High-Performance Computing (HPC) GPUs in Enterprise IT: Why They’re in High Demand (and Hard to Get)

Generative AI, deep learning, and other AI-powered applications need GPUs (graphics processing units) to process high-performance computing (HPC) tasks like complex calculations and large-scale simulations. 

As enterprise IT, data centers, and research institutions adopt AI, financial modeling, and cloud-based workloads, there’s been an increasing global shortage of GPUs. To keep up, companies need to rethink their procurement strategies for sourcing hardware.

In this article, we’ll discuss why high-performance computing GPUs are in demand and what alternative procurement strategies enterprises can adopt to recover from the shortage. 

The Role of GPUs in High-Performance Computing

High-performance computing requires immense processing power to handle data-intensive tasks. GPUs excel in parallel processing, with thousands of cores executing multiple calculations at once.

Compared to CPUs (central processing units) that usually process tasks sequentially, GPUs can divide work into thousands of threads and handle them simultaneously. This makes them highly effective for high-performance computing workloads like deep learning, large-scale simulations, and real-time data analytics.

GPUs process multiple workloads simultaneously to complete computations faster than traditional processors, often delivering higher performance per watt​. When optimized for power efficiency, this can result in quicker results and lower energy costs for HPC tasks

Factors Driving the High Demand for HPC GPUs

Here are some factors driving the high demand for HPC GPUs:

AI & Machine Learning Growth

AI and machine learning rely on deep learning algorithms that process massive datasets that CPUs can’t handle. GPUs have thousands of cores to speed up neural network computations. They reduce training time and allow AI applications in industries like healthcare, finance, and automation.

While alternatives like TPUs (tensor processing units) and FPGAs (field programmable gate arrays) exist, GPUs are largely preferred for training and deployment.

Cloud-based Computing

Tech giants like AWS, Google Cloud, and Microsoft Azure consume huge GPU inventories for AI training, machine learning, and high-performance computing. They rely on GPU-powered cloud services to scale workloads. 

But with increased demand, hyperscalers can secure bulk GPU supplies that affect the supply of these processors.

Scientific Research 

Medicine, physics, engineering, and climate modeling rely on GPUs to handle vast datasets and perform complex simulations for data analysis. 

For example, the Janelia Research Campus uses a high-performance computing cluster with 300 GPUs and 25 petabytes of storage to support its data-intensive neuroscience research. 

Similarly, biomedical research, epidemiological modeling, and drug discovery depend on GPUs for complex computations.

Cryptocurrency Mining

Cryptocurrency mining uses GPUs to calculate hashes, which secure and validate blockchain transactions. With parallel processing capabilities, GPUs are far more efficient than CPUs for mining.

Increased demands for crypto mining and bulk purchases for high-performance GPUs contribute to global shortages and rising costs. 

Edge Computing 

AI models running directly on edge devices rely on GPUs to analyze sensor data, detect patterns, and automate responses instantly. This reduces reliance on cloud computing, lowers latency, and improves performance, making GPUs essential for modern edge applications.

With companies investing in edge AI solutions, demand for GPUs has increased, pushing enterprises to secure high-performance hardware to meet high-capacity demands. 

IoT Devices

IoT devices generate massive amounts of data that GPUs help process locally instead of sending it to remote servers. This speeds up AI-powered analytics in security cameras, predictive maintenance, and smart city infrastructure.

GPU-Based Workloads

GPUs help improve performance for resource-intensive tasks such as AI, machine learning, and deep learning through faster data processing and analysis. When paired with advanced computing technologies, these processors allow enterprises to scale their operations and handle complex workloads.

Supply Chain Issues

GPU shortages happen not only because of high demand but also due to supply chain constraints and production shifts towards newer models. For example, NVIDIA high computational power GPUs, like the H100, are in such high demand that tech giants buy them in bulk, causing supply issues.

Manufacturers of GPU key components like CoWoS packaging and high-bandwidth memory (HBM) can’t scale production fast enough. A study predicts that if GPU demand doubles by 2026, suppliers will need to increase output by 30%, with CoWoS requiring nearly triple capacity. 

But semiconductor manufacturing takes years to expand, and geopolitical risks further disrupt supply chains, making shortages likely to persist.

Best Procurement Strategies to Extend Your HPC GPU Lifecycle

Here are alternative procurement strategies you can use to secure the GPUs necessary for high computational capacity:

Optimizing Existing GPU Resources

You can maximize your current GPUs’ performance and lifespan before seeking new procurement. 

GPU workload management tools can distribute computing tasks through virtualization and GPU partitioning. This allows multiple users or processes to share a single GPU, preventing underutilization. 

Overheating also reduces GPU lifespan. Use better airflow and liquid cooling systems for stable performance, preventing thermal throttling and hardware degradation.

