When an AI agent loses context mid-task because traditional storage can’t keep pace with inference, it is not a model problem — it is a storage problem. At GTC 2026, Nvidia announced BlueField-4 STX, a modular reference architecture that inserts a dedicated context memory layer between GPUs and traditional storage, claiming 5x the token throughput, 4x the energy efficiency and 2x the data ingestion speed of conventional CPU-based storage.

The bottleneck STX targets is key-value cache data. KV cache is the stored record of what a model has already processed — the intermediate calculations an LLM saves so it does not have to recompute attention across the entire context on every inference step. It is what allows an agent to maintain coherent working memory across sessions, tool calls and reasoning steps. As context windows grow and agents take more steps, that cache grows with them. When it has to traverse a traditional storage path to get back to the GPU, inference slows and GPU utilization drops.

STX is not a product Nvidia sells directly. It is a reference architecture the company is distributing to its storage partner ecosystem so vendors can build AI-native infrastructure around it.

STX puts a context memory layer between GPU and disk

The architecture is built around a new storage-optimized BlueField-4 processor that combines Nvidia’s Vera CPU with the ConnectX-9 SuperNIC. It runs on Spectrum-X Ethernet networking and is programmable through Nvidia’s DOCA software platform.

The first rack-scale implementation is the Nvidia CMX context memory storage platform. CMX extends GPU memory with a high-performance context layer designed specifically for storing and retrieving KV cache data generated by large language models during inference. Keeping that cache accessible without forcing a round trip through general-purpose storage is what CMX is designed to do.

“Traditional data centers provide high-capacity, general-purpose storage, but generally lack the responsiveness required for interaction with AI agents that need to work across many steps, tools and different sessions,” Ian Buck, Nvidia’s vice president of hyperscale and high-performance computing said in a briefing with press and analysts.

In response to a question from VentureBeat, Buck confirmed that STX also ships with a software reference platform alongside the hardware architecture. Nvidia is expanding DOCA to include a new component referred to in the briefing as DOCA Memo. 

“Our storage providers can leverage the programmability of the BlueField-4 processor to optimize storage for the agentic AI factory,” Buck said. “In addition to having a reference rack architecture, we’re also providing a reference software platform for them to deliver those innovations and optimizations for their customers.”

Storage partners building on STX get both a hardware reference design and a software reference platform — a programmable foundation for context-optimized storage.

Nvidia’s partner list spans storage incumbents and AI-native cloud providers

Storage providers co-designing STX-based infrastructure include Cloudian, DDN, Dell Technologies, Everpure, Hitachi Vantara, HPE, IBM, MinIO, NetApp, Nutanix, VAST Data and WEKA. Manufacturing partners building STX-based systems include AIC, Supermicro and Quanta Cloud Technology.

On the cloud and AI side, CoreWeave, Crusoe, IREN, Lambda, Mistral AI, Nebius, Oracle Cloud Infrastructure and Vultr have all committed to STX for context memory storage.

That combination of enterprise storage incumbents and AI-native cloud providers is the signal worth watching. Nvidia is not positioning STX as a specialty product for hyperscalers. It is positioning it as the reference standard for anyone building storage infrastructure that has to serve agentic AI workloads — which, within the next two to three years, is likely to include most enterprise AI deployments running multi-step inference at scale.

STX-based platforms will be available from partners in the second half of 2026.

IBM shows what the data layer problem looks like in production

IBM sits on both sides of the STX announcement. It is listed as a storage provider co-designing STX-based infrastructure, and Nvidia separately confirmed that it has selected IBM Storage Scale System 6000 — certified and validated on Nvidia DGX platforms — as the high-performance storage foundation for its own GPU-native analytics infrastructure.

IBM also announced a broader expanded collaboration with Nvidia at GTC, including GPU-accelerated integration between IBM’s watsonx.data Presto SQL engine and Nvidia’s cuDF library. A production proof of concept with Nestlé put numbers on what that acceleration looks like: a data refresh cycle across the company’s Order-to-Cash data mart, covering 186 countries and 44 tables, dropped from 15 minutes to three minutes. IBM reported 83% cost savings and a 30x price-performance improvement.

The Nestlé result is a structured analytics workload. It does not directly demonstrate agentic inference performance. But it makes IBM and Nvidia’s shared argument concrete: the data layer is where enterprise AI performance is currently constrained, and GPU-accelerating it produces material results in production.

