applications: Not every workload needs the largest GPU. For many production deployments, the priority is efficient, low-latency serving. The NVIDIA L40S is optimized for LLM inference and also excels at graphics and video processing. With 48 GB of memory, it can host sizable models or serve multiple concurrent models. GH200 targets inference at scale. Its 282 GB of HBM3e helps keep very large models in memory to reduce data transfer overhead, and its combined CPU plus GPU design suits data-intensive serving. At the entry level, A16 is aimed at lighter inference and streaming or graphics tasks, with strong throughput per dollar for parallel jobs such as batch scoring or video transcoding.

Visualization, rendering, and virtual desktops: Beyond AI, Vultr supports graphics-intensive work. The NVIDIA A40 enables 3D rendering, CAD, virtual production, and VR or AR environments docs.vultr.com. Studios and designers can spin up A40 instances for complex scenes or to run GPU-accelerated tools such as Autodesk Maya, Blender, or Unreal Engine in the cloud. Scientific visualization and engineering simulation also benefit from A40’s memory capacity and FP32 performance. For VDI, the A16 was introduced to enable cloud-hosted desktops with a responsive user experience datacenterknowledge.com. For heavier graphical applications in VDI, A40-backed virtual workstations are available. The L40S bridges AI and graphics, enabling real-time graphics or video encoding alongside AI inference in media workflows.

Across these scenarios, the cloud model allows teams to match GPU type to workload and scale up or down as demand changes. A common pattern is training on A100 or H100 clusters and deploying inference on a fleet of L40S or A16 instances.

Global Infrastructure and Scalability

Vultr operates GPU-capable regions across North America, Latin America, Europe, Asia, Australia, and Africa, with 32 locations as of the latest count. This reach helps users deploy close to end-users or data sources and can support data sovereignty requirements. Vultr has expanded GPU capacity rapidly, including a global rollout of GH200 across 32 data centers, and additional H100 cluster capacity at a U.S. site powered by clean hydroelectric energy. This Washington state location highlights efforts to reduce carbon footprint and improve price-to-performance with a low PUE facility.

Availability can vary by region at launch. For example, A16 VDI instances initially appeared in eight core locations (Los Angeles, New Jersey, London, Frankfurt, Tokyo, Bangalore, Singapore, Sydney), with expansion over time. High-end offerings such as multi-GPU bare metal servers may concentrate in regions with suitable power and cooling. Today, most mainstream options like A40, A100, and L40S are accessible across many data centers. The control panel and API indicate region availability for each type.

Vultr supports both virtualized instances and bare-metal deployments. The standard Cloud GPU service provides vGPUs or passthrough GPUs attached to VMs. For dedicated performance, GPU bare-metal servers are available, such as 4×A40 or 8×A100 nodes. NVIDIA HGX multi-GPU configurations enable strong scaling with NVLink or NVSwitch. GPUs integrate with Vultr’s managed Kubernetes service, virtualized compute, and bare metal offerings, which supports containerized workflows and MLOps patterns.

Developer Experience and Support

Vultr provides GPU-optimized OS images based on Ubuntu with NVIDIA drivers, CUDA, cuDNN, and related components preinstalled. This reduces setup time for PyTorch, TensorFlow, and other GPU-accelerated workloads. Images are kept updated and tuned for the hardware.

Vultr’s Container Registry and integration with NVIDIA’s NGC catalog streamline deployment. Users can pull framework and model containers and run them on GPU instances or Kubernetes pods. Teams can manage resources via web console, CLI, or API and integrate with infrastructure-as-code and automation pipelines. Vultr participates in the NVIDIA Cloud Service Provider program and offers documentation and standard support channels. Enterprise customers can engage sales and solutions teams for architecture guidance and capacity reservations.

Vultr often emphasizes accessibility of advanced computing through pay-as-you-go models. This underscores cost, convenience, and accessibility. Small teams can access top-tier GPUs such as A100 and H100 and do so from many parts of the world. With a range of instance sizes, including fractional options, the platform can fit individual experimentation and multi-node training alike.

Overall Value Proposition

Vultr’s cloud GPU offering provides a broad set of options from cost-effective A16 vGPUs for VDI and lightweight jobs to L40S and H100 for demanding AI tasks. The global footprint enables low-latency or compliance-aligned deployments in many regions. On the performance front, Vultr has adopted recent NVIDIA architectures and has announced AMD support, helping customers access modern hardware.

From a business perspective, flexible pricing and a generally leaner cost profile compared to the largest clouds can improve price-to-performance for selected use cases. Startups and research labs can benefit from reserved discounts for long-running projects or the ability to burst on demand without long commitments. Preconfigured environments and container integrations shorten time to productivity.

Selecting a provider still requires consideration of managed services, support levels, and ecosystem needs. Vultr focuses on core compute and straightforward deployment rather than a large catalog of proprietary AI services. Organizations that want direct control over their AI stack and costs may find this approach appealing. IT leaders will note that Vultr delivers core AI infrastructure elements, while developers and data scientists gain an accessible environment for training and inference.

Conclusion: Vultr’s GPU cloud combines modern GPU hardware, flexible deployment models across VMs, bare metal, and Kubernetes, and an emphasis on accessibility. Whether you are spinning up a single GPU for a quick experiment, running a distributed training job on a cluster, or hosting a fleet of GPU-accelerated services, the platform is designed to scale with those needs. The balance of performance and practicality can serve both engineering teams and business stakeholders as AI adoption grows across organizations of all sizes.