Every AI, machine learning, or rendering project eventually runs into the same question: how much GPU power do you actually need, and should you rent it flexibly in the cloud or run it on hardware dedicated only to you? At Webyne, we offer both — a full lineup of GPU Cloud instances and GPU Dedicated Servers, built on NVIDIA's most trusted architectures, from the A16 to the latest RTX Pro 6000 Blackwell.
In this guide, we'll walk through every plan we offer, who each one is built for, and how to pick the right fit for your workload — whether that's training a model from scratch, running production inference, or rendering graphics at scale.
GPU Cloud PlansGPU Cloud is built for teams that need to compute on demand — spin up an instance in minutes, scale when your workload grows, and avoid the upfront cost of owning hardware. It's the right starting point if your usage is variable, or if you're still validating a model or product before committing to fixed infrastructure.
A16 GPU Cloud
The NVIDIA A16 is a multi-instance GPU designed for high user density rather than raw single-workload power. It's a strong fit for virtual desktop infrastructure (VDI), light inference tasks, and environments where several users or processes need to share GPU resources efficiently. If you're running graphics-light, multi-user applications, this is the most cost-effective entry point into our GPU Cloud lineup.
A100 GPU Cloud
The A100 is one of the most widely adopted GPUs in AI infrastructure today, and for good reason — it handles deep learning training, high-performance computing, and large dataset processing with consistent, enterprise-grade performance. If your workload involves training models of meaningful size, or running compute-heavy simulations, the A100 GPU Cloud plan gives you that power without a hardware purchase.
V100 GPU Cloud
Still a dependable workhorse, the V100 suits teams running established machine learning pipelines that don't need the very latest architecture. It's a good middle-ground option when you want solid training and inference performance at a lower cost than newer-generation GPUs.
L40S GPU Cloud
The L40S is a hybrid GPU — equally capable at AI inference, 3D rendering, and generative AI workloads. Teams that split time between visualization work (rendering, simulation) and deploying AI models often find the L40S hits the sweet spot, since it avoids the need for separate hardware for each task.
RTX Pro 6000 Blackwell GPU Cloud
Our newest and most powerful cloud offering, built on NVIDIA's Blackwell architecture. This plan is designed for the heaviest workloads we support in the cloud — large language model inference and fine-tuning, generative AI pipelines, and any project where throughput and speed directly affect your output. If you're building on the latest generation of AI models, this is the plan to be on.
GPU Dedicated Server PlansDedicated GPU servers give you the entire machine — no shared tenancy, no variable performance from neighboring workloads. This matters most for production systems, workloads with sensitive data, or any project that needs consistent, predictable performance over a long period.
L4 GPU Dedicated Server
The NVIDIA L4 is built for efficient, always-on inference and video processing workloads. As a dedicated server, it's a strong choice for production inference pipelines that need to run continuously without the cost overhead of a larger GPU.
A100 GPU Dedicated Server
Get the A100's full training performance dedicated entirely to your workload. This plan suits teams running long training jobs, fine-tuning large models, or processing heavy datasets where sharing GPU resources would create bottlenecks.
RTX Pro 6000 Blackwell GPU Dedicated Server
Our flagship dedicated plan. Full, exclusive access to Blackwell-generation compute — built for enterprises running mission-critical AI systems, large-scale model training, or high-throughput inference where downtime or shared performance simply isn't an option.
GPU Cloud vs Dedicated Server: How to Decide
|
Consideration |
GPU Cloud |
GPU Dedicated Server |
|
Best for |
Variable or growing workloads |
Consistent, long-term production workloads |
|
Setup speed |
Minutes |
Slightly longer provisioning |
|
Resource sharing |
Instance-based, scalable |
None — fully isolated hardware |
|
Ideal use case |
Testing, scaling, short-term projects |
Production AI, sensitive data, 24/7 workloads |
Current Offers on All GPU Plans
These offers apply across the entire GPU lineup — from the entry-level A16 GPU Cloud plan to the flagship RTX Pro 6000 Blackwell GPU Dedicated Server. The longer you commit, the more free GPU time you unlock, making it the most cost-efficient way to run sustained AI, training, or rendering workloads on Webyne.