Repurpose Older GPUs for Secondary Tasks

Instead of replacing older GPUs, you can repurpose them for less demanding tasks, such as data preprocessing, model inference, or visualization workloads. This offloads high-performance GPUs to critical computations while your old GPUs handle small tasks quickly.

IT Asset Disposition (ITAD) for GPU Refresh Cycles

IT Asset Disposition (ITAD) provides a structured process to refresh your GPUs by recovering value from decommissioned equipment, refurbishing GPUs for reuse, and sourcing pre-owned units from secondary markets. 

Certified ITAD providers like Inteleca can help you extend GPU lifecycles and recover value in two ways:

• Audit existing inventory to identify reusable GPUs.
• Refurbish and resell viable GPUs while responsibly recycling hardware that has reached EOL.

Secondary Sourcing for High-Quality GPUs

You can turn to secondary sources to procure high-quality or refurbished GPUs. This is a cost-effective alternative to buying new hardware while ensuring performance and reliability. 

Here are some options:

• ITAD providers and resellers: ITAD companies like Inteleca recover, test, and refurbish decommissioned GPUs from data centers, enterprises, and HPC facilities. They are trusted resellers that grade, certify, and resell these GPUs with warranties to meet your performance standards.
• Enterprise liquidation and auctions: Enterprises that upgrade hardware often liquidate their old GPUs through direct sales, liquidation firms, or auctions. You can buy these in bulk, often for a lesser price. 
• OEM refurbished programs: Some original equipment manufacturers (OEMs) like NVIDIA and AMD offer certified refurbished GPUs through their resale programs. These units undergo manufacturer testing and reconditioning to meet factory standards at a lower cost.

High-Performance Computing GPU Benchmarks

When GPUs are hard to find, benchmark tests help you compare their speed, efficiency, and performance. These tests show which GPUs work best for AI, deep learning, and scientific computing for smarter buying decisions.

Some of them include:

• Floating-point performance (TFLOPS): This determines how fast a GPU can process AI and scientific computations.
• Memory bandwidth (GB/s): This shows how quickly data moves between memory and processors.
• Tensor core performance: This metric measures AI acceleration in deep learning models.
• Energy efficiency (performance per watt): This ensures high computing power while managing operational costs.

These benchmarks help you compare GPUs like NVIDIA’s H100, AMD’s MI300, and other AI accelerators so you can invest in the best hardware for high-performance computing needs.

Benefits of Using ITAD to Meet HPC Demand

ITAD is a strategic and sustainable solution for recovering, refurbishing, and redistributing GPUs from decommissioned infrastructure. 

Here’s how it can benefit you:

Recovering GPUs from Decommissioned Infrastructure

ITAD providers audit, test, sanitize data, and refurbish processing units while maintaining a full chain of custody, making them available for resale or redeployment.

Inteleca adopts a customer-centric approach, working with our clients to decommission old hardware with maximum value return to invest in new upgrades or offset service costs. 

Our in-house ITAD experts carefully inspect each asset, maintain a full chain of custody for tracking and reporting, and extend the life of your GPUs through certified refurbishment and resale. 

Cost Savings on Enterprise-Grade Hardware

Buying new GPUs can be expensive, especially when demand exceeds supply. ITAD reduces costs by offering pre-owned or surplus new GPUs at a fraction of the price while maintaining performance standards. Responsible, secure ITAD can  help you resell old hardware to recover costs. 

Reduced E-Waste

By recycling and repurposing GPUs, ITAD reduces e-waste and the environmental impact of discarded IT equipment. You can partner with R2v3-certified ITAD providers like Inteleca who follow these standards and also implement sustainable practices into daily operations.

Compliance and Secure Data Erasure

ITAD providers follow strict data security protocols, making sure that all decommissioned GPUs undergo certified data erasure and sanitization. This reduces compliance risks related to data breaches or regulatory non-compliance when disposing of old hardware.

Maximizing ROI from Retired Hardware

You can recapture value by selling or repurposing your decommissioned GPUs through ITAD programs. Instead of letting old hardware sit idle, you can offset costs while updating your infrastructure.

Reducing Dependence on Volatile Supply Chains

The global chip shortage and geopolitical trade restrictions have made new GPUs harder to procure.The secondary market allows you to scale high computational operations without waiting for new production cycles or long lead times.

Conclusion

While alternatives like TPUs and FPGAs can process high computational tasks, GPUs are more cost-effective for performing complex learning and arithmetic tasks faster while saving energy. 

As AI applications increase, GPU demand will force enterprises to adopt different procurement strategies, including secondary market sourcing, ITAD for GPU refresh cycles, and optimization of existing hardware.
Learn more about how you can optimize your old hardware through a certified ITAD process. Contact our team today.