Knowledge Base

For companies building robotics, industrial vision, medical devices, autonomous systems and other embedded AI products, an NVIDIA Jetson end-of-life notice is not just a technical update.

It is a procurement event.

When a production module approaches end of life, OEMs may have only a limited window to secure enough inventory to support existing products through the remainder of their commercial lifecycle.

That makes last-time-buy planning one of the most important — and often underestimated — parts of embedded AI procurement.

In 2026, this became particularly relevant for several legacy NVIDIA Jetson platforms.

NVIDIA has confirmed revised lifecycle timelines for Jetson TX2 NX, Xavier NX and AGX Xavier modules. The company cited increasing difficulty sourcing the LPDDR4 memory used in these products as a key reason for the change. NVIDIA's last-time forecast and purchase-order deadline for the affected modules was July 15, 2026, with final shipments scheduled no later than July 15, 2027.

For OEMs still shipping products based on these platforms, the question is no longer simply:

“When will this module reach EOL?”

The more important question is:

“How many modules will we need before supply becomes dependent on remaining channel inventory?”

For decades, data center cooling was primarily a facilities consideration.

With modern AI infrastructure, it has become part of the compute architecture itself.

An NVIDIA H200 SXM GPU can operate at up to 700W TDP, while newer Blackwell systems push power density significantly higher. At the extreme end, an NVIDIA GB200 NVL72 rack combines 72 Blackwell GPUs and 36 Grace CPUs in a liquid-cooled rack-scale architecture. NVIDIA documentation puts a fully loaded GB200 NVL72 rack at approximately 120 kW.

At those densities, choosing between air and liquid cooling is no longer simply a question of cooling efficiency.

It affects:

• rack density
• power distribution
• data center design
• deployment cost
• server selection
• maintenance
• scalability
• and ultimately how much AI compute can be deployed in a given facility

So where does traditional air cooling still make sense, and when does liquid cooling become necessary?

Choosing an AI GPU is no longer just about selecting the latest NVIDIA accelerator. Today's enterprise buyers must also decide which platform best suits their workloads and infrastructure.

The same GPU architecture may be available in multiple form factors— PCIe, SXM, and NVL— each designed for different deployment scenarios.

Understanding these differences is essential for infrastructure architects, procurement teams, and AI engineers planning new clusters or expanding existing data centers.

In this guide, we'll explain how these platforms differ, where each excels, and how to make the right investment.

The modern data center is under constant pressure to do more with less. Organizations must support growing workloads, AI initiatives, cloud environments, and digital transformation projects while keeping infrastructure costs under control.

As a result, many businesses are turning to refurbished servers and used data center equipment as a strategic alternative to purchasing brand-new hardware. What was once considered a budget option has become a mainstream procurement strategy for enterprises, service providers, data centers, and IT teams worldwide.

However, success in the refurbished hardware market depends on more than finding a low price. Buyers must identify trusted suppliers, verify quality standards, and ensure that equipment meets both technical and operational requirements.

This guide explains everything you need to know about purchasing refurbished data center hardware while maximizing IT infrastructure savings and minimizing risk.

Just a few years ago, the AI hardware market was defined by one word:

shortage.

Companies waited months for GPU allocations.

Prices surged.

Availability disappeared.

In some cases, organizations purchased whatever hardware they could secure — regardless of cost.

But in 2026, the market is beginning to look very different.

The conversation is shifting from:

“How do we get GPUs?”

to:

“What do we do with excess GPU inventory?”

So what changed?

In the electronics and IT hardware industry, one assumption has persisted for decades:

Newer date codes mean better quality.

From semiconductors to data center components, buyers often request “recent date codes” as a condition for purchase — sometimes even rejecting perfectly functional inventory based solely on manufacturing date.

But in reality, this belief is largely a myth.

And in 2026, it’s becoming increasingly clear that date codes are one of the most misunderstood factors in hardware procurement.



The AI infrastructure boom is no longer driven by GPUs alone.

In 2026, two critical components are becoming just as important — and increasingly expensive:

HBM (High Bandwidth Memory) and enterprise SSDs.

As demand for AI compute continues to scale, the cost dynamics of memory and storage are shifting rapidly, creating new challenges for data centers and infrastructure buyers.

Understanding these trends is essential for planning AI deployments effectively.

The AI infrastructure boom is no longer driven by GPUs alone.

In 2026, two critical components are becoming just as important — and increasingly expensive:

HBM (High Bandwidth Memory) and enterprise SSDs.

As demand for AI compute continues to scale, the cost dynamics of memory and storage are shifting rapidly, creating new challenges for data centers and infrastructure buyers.

Understanding these trends is essential for planning AI deployments effectively.

The race to build AI infrastructure has created one of the most aggressive hardware markets in recent history.

But in 2026, a new reality is emerging: many companies are significantly overpaying for AI hardware — without realizing it.

Not because they’re making bad decisions.

But because the market has changed faster than their strategy.

If you’re operating a data center or managing AI infrastructure, there’s a growing risk you may be overlooking:

your existing GPU hardware could be losing value faster than your ROI model assumes.