Why the storage layer is becoming a first-class infrastructure decision

STX is a signal that the storage layer is becoming a first-class concern in enterprise AI infrastructure planning, not an afterthought to GPU procurement.

General-purpose NAS and object storage were not designed to serve KV cache data at inference latency requirements. STX-based systems from partners including Dell, HPE, NetApp and VAST Data are what Nvidia is putting forward as the practical alternative, with the DOCA software platform providing the programmability layer to tune storage behavior for specific agentic workloads.

The performance claims — 5x token throughput, 4x energy efficiency, 2x data ingestion — are measured against traditional CPU-based storage architectures. Nvidia has not specified the exact baseline configuration for those comparisons. Before those numbers drive infrastructure decisions, the baseline is worth pinning down.

Platforms are expected from partners in the second half of 2026. Given that most major storage vendors are already co-designing on STX, enterprises evaluating storage refreshes for AI infrastructure in the next 12 months should expect STX-based options to be available from their existing vendor relationships.

Uri Gal of the University of Sydney discusses the factors impacting the working landscape and tech-based jobs.

In the past few months, a wave of tech corporations have announced significant staff cuts and attributed them to efficiency gains driven by artificial intelligence (AI).

Companies such as Atlassian, Block and Amazon have announced they would lay off thousands of employees due to increased reliance on AI.

The narrative these companies offer is consistent: AI is making human labour replaceable, and responsible management demands adjustment.

The evidence, however, tells a more nuanced story.

The automation story is partly true

Genuine disruption is visible in specific corners of the labour market, though the scale of that disruption is commonly overstated. Research from Anthropic published earlier this month shows that although many work tasks are susceptible to automation, the vast majority are still performed primarily by humans rather than AI tools.

Moreover, some occupations are more exposed to displacement than others: computer programmers sit at the top of the list, followed by customer service representatives and data entry workers. Yet even within the most exposed occupations, AI use is still limited.

The aggregate economic data reflects this reality. A 2025 Goldman Sachs report estimated that if AI were used across the economy for all the things it could currently do, roughly 2.5pc of US employment would be at risk of job loss.

That’s not a trivial number. However, the report notes that workers in AI-exposed occupations are currently no more likely to lose their jobs, face reduced hours or earn lower wages than anyone else.

The report does note early signs of strain in specific industries. Goldman Sachs identifies sectors where employment growth has slowed that align with AI-related efficiency gains. Examples include marketing consulting, graphic design, office administration and call centres.

In the tech sector, US workers in their 20s in AI-exposed occupations saw unemployment rise by almost 3pc in the first half of 2025. Anthropic’s research also found that job-finding rates (the chance of an unemployed person finding a job in a one-month period) for workers aged 22 to 25 entering AI-exposed occupations have fallen by around 14pc since the launch of ChatGPT in 2022. This is a tentative but telling signal about where the pressure is being felt first.

These are meaningful signals, but they are sector-specific and concentrated – not the evidence of sweeping displacement that corporate announcements often imply. That gap between the evidence and the rhetoric raises an obvious question: what else might be driving these decisions?

What is the motive?

The timing and framing of the layoffs attributed to AI warrants closer examination. Corporate restructuring, over-hiring during the post-pandemic boom as demand for online services soared, and pressure from investors to demonstrate improved profit margins are all forces operating at the same time as genuine advances in AI.

While these are not mutually exclusive explanations, they are rarely acknowledged alongside one another in corporate communications.

There is a powerful financial incentive for companies to be seen to be embracing AI aggressively. Since the launch of ChatGPT, AI-related stocks have accounted for about 75pc of S&P 500 returns.

A workforce reduction framed around AI adoption sends a signal to investors that a straightforward cost-cutting announcement does not. A company making AI-related innovations looks a lot better than one sacking staff due to declining revenues or poor strategic decisions.

It is also worth distinguishing between two kinds of workforce reduction. In the first, AI genuinely increases productivity to the point where fewer workers are needed to produce the same output. In the second, staff reductions are not a consequence of AI, but a way to fund it.

Meta illustrates this distinction. The social media giant is reportedly planning to lay off as much as 20pc of its workforce, while simultaneously committing $600bn to build data centres and recruit top AI researchers.

In this case, the workers being let go are not being replaced by AI today; they are subsidising the AI bet their employer is making on the future.

