Cloud GPU vs. Building a GPU Machine

Machine learning requires tremendous computing power, especially for training large neural networks on massive datasets. This has made GPUs (graphics processing units) an essential piece of hardware for AI development. Their ability to perform massively parallel calculations gives them a huge performance advantage over traditional CPUs. 

However, securing access to that GPU horsepower can be challenging. Data scientists and machine learning engineers essentially have two main options – using cloud GPU services or building their own GPU-accelerated machines locally. Each approach has pros and cons in terms of cost, performance, flexibility, and more.

In this guide, we'll explore the key considerations for deciding whether to rent GPU power from a cloud provider or invest in an on-premises GPU solution. 

The Benefits of Cloud GPUs

One of the biggest benefits of GPU cloud providers is their on-demand availability and scalability. Providers like Google Cloud AI, AWS, Microsoft Azure and others allow you to provision GPU resources for machine learning workloads almost instantly. You can spin up a powerful multi-GPU cloud instance when needed, then shut it down when not, only paying for what you use.

This elastic model eliminates expensive hardware purchases and lets you instantly scale GPU power up or down based on your project's needs. Renting top GPUs like NVIDIA A100s through the cloud can deliver performance on par with an on-prem data center at a fraction of the upfront cost.

Additionally, cloud solutions provide built-in redundancy, high availability, and centralized management across geographic regions. They offer seamless access to interconnected cloud services for storage, networking, deployment, and more.

Another advantage is that cloud providers can rapidly provide the latest GPU hardware in their cloud regions. You can get access to cutting-edge silicon for AI almost immediately, without complex hardware upgrades.

Drawbacks of Cloud GPUs

While offering tremendous flexibility, cloud GPU instances do have some potential downsides to consider:

Cost at Scale – Cloud bills can quickly add up, especially for parallelized, multi-GPU workloads running 24/7. Prolonged usage may offset hardware investment costs.

Data Transfer – Shuffling huge datasets to/from the cloud incurs transfer fees that can get expensive for large models with terabytes of data.

Vendor Lock-In – Migrating GPU workloads between cloud platforms presents technical and pricing challenges.

Limited VRAM – Instance GPU memory sizes are fixed, potentially not enough for extremely large models.

Lapsed Sessions – To avoid runaway cloud GPU costs, your instances shut down after periods of inactivity.

Potential Bottlenecks – Sharing bandwidth between cloud VMs can impact networking performance.

Building a Local GPU Machine

On the flip side, building your own local GPU infrastructure provides a number of unique benefits:

Total Cost Control – After the upfront hardware costs, you have no recurring fees for GPU usage. Lower long-term cost at scale.

Data Locality – Keeping data on-prem avoids costly transfers and unpredictable cloud egress fees.

Customization – Configure systems precisely, add expansion GPUs/VRAM, adjust cooling, etc.

Dedicated Resources – No shared tenancy bottlenecks. GPU instances remain available indefinitely.

Physical Security – Hold sensitive data locally behind your own security controls.

Maximum VRAM – Install GPUs with huge memory pools for ultra-large models.

The major drawback of on-prem infrastructure is the substantial upfront capital, operating, and maintenance overhead. High-end GPU systems are extremely expensive, complex to cool, and require periodical multi-thousand dollar hardware upgrades.

Cloud vs Local Cost Considerations

It's difficult to broadly state whether the cloud or local deployment is cheaper for GPU computing, as it depends heavily on uptime, workload intensity, and duration.

The cloud offers much lower initial costs and eliminates infrastructure management. It tends to be cheaper than owning hardware for short-term, periodic GPU usage or quickly scaling resources up/down.

For projects requiring sustained, long-term GPU compute over several years, however, an on-prem solution may have lower total costs. Not being subject to recurring cloud fees eventually bridges the hardware investment gap.

Another important factor is VRAM demands. Cloud GPU instances currently max out around 16-24GB VRAM per GPU. If your AI models need 32GB, 48GB, or more, a self-built machine may be required. This provides flexibility to install GPUs with massive memory pools for cutting-edge workloads.

Performance and data transfer costs should also be weighed carefully. While the cloud allows instant scalability, egress bandwidth and persistently moving terabytes of model data can rapidly inflate costs.

Hybrid Cloud/Local GPU Solution

Due to the above trade-offs, many organizations adopt a hybrid architecture, incorporating both cloud and local GPU assets.

They may conduct initial data preprocessing and model development/experimentation on cloud instances for agility. But then shift performance-intensive training runs of finalized models to a dedicated local GPU cluster or high-powered workstations. This gets the best of both worlds.

Cloud "bursting" for peak demands also allows temporarily renting GPU resources from providers to augment local capacity. Reserving instances can lock in discounted rates for consistent workloads too.

Additionally, options like GPU virtualization and external GPU cloud services allow organizations to accelerate existing infrastructure and endpoints without procuring dedicated GPU machines.

The Evolving GPU Landscape for AI

While GPUs currently dominate AI compute, specialized hardware accelerators like Google's TPUs, Intel's Habana Gaudi AIs, and novel architectures like Cerebras' massive AI chips are emerging. These are designed specifically for machine learning workloads, rather than repurposing gaming GPU silicon.

Both NVIDIA and AMD remain committed to advancing GPU architectures optimized for rapidly evolving AI algorithms and neural network sizes. Their GPUs provide flexible programmability and proven performance that will likely keep them atop the AI compute landscape for years ahead.

That said, the dynamic competitive landscape emphasizes the benefits of cloud GPU services' hardware abstraction rather than investing heavily into on-prem architectures that could stagnate.

Conclusion

No matter your choice of cloud or local GPU infrastructure today, ensuring agile access to to the latest computational resources will remain instrumental to achieving state-of-the-art artificial intelligence development in the future. As new accelerator architectures like TPUs, custom AI chips, and novel computing fabrics emerge, maintaining flexibility will be key to rapidly leveraging the next breakthroughs for training ever-larger and more sophisticated machine learning models. Betting too heavily on any single hardware paradigm poses future risks of obsolescence. An agile hybrid strategy incorporating both cloud and on-prem resources provides the versatility to adapt as the AI landscape continues its relentless evolution.