An AI Accelerator is a deep learning or neural processor created specifically for inference and to improve the performance of an AI task. While Graphics Processing Units (GPUs) are the most common type, other specialized accelerators include Tensor Processing Units (TPUs), Data Processing Units (DPUs), Field-Programmable Gate Arrays (FPGAs), and Application-Specific Integrated Circuits (ASICs).
With so many acronyms to remember (and even more accelerator types left unnamed), we will focus on GPUs because their highly parallel architecture makes them incredibly versatile for a wide range of AI workloads.
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GPU acceleration
GPUs are an in-demand commodity throughout the IT industry. Manufacturers like NVIDIA and AMD are powering the world’s insatiable appetite for artificial intelligence and machine learning applications.
An AI GPU accelerator is responsible for making hundreds of thousands of calculations in parallel, and it’s used in every facet of AI, large language models (LLMs), data analytics, and high-performance computing.
Such widespread adoption not only highlights their critical role in advancing modern technology but also explains why GPU demand continues to outpace supply. This leads us to a crucial question: Can your cloud hosting provider truly deliver the AI GPU hosting you need? Let’s dive into what that really means with 5 things you need to know.
1: Pick the GPU your workload requires
This might sound like common sense, but it’s important to understand the GPU accelerator hardware available from your hosting provider. GPUs are not created equal; they vary massively in specification and capability.
It’s important to know what VRAM does, what tensor cores are, and what an NVLink interconnect does; otherwise, it’s very easy to overspec and overpay for GPU resources if you don’t understand exactly what’s needed.
Understand your workload: Do you want to train your own AI model, use private LLMs, or perhaps need a chatbot application? Different AI tasks require different GPU specifications.
Understand data size and IO: AI models are typically trained on huge datasets, so you need a GPU that can process data at a good rate, and you need underlying storage, ideally NVMe SSDs that can keep up to prevent bottlenecks.
Consider future scalability: Ask yourself: Will your AI project grow? You need a hosting provider that can grow with you because you may need a bigger server (more memory, faster CPU) in the near future.
Get developer feedback when choosing a framework: Does the hosting environment support your preferred framework tools out of the box? Ask your devs what they want to use; popular AI frameworks include TensorFlow, PyTorch, and JAX.
2: Choose a provider with the necessary hardware platform
GPUs are very important for AI workloads, but it’s also important to consider the underlying hardware and ensure that it’s fit for purpose. Network architecture and interconnects are critical for maximizing the performance of AI accelerators when hosted in the cloud.
Don’t forget the importance of CPUs: CPUs are still critical for AI as they control the throughput of data to all aspects of the cloud platform. You need a provider that uses the latest CPU architectures, such as Intel Xeon or AMD EPYC, and examines the number and the speed of the CPU cores.
Go beyond high-speed networking: Fiber-optic connected networking is essential for AI platforms to function with low latency. There are 3 types of networking commonly deployed:
InfiniBand provides very low-latency and high-bandwidth communication between nodes (servers) containing GPUs. This is ideal for large-scale distributed AI clusters.
NVLink is NVIDIA’s high-speed interconnect for direct GPU-to-GPU communication within a single server. It’s needed for multi-GPU setups and is great for preventing bottlenecks.
High-Bandwidth Ethernet (e.g., 100GbE+) offers affordable performance for distributed AI and high-performance storage.
Latency: The ultimate goal is to choose the best hardware that will achieve the lowest latency. This is crucial because rapid GPU IO prevents bottlenecks and improves efficiency.
3: Embedded cloud AI ecosystem
One major benefit of GPU hosting is the fact that it’s easy to integrate with existing services from a provider. Pick managed services that work for you. Popular integrations include hooking into cloud storage layers, picking managed security services, and backups.
If you are a business, server management options are a great way to ensure optimal performance and uptime, letting you focus on developing your AI application whilst the provider manages the underlying infrastructure, load balancing, MFA, Antivirus, Intrusion Prevention, and DDoS Protection behind the scenes.
4: Cost optimization
It’s essential to keep on top of your operational expenditure, especially when using GPU accelerators. Costs can spiral if you make inefficient deployment decisions, overspecify your server requirements, or leave resources running idle around the clock. Costs vary significantly, so shop around and pick a provider that offers the hardware you need for a cost that is sustainable.
Cloud GPU hosting is the way forward (unless you can afford about $40,000 for a decent-spec GPU), do not forget about all the cooling and power requirements too. Remember to optimize your instance sizing, monitor and turn off idle resources, and take advantage of multi-instance GPU capabilities, where providers essentially slice up a GPU into smaller and very affordable partitions.
5: Support, reliability, and compliance
These factors underline a provider’s ability to deliver GPU hosting that meets the needs of modern business. You may run into blockers or issues that could prevent you from releasing on schedule. Having 24x7x365 support skilled in AI/ML available for when things go wrong is vital to business continuity.
Look for strong uptime guarantees and providers that back up their claims with service credits if the unexpected happens. They should also demonstrate proven redundancy capabilities, disaster recovery, and a proactive approach to monitoring to safeguard your expensive GPU operations.
Beyond that, ensure your chosen provider meets all necessary compliance standards for your industry, whether it’s GDPR, HIPAA, or SOC 2. Understand where your data will reside and confirm the provider has strong security controls like encryption and proper access management in place. Finally, always clarify the shared responsibility model so you know exactly what security aspects the provider handles versus what falls to you.
Key takeaways
Choosing the right cloud GPU hosting means looking beyond just raw power. It’s about picking a provider that meets your specific requirements, delivers strong, flexible server infrastructure, and offers comprehensive tools for integration.
By optimizing costs and prioritizing critical areas like expert support, reliability, and adherence to compliance standards, you ensure your operations run efficiently and without unexpected hitches.
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