The more plausible future

The big picture is likely one of transformation rather than elimination. According to a recent PwC report, employment is still growing in most industries exposed to AI, although growth tends to be slower than in less exposed sectors.

At the same time, wages in AI-exposed industries are rising roughly twice as fast as in those least touched by the technology. Workers with AI skills command an average wage premium of about 56pc across the industries analysed.

Together, the data points toward a flattening of the traditional workplace pyramid rather than mass displacement. Firms require fewer junior employees for routine analytical and administrative work, while experienced professionals who deploy AI tools effectively become more productive and command greater value.

AI is a consequential technology and will have a significant impact in the long term. What is in doubt is whether the dramatic, AI-attributed workforce reductions announced by individual companies accurately reflect that trajectory, or whether they conflate genuine technological change with decisions that would have been made regardless.

Making this distinction is not merely an academic exercise. It shapes how policymakers, educators and workers themselves understand the nature of the disruption they are navigating.

By Uri Gal

Uri Gal is a professor of business information systems at the University of Sydney Business School. His research focuses on the organisational and ethical aspects of digital technologies. He is particularly interested in the relationships between people and technology, and in the changes in the nature of work associated with the introduction of algorithmic technologies.

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When a water-treatment plant outside Denver discovered an algae problem in its pipes, it did not call an engineering firm. It called the students.

The aquatic robotics team at the Innovation Center at St. Vrain Valley Schools in Longmont, Colorado, sent underwater robots into the facility, collected data, identified the algae species and helped eradicate it. The plant now contracts with the student team for quarterly checkups. Neighboring towns have started calling, too.

This is not a simulation or a classroom exercise conjured up to look like real work. It is real work, and it reflects a broader shift underway in districts. Increasingly, schools are building career learning pathways that connect students directly with professional challenges, industry mentors and, in some cases, a paycheck.

The Case for Real Work

The urgency behind these efforts is hard to ignore. A 2023 review from the American Institutes for Research, drawing on two decades of studies, found that career and technical education participation has statistically significant positive impacts on academic achievement, high school completion, employability skills and college readiness.

The question districts are now wrestling with is not whether to offer career pathways, but whether those pathways lead anywhere real.

Policy leaders are paying attention. The Education Commission of the States has identified building aligned career pathways and removing barriers to economic opportunity as one of its top priorities through 2027.

At St. Vrain, Assistant Superintendent of Innovation Joe McBreen has spent years trying to answer that question through a program known as project teams.

After school each day, roughly 264 students log in at the district’s Innovation Center and begin work as paid district employees, billing hours against accounts for actual clients. Students can join a drone show team, a cybersecurity unit, an AI development group or a dozen other teams, rotating among them as their interests evolve.

“It’s low threat, high reward,” says McBreen. “Students get paid, grow their network, develop soft skills and test drive careers. And if they get into a team and realize it’s not for them, there’s real value in that, too.”

The model relies heavily on industry mentors who bring in real work rather than invented classroom projects. Damon Brown, a senior cybersecurity adviser for the U.S. Department of State focused on Ecuador, mentored seven St. Vrain students on a complex assignment.

He asked them to design the architecture for a cyber intelligence fusion center using open-source tools — work that could have cost hundreds of thousands of dollars if contracted from a professional firm.

“The students knocked it out of the park,” says Brown.

They built the system architecture, wrote user manuals, recommended equipment and conducted a threat analysis of countries surrounding Ecuador. Brown was so impressed he is now hiring six St. Vrain interns.

“This experience binds people together,” he says.

The program also has a way of growing in unexpected directions. After one student’s grandparent was victimized by a cybercrime, the cybersecurity team created an awareness curriculum for senior citizens. They taught five classes to 24 senior citizens in the first year; the second session was standing room only. Senior facilities now pay the students to come in and teach.

Meanwhile, the drone team flies commercial shows for companies across the country on Friday afternoons, billing clients at rates few drone pilots in the country can match. One former member is now studying aerospace engineering and using money from drone flying to help pay for college.

Taking the Model Out West

St. Vrain’s work has drawn attention from educators around the country, some of whom are adapting pieces of the model to fit their own communities.

Kris Hagel, chief information officer of Peninsula School District in Washington state, visited the Innovation Center and came away convinced he could build something similar.

Two years ago, Peninsula launched a paid drone internship program, starting with seven students and gradually expanding. Students work alongside industry partners while learning how to navigate FAA regulations, program autonomous flight paths and repair drones.

