Is your infrastructure ready for the AI models of tomorrow?
CPUs weren’t built for the kind of parallel processing AI demands, making training slow, resource-intensive and difficult to scale. In some cases, training a large neural network on standard hardware can take weeks, only to deliver subpar performance.
That’s why everyone is running their AI workloads on cloud GPU servers now. With faster training speeds than CPUs and architectures designed to handle trillion-parameter models like NVIDIA’s upcoming Blackwell, GPU servers are the go-to choice for scalable, high-performance AI development. Their parallel processing capabilities make them imperative for training LLMs and executing other AI workloads, often delivering over 200% faster performance compared to CPUs ( A Datadog report). In our latest article, we discuss why cloud GPU servers are ideal for running your AI workloads.
Cloud GPU Servers are virtual machines in the public cloud equipped with high-performance GPUs like the NVIDIA A100, NVIDIA H100 SXM or NVIDIA H100 PCIe. You can rent them by the hour (or even by the minute), making them ideal for scalable AI workloads like training large models or running inference.
A cloud GPU server works like any standard VM but with a physical GPU card installed and fully accessible. Cloud providers handle the setup, running drivers like CUDA so your applications can offload compute tasks using familiar frameworks such as TensorFlow, PyTorch or cuDNN. Many cloud GPU providers like Hyperstack also offer pre-configured images with NVIDIA drivers and CUDA already installed for Ubuntu 20.04 LTS and 22.04 LTS, making it easy to launch and run GPU workloads right away.
Cloud GPU servers for AI also feature NVIDIA Multi-Instance GPU (MIG) that allows a single powerful GPU (like the NVIDIA A100 or NVIDIA H100) to be split into multiple isolated instances, helping you maximise resource usage. And whether you’re working on a single-GPU VM or scaling across a cluster of 8+ GPUs connected via NVLink, the result is the same: cloud GPU servers give your AI workloads a massive boost in parallel compute power, far beyond what CPUs can deliver.
From initial model development to full-scale deployment, here's why Cloud GPU servers for AI are the go-to choice for training, fine-tuning and inference:
GPUs are purpose-built for handling thousands of parallel operations, making them ideal for the matrix-heavy computations behind deep learning. With thousands of cores working in parallel, training neural networks on GPUs can be significantly faster than on traditional CPUs. This parallel architecture is especially beneficial for large datasets and complex models like transformers or convolutional networks, cutting training time from days to hours.
Modern GPUs are designed to move data quickly and that’s critical for AI workloads. Take the NVIDIA A100 80GB, for example. It features 2 terabytes per second (TB/s) of memory bandwidth for rapid data movement across large batches and layers during training. The NVIDIA H100 takes it even further:
Both include 80 GB of high-speed memory, making them ideal for training massive language models. This high bandwidth with substantial VRAM, allows AI workloads to scale efficiently, delivering greater throughput per dollar compared to CPU-only systems.
Offloading compute-intensive tasks to the GPU frees up your CPU to handle data loading, preprocessing and I/O, making the overall system more efficient. Many deep learning frameworks, like TensorFlow and PyTorch are already GPU-optimised using libraries such as cuDNN or NVIDIA Tensor Cores for seamless performance gains with minimal code changes. Even for inference, GPUs enable parallel query processing and low-latency predictions, improving responsiveness at scale.
Cloud GPUs offer the elasticity AI teams need. Whether you're experimenting on a single instance or training across a multi-node cluster with dozens of GPUs, you can scale up or down instantly based on workload demands. You pay only for what you use, no capital expense or idle hardware. Need more power? You can switch to higher-end GPUs or expand your cluster within minutes, making cloud infrastructure far more agile than traditional on-prem setups.
Cloud GPU servers are purpose-built for compute-intensive AI and ML tasks. No matter your project size or scope, if your AI workload needs speed, scalability or high memory capacity, cloud GPU servers are the way to go.
Running AI workloads in the cloud doesn’t have to break your budget. With the right strategies, you can significantly cut costs while still accessing top-tier GPU performance. Here are a few key ways to optimise your cloud GPU spend:
Spot virtual machines let you access unused GPU capacity at a significantly reduced price compared to on-demand VMs. Ideal for fault-tolerant training tasks, spot VMs on Hyperstack are available for NVIDIA A100 and NVIDIA L40 VMs, offering the same performance at a fraction of the cost.
Spot VMs come with a fixed discount percentage, making them easy to budget for. However, they carry a non-zero risk of termination without prior notice, so they’re best suited for workloads that can tolerate interruptions such as distributed training with checkpointing, batch inference or model experimentation. Learn more about Spot VMs here.
Hyperstack supports virtual machine hibernation, allowing you to pause your VM and save its current state (including memory, configuration and disk data) to persistent storage. When you're ready, you can resume the VM without a full reboot, picking up exactly where you left off.
This is perfect for workflows that span multiple sessions or require periodic pauses such as iterative training or debugging sessions, because you avoid paying for idle time while preserving your progress. It’s a smart way to maintain flexibility and reduce idle costs.
If you have ongoing or scheduled AI workloads, consider reserving GPU capacity in advance. Hyperstack offers reservation options for high-performance GPUs, including the powerful NVIDIA A100, NVIDIA H100 SXM, NVIDIA H100 PCIe and NVIDIA H200 SXM, providing lower hourly pricing and guaranteed availability, even during peak usage periods.
Reservations are ideal for teams with stable, long-term projects such as continuous model retraining or production inference, ensuring you avoid unexpected delays or resource shortages. You can also monitor GPU consumption in real time to manage timelines and avoid unexpected costs via reservation.
Cloud GPU servers for AI offer unmatched performance, scalability and cost flexibility compared to traditional CPUs. Designed for parallel processing, GPUs excel at handling AI workloads such as training large language models (LLMs), running real-time inference and optimising models for production. With faster training speeds, higher memory bandwidth and the ability to scale seamlessly, GPUs are the ideal choice for AI development.
At Hyperstack, we provide high-performance GPU computing with flexible pricing options and features like high-speed networking, pre-built OS images, DevOps tools and more, making it easier for teams to accelerate their AI projects while optimising costs. If you're new to Hyperstack, then try our GPU platform today for your AI workloads.
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Cloud GPU servers are virtual machines in the cloud with attached GPUs, ideal for AI workloads like training, inference, and fine-tuning.
GPUs handle parallel processing better, reducing training times and improving performance for large models like LLMs.
Yes, you can use hibernation or reservations to lower costs while still accessing high-end GPU performance.
LLM training, inference, vision tasks, reinforcement learning and model optimisation all perform better on cloud GPUs.
Log in to the Hyperstack console here to select your preferred cloud GPU for AI and begin running your workloads in a real cloud environment.