In 2026, the AI hardware market is no longer driven by scarcity alone - it’s driven by timing, supply dynamics, and infrastructure shifts.

In a market dominated by the latest GPU releases, it’s easy to assume that newer always means better.

But in 2026, many organizations are making a different decision.

Instead of chasing the newest architectures, they are prioritizing something else: return on infrastructure investment.

And for a growing number of teams, that brings them back to a proven platform — the NVIDIA A100.

The rapid growth of artificial intelligence has transformed GPUs into some of the most valuable assets in modern data centers. Companies invest heavily in GPU infrastructure to support machine learning models, generative AI platforms, and large-scale analytics.

However, unlike traditional servers, AI hardware depreciates much faster.

New GPU architectures are released regularly, performance per watt improves quickly, and organizations constantly upgrade their infrastructure to remain competitive.

This raises an important question for many companies:

When is the best time to sell your GPUs?

Understanding how AI hardware depreciation works can help organizations recover significantly more value from their infrastructure.

Artificial intelligence infrastructure is evolving at an unprecedented pace. Organizations are deploying increasingly powerful GPU clusters to support machine learning, generative AI, and data-intensive workloads.

However, this rapid innovation cycle creates an overlooked financial challenge: hardware obsolescence and idle capacity.

Many companies accumulate surplus GPUs as they upgrade infrastructure, migrate workloads, or scale clusters differently than originally planned.

From an IT perspective, idle hardware is simply unused capacity.

From a financial perspective, it is trapped capital.

Organizations that treat GPUs as depreciating financial assets rather than static equipment can recover value, optimize capital allocation, and reduce infrastructure costs.

AI infrastructure in 2026 is no longer defined by GPU price alone.

Organizations investing in high-performance accelerators often focus on acquisition cost — but the real economics of AI deployment extend far beyond hardware procurement.

An 8×H100 AI server does not cost what it appears to cost.

Because GPU pricing represents only the visible layer of AI infrastructure economics.

The AI hardware cycle is accelerating faster than traditional enterprise infrastructure refresh models can adapt. With NVIDIA H100 widely deployed, H200 entering production environments, and next-generation GPUs on the horizon, decision-makers face a critical question:

Upgrade now — or wait?

As artificial intelligence expands beyond the cloud into physical environments—factories, cities, hospitals, and retail—the demand for compact, high-performance edge computing solutions has never been higher. Nvidia’s Jetson Xavier modules offer purpose-built platforms designed to accelerate AI inference in power-constrained, real-time environments.

As AI workloads continue to scale and data centers evolve, the demand for high-bandwidth, low-latency interconnects is at an all-time high. NVIDIA, a leader in accelerated computing, addresses this challenge with its 800Gbps Twin-Port OSFP transceivers — essential components for high-performance networking environments.

In the rapidly evolving landscape of artificial intelligence (AI) and high-performance computing (HPC), selecting the right GPU is pivotal. NVIDIA's A100 and H100 GPUs stand out as industry leaders, each offering unique advantages tailored to specific workloads. Additionally, other GPUs like the V100 and RTX 6000 provide compelling options for various applications.

In the rapidly evolving tech industry, data centers face the dual challenge of acquiring high-quality hardware cost-effectively and managing the decommissioning of outdated equipment. Partnering with REVO.tech’s offers strategic advantages in both procurement and asset disposition.

Excess or surplus electronic components are a common challenge in the supply chain, affecting manufacturers, distributors, and retailers. Understanding the reasons behind surplus inventory is essential for optimizing inventory management and recovering costs. At the same time, REVO.tech offer innovative solutions to help businesses turn their excess inventory into profits.

Excess or surplus electronic components are a common challenge in the supply chain, affecting manufacturers, distributors, and retailers. Understanding the reasons behind surplus inventory is essential for optimizing inventory management and recovering costs. At the same time, REVO.tech offer innovative solutions to help businesses turn their excess inventory into profits.

As cloud providers continuously upgrade their infrastructure to meet growing demands, they often face the challenge of dealing with surplus or outdated hardware. Instead of letting this valuable equipment sit idle, selling overstock hardware offers a smart solution for optimizing costs and making room for new technology. Here’s how cloud providers can save money and maximize their return on investment (ROI) by liquidating excess infrastructure.

The data center landscape is evolving rapidly, driven by the increasing demands of artificial intelligence (AI), machine learning, big data analytics, and cloud computing. At the heart of this transformation are the innovations in GPU and CPU hardware, which play a crucial role in enhancing data center efficiency, performance, and scalability. Here, we explore the emerging trends in GPU and CPU technology and how they are poised to impact data center and cloud infrastructure over the next few years.

As data centers evolve to meet the growing demands of cloud computing, artificial intelligence, and big data applications, optimizing rack density has become a critical factor in achieving high performance and efficiency. Selecting the right servers plays a vital role in ensuring that data centers can handle increased workloads while minimizing costs, energy consumption, and physical space requirements. Here’s a guide to the best practices for optimizing rack density in high-performance data centers.