“When you’re willing to look at what’s cutting edge and think innovatively without being constrained by traditional systems, you can create opportunities for kids that transcend what we think of as traditional education,” says Hagel. “This program has become so much more than I thought was possible.”

The district partnered with Firefly Drone Systems, one of the few American drone manufacturers, to train students and help them operate drone shows.

The program also includes multiple roles beyond piloting, including marketing, animation design and equipment maintenance. Hagel envisions a future where students studying business management hire other students to operate the program.

A skilled drone operator who leaves high school with the capital to purchase equipment can enter a six-figure career almost immediately, says Hagel.

Finding the Problem First

Not every district is building toward robotics contracts or drone shows. For Michele Davis, CTE department chair at Metropolitan School District of Steuben County in Indiana, the real-world pathway is entrepreneurship.

Working with the StartED Up Foundation, Davis guides students through a three-year sequence: identifying an actual problem, developing a solution, building out the business model and presenting it to real audiences.

Students take “opportunity walks” around the school, documenting everyday frustrations and brainstorming solutions. They learn how to market their ideas professionally by practicing elevator pitches, presenting case studies to various audiences and explaining their ideas to elementary school students.

“Opportunities are everywhere,” says Davis.

The ideas that emerge can be surprisingly practical. One student designed a reversible outfit to solve a quick-change problem in theater productions. Another class developed a mobile trailer concept that could help unhoused people access hygiene services.

Beyond the business concepts themselves, Davis says the program focuses heavily on communication skills and confidence. “We get students comfortable doing things that are normally uncomfortable,” she says.

A Credential, Not Just a Class

At Suffern Central School District in Rockland County, New York, Superintendent P. Erik Gundersen has taken yet another approach.

Through a partnership with the League of Innovative Schools and curriculum provider Paradigm, the district launched a three-year cybersecurity certification pathway embedded directly into the high school. About 60 students are currently enrolled.

The program was designed to reach students who might not otherwise see themselves in a cybersecurity career. The district actively recruited students from immigrant communities and others who are new to the U.S.

Students work in a “sandbox” environment that simulates real cyber incidents, allowing them to practice identifying threats and responding to attacks.

“The means to send a kid to college is not as great as it was, and a lot of what we’re reading questions the importance of a college education,” says Gundersen.

Those economic realities, he says, are pushing districts to rethink how they prepare students for the workforce.

Career credentials embedded with traditional high schools can open doors for students who may not otherwise have clear pathways into high-skill industries.

Education That Looks Like Life

Across these programs, the details vary widely, but the philosophy is the same: Authentic experience is not a supplement to education. It is education.

As McBreen says, “I encourage districts to expand their vision. Anyone can do this. Start small.”

The America’s Talent Strategy: Building the Workforce for the Golden Age report, published last year by the U.S. Departments of Commerce, Education, and Labor, identified a significant engineering and skills gap. The 27-page report concluded that the shortage of talent in essential areas—including advanced manufacturing, artificial intelligence, cloud computing, and cybersecurity—poses significant risks to U.S. economic and technological leadership.

To help attract talent in those fields, the Labor Department last month introduced incentives for apprenticeships, including a US $145 million “pay for performance” grant program. The funding aims to develop registered apprenticeships in high-demand fields including artificial intelligence and information technology.

Reacting to the urgent national need for targeted workforce development were members of IEEE Young Professionals, led by Alok Tibrewala, an IEEE senior member. He is a cochair of the IEEE North Jersey Section’s Young Professionals group.

“As a software engineer, this impending shortage concerns me because I believe that the U.S. AI and cybersecurity skills gap would show up first in the early-career pipeline,” Tibrewala says. “Students will be entering the U.S. workforce without enough hands-on experience building secure AI-enabled enterprise and cloud systems, and this gap will persist without practical, mentor-led training before graduation.”

Tibrewala led a strategic planning session with representatives from the New Jersey Institute of Technology, IEEE Member and Geographic Activities, and IEEE Young Professionals to discuss holding an event that would provide practical, industry-relevant training by experts and IEEE leaders.

“I was able to establish a partnership with NJIT, recruit speakers, design the event’s agenda, and promote the event to ensure it was aligned with the strategy outlined in the workforce report,” he says. “This effort aligns with broader U.S. workforce development priorities focused on industry-driven skills training in critical technology areas.”

