Rent bare metal AI servers powered by NVIDIA GPU accelerators on a fully dedicated cloud—purpose-built for AI and ML projects. Design your own AI server by fine-tuning CPUs, RAM, and storage to balance cost against raw performance. Spin up a GPU-backed machine learning or deep learning server with instant 24/7 technical support and crypto payments welcome.
"A genuinely solid provider. Couldn't be happier. The support team is outstanding, and they delivered the custom GPU build exactly the way we needed it."
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Our bare metal machines run on latest-generation AMD EPYC or Intel Gold CPUs paired with high-throughput Nvidia GPU accelerators—well suited to training, validating, and serving machine learning models. The whole physical server belongs to you alone, delivering stronger performance, tighter security, and better cost-efficiency.
Assemble an AI or deep learning server that matches your exact workloads. On custom dedicated bare metal, drop in Nvidia GPUs to boost parallel throughput, pick multi-core CPUs, scale up to 1TB of RAM for preprocessing and inference, and add up to 16TB of NVMe storage for rapid data access.
Round-the-clock support comes standard with every plan. Our engineers are on hand to help and usually return a fix in under 15 minutes, keeping your servers running smoothly. Reach us through live chat, email, phone, Telegram, or Discord, and get help with your AI and machine learning server setups whenever you need it.
Pick a GPU server tuned for deep learning and machine learning. Wlvhosting’s AI servers are a strong fit for demanding deep learning runs, production-grade machine learning pipelines, and much more.
Wlvhosting’s bare metal cloud gives you dedicated CPU, RAM, and storage, so you get peak performance with none of the virtualization overhead. Custom AI servers also hand you tighter data control and the freedom to scale up or down whenever you need to.
Spin up a custom GPU server for deep learning and machine learning. Attach your GPU of choice at deployment to speed up heavy math, cutting AI model training time and enabling real-time inference. Combine top-tier CPUs with NVIDIA Tensor Core GPUs for the compute headroom to tackle even the most demanding AI models.
GPUs may steal the spotlight for AI acceleration, but high-performance CPUs do the heavy lifting that keeps workloads steady. Choose AMD EPYC or Intel Gold processors for large, data-intensive AI applications and dependable throughput, with configurations supporting up to 128 CPU cores.
Tailor your own AI server to the workloads you actually run, mixing strong CPUs, ample RAM, and modern Nvidia GPUs to get the most out of AI: quicker training, larger models, and better all-round performance. Pay only for what you use and stretch your ROI further.
AI training demands fast, constant access to data. Crunch large, complex models and datasets with up to 1TB of RAM, and wire in up to 80TB of directly attached NVMe storage to keep data moving. Free backup storage is included, and you can grow it to 2TB later as you scale.
Make your AI applications more efficient, scalable, and flexible. Every plan includes up to 100TB of free monthly egress traffic, with anything beyond that priced from $0.5/TB.
DDoS protection is switched on by default at no extra charge, automatically absorbing attacks to prevent downtime so your AI applications keep running securely and efficiently online.
Our engineers are available 24/7 to help and keep your servers running flawlessly. We aim to deliver a working fix in under 15 minutes, and you can reach us via live chat, email, phone, Telegram, or Discord.
An AI server is a machine engineered for the heavy demands of AI work, supplying the compute, storage, and networking such tasks require. The right configuration usually hinges on your application, model size, dataset volume, and the AI framework you use (TensorFlow, PyTorch).
For instance, NLP leans on strong CPUs for text processing, while computer vision relies on more capable GPUs for image and video analysis. Cloud-based infrastructure adds the scalability and flexibility to match.
A GPU-powered server with one or several high-performance GPUs (such as NVIDIA A100), sized to your AI core and dataset. Pair it with a capable multi-core CPU (such as AMD EPYC) to keep deep learning workloads running smoothly.
Plenty of RAM: 32 GB covers basic tasks, but 64 GB is recommended for most AI applications with large datasets, and 128GB or more for heavy-duty jobs. Generous fast storage matters too, starting at 500 GB NVMe and scaling to 1TB-plus drives, with extra capacity on hand for very large datasets.
Let's see what Wlvhosting can do for your workloads. Complete the form to request a test server and our team will be in touch.
Share a few details about your project. If it's a good match, we'll pair you with a test server and help you hit the ground running, with our support engineers guiding your setup from day one.
An AI server is a machine built for the heavy demands of AI, machine learning, and deep learning, delivering the compute, storage, and networking these tasks need. GPUs are central to it, handling parallel processing across large datasets. Wlvhosting offers ready-made GPU servers tuned for AI and deep learning, and you can just as easily build a custom AI server of your own.
A GPU dedicated server is any custom dedicated server fitted with GPU accelerators. You can choose from Nvidia A100, A40, A16, A10, A2, Tesla P4, Quadro K2200, and K4200 accelerators. They form excellent machine learning, AI, and deep learning servers, delivering high performance and parallel processing that lift overall computational efficiency.
Simply add the GPU you want while deploying a custom dedicated server that supports accelerators. Provisioning a GPU dedicated server generally takes 24 to 72 hours, depending on the location you choose.
Supported operating systems for AI servers include Ubuntu (22.04, 24.04), Debian (11, 12), AlmaLinux (8, 9, 10), OpenSUSE (Leap 15), Windows Server (2019, 2022), and VMware ESXi (7, 8). You can also self-install any other custom OS over IP KVM.
Nvidia A100, A40, and A10 Tensor Core GPUs are common choices in hosting environments. Built for HPC, deep learning, and data processing, they deliver strong parallel processing and efficiency.
Adding a GPU to a server sharply boosts compute power, especially for parallel tasks like AI, machine learning, and deep learning. Large datasets and complex calculations run far more efficiently on GPUs than on a CPU-only server.
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