Compute AI
Powerful digital infrastructure to build what will shape the future
Cluster de GPU de alto desempenho em uma plataforma que vai redefinir sua experiência com infraestrutura sob demanda.
Why is WCI different from everything you know?
Infrastructure optimized for AI workloads
On-demand scalability
Predictable costs (in local currency, with no conversion)
Architecture and workloads:
- Machine Learning
- Generative AI
- LLM Training
- Inference
- HPC
- Data Science
WCI is Wevy’s cloud infrastructure platform
Designed to support critical applications, data, and innovation with predictability and control, WCI offers specialized GPU-based infrastructure for training, running, and scaling artificial intelligence models. This allows companies to access high-performance GPU clusters on demand, accelerating machine learning, data science, and AI-driven application projects.
Everything you need today
to build what the market will demand tomorrow
Model training
GPU infrastructure built for large-scale machine learning and deep learning model training.
Inference at scale
Run AI models in production with low latency and high availability.
Accelerated computing
GPU compute for intensive workloads such as simulations, rendering, and large-scale data analysis.
The right infrastructure does not adapt to the decision
it is born from the right decision
The difference is not only in what WCI delivers, but especially in what it eliminates. Discover it.
Infrastructure built for modern workloads
— the ones you use today and the ones you will need tomorrow
Next-generation GPUs
GPU compute para workloads intensivos como simulações, renderização e análise de grandes volumes de dados.
Scalable GPU clusters
Run workloads ranging from single instances to distributed clusters.
High-performance networking
Fast interconnects designed for distributed model training.
High-throughput storage
Infrastructure built for large volumes of data used in AI.
Environments for ML and data science
Compatibility with frameworks such as PyTorch, TensorFlow, and CUDA.
WCI is the best option for companies that:
- develop machine learning models
- train artificial intelligence models
- run inference at scale
- work with large volumes of data
- need to accelerate compute-intensive workloads
Investing in robust, reliable infrastructure is also a way of looking to the future.
The experience of those who have seen the future change more than once
Wevy is the first Latin American multinational in cloud computing.
Compute
Digital infrastructure that delivers autonomy and predictability
Software Cloud Modernization
Software modernization platform with no refactoring
Managed Services
Monitoring, support, and sustainment for critical infrastructures
Data & AI - AI no-code
Custom dashboards and enterprise apps built from simple prompts
FAQ
Frequently Asked Questions
What is GPU Cloud and what is it used for?
GPU Cloud is a cloud infrastructure model that provides graphics processing units (GPUs) on demand for compute-intensive workloads.
Unlike traditional infrastructures based mainly on CPUs, GPUs can perform thousands of operations in parallel, which speeds up tasks such as training machine learning models, deep learning, and generative artificial intelligence.
With GPU Cloud, companies can access this processing power without having to invest in their own hardware, using cloud GPUs to develop, train, and run artificial intelligence applications.
What is the difference between CPU and GPU for artificial intelligence?
The CPU (Central Processing Unit) is designed to perform general computing tasks, while the GPU (Graphics Processing Unit) was developed to process large volumes of calculations simultaneously. This parallel processing capability makes GPUs ideal for tasks such as:
- machine learning model training
- processing large volumes of data
- artificial intelligence model inference
- rendering and computational simulation
That is why most modern AI and deep learning projects use GPU-based infrastructure.
When does it make sense to use GPU infrastructure for AI?
GPU-based infrastructure is recommended when applications involve intensive data processing or complex artificial intelligence models. It is especially suitable for:
- machine learning model training
- developing generative AI applications
- running LLMs (Large Language Models)
- processing large volumes of data
- data science and scientific computing workloads
In many cases, the use of GPUs drastically reduces the time required to train or run AI models.
GPU Cloud or on-premises infrastructure: which is better?
For many companies, using cloud GPUs is more efficient than investing in their own infrastructure.
GPU data centers require significant investments in hardware, power, cooling, and maintenance. In the cloud, on the other hand, resources can be provisioned quickly and scaled based on demand.
In addition, GPU Cloud platforms allow engineering and data science teams to focus on developing artificial intelligence models and applications, without having to manage the physical infrastructure.
How much does it cost to use GPU in the cloud?
The cost of GPU Cloud depends on several factors, including:
- the type of GPU used
- the number of instances
- usage time
- storage and network capacity
In general, the pricing model follows an on-demand infrastructure approach, in which companies pay only for the compute resources they use.
This makes it possible to scale artificial intelligence projects flexibly, without the need to invest upfront in specialized hardware.
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