The IEEE Buildathon event was held on 1 November at NJIT’s Newark campus. More than 30 students and early-career engineers heard from 11 speakers. Through interactive workshops, live demonstrations, and networking opportunities, they left with practical, employer-aligned skills and clearer career pathways for AI-era skills-building.

Tibrewala chaired the event and also serves as chair of the IEEE Buildathon program.

Session takeaways

Region 1 Director Bala S. Prasanna, a life senior member, gave the keynote address. He emphasized the need for universities, industry practitioners, and IEEE volunteer leaders to collaborate on programs to enhance technical skills.

IEEE Member Kalyani Matey, cochair of the IEEE North Jersey Section’s Young Professionals, conducted a workshop on how to build one’s personal brand and a responsive network. Participants received valuable insights about résumé building, effective communication strategies, and enhancing their visibility and employability.

“Over time, this kind of structured, employer-aligned training will help increase confidence, employability, and technical readiness across the country. With sustained support, programs like the IEEE Buildathon can become a practical bridge from education to industry in the AI era.” —Alok Tibrewala

Tibrewala led the Unlocking AI’s Potential: Solving Big Challenges With Smart Data and IEEE DataPort session. The web-based DataPort platform allows researchers to store, share, access, and manage their research datasets in a single, trusted location. He discussed needed skills including AI literacy, strong data handling and dataset stewardship, and turning data into actionable insights.

Chaitali Ladikkar, a senior software engineer, delivered the insightful Brains Behind the Game seminar. Ladikkar, an IEEE member, highlighted the transformative impact AI is having on gaming and game engine technologies. She explained how AI is reshaping game development. She also covered how machine learning is being used for animation, faster content generation and testing of new titles. Her seminar received enthusiastic feedback from participants.

The Building Better Business Relationships DiSC workshop provided insights into enhancing professional relationships and communication within an engineering workforce. DiSC is a behavioral self-assessment used to understand an individual’s communication style and to adapt to others.

Participant experience and testimonials

The event received high praise from participants for its practical and industry-relevant content, according to Tibrewala.

“This training significantly enhanced my understanding and readiness for industry roles, filling gaps my regular academic coursework did not fully address,” said Humna Sultan, an IEEE student member who is a senior studying computer science at Stevens Institute of Technology, in Hoboken, N.J.

“The Buildathon was structured around real engineering challenge scenarios that deepened my understanding of AI and cloud technologies,” said Carlos Figueredo, an IEEE graduate student member who is studying data science at the University of Michigan, in Ann Arbor. “It boosted my confidence and practical skills essential for the industry.”

Bavani Karthikeyan Janaki said “it was incredible to see how technology and sustainability came together to drive real-world impact, thanks to the dedicated efforts of the organizers including Tibrewala, Matey, and the IEEE North Jersey Young Professionals.” Janaki is pursuing a master’s degree in computer and information science at Long Island University, in New York.

Funding and collaborative efforts

The Buildathon was made possible through grants from the IEEE Young Professionals group and funding from the IEEE North Jersey Section and IEEE Member and Geographic Activities. Their support shows how IEEE’s professional organizations can collaborate to address workforce needs by supporting the delivery of technical sessions that strengthen early-career pipelines.

Future plans and a call to action

Building on the event’s success, Tibrewala and Matey plan to make the IEEE Buildathon an ongoing initiative. They are exploring ways to expand it to additional university campuses and IEEE communities.

Tibrewala says they plan to refine the format based on participant feedback and lessons learned. To support consistent quality, he and Matey say, they are working on a playbook for organizers that will include a repeatable agenda, a workshop template, speaker guidelines, and post-event feedback forms.

The approach depends on continued coordination among host universities, local IEEE sections, and Young Professional volunteers, Tibrewala says.

“Enabling other groups to run similar events,” he says, “can help more students and early-career engineers gain practical exposure to AI, data, cloud, cybersecurity, and other key emerging technologies in a structured setting.

“Efforts like this help translate national workforce priorities into real training that students and early-career engineers can apply immediately to their projects. This also helps close the gap between classroom learning and the realities of building secure, reliable systems in production environments. Over time, this kind of structured, employer-aligned training will help increase confidence, employability, and technical readiness across the country.

“With sustained support, programs like the IEEE Buildathon can become a practical bridge from education to industry in the AI era.”