NVIDIA GTC 2026
Enterprise AI moves from promise to production at the Nvidia GTC AI Conference & Expo, where industrial-scale innovation takes center stage. During the event, theCUBE covers next-generation GPUs, AI f
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- All three pillars present:developer + deepTech + cSuite (developer + deepTech + cSuite)
Developer
6 citationsFor practitioners shipping against this infrastructure
Developer Infrastructure Shifts at GTC 2026
The most significant architectural shift emerging from GTC 2026 is the introduction of Context Memory Extension (CMX) as a dedicated storage tier for KV cache in AI clusters. As Ace Stryker from Solidigm explained, "What's new now is the third job. It feels like storage kind of got a promotion this year, right? And that third job is new dedicated nodes specifically for storing context memory or KV cache." This represents a fundamental departure from traditional two-tier storage architectures, where storage served either local GPU feeding or network-attached shared storage.
The technical implications are substantial for developers building inference applications. Val Bercovici from WEKA demonstrated that CMX implementations can deliver "550% more tokens" from the same hardware investment through storage-memory arbitrage. The proof of concept with Firmus showed 6.5x improvement in token throughput, effectively creating the equivalent of "five and a half new data centers out of thin air" for agent workloads. This isn't theoretical—it's production-ready infrastructure that developers can leverage today.
NVIDIA's Dynamo 1.0 release fundamentally changes how developers should architect inference applications. Andy Pernsteiner from VAST Data noted that "if you can offload previously computed attention data from an LLM session, if you put it onto storage, yes, it's slower to initially fetch it, but what it means is that the GPU can be used for more active sessions." The result is a 10x improvement in inference capability from single GPU servers, achieved by asynchronously fetching session data while GPUs handle new requests.
The storage density requirements are driving new form factors that developers need to understand. CT Sun from AIC outlined the emerging storage hierarchy: "G1 is actually inside the GPU, so it's the HPM. And also G2 is a system memory. So G3 is a local SSD... But those KV cache, when you create those content, it will keep increase and increase. And one day overflow, you need still have something to keep it. So you have G3.5, we call it content extended memory storage." This G3.5 tier represents a new architectural layer that developers must plan for in their application designs.
For developers working with multi-agent systems, the infrastructure implications extend beyond storage. Steve Kearns from Elastic emphasized that "when you start to let that agent loose to call these tools in a loop, run retrieval, take the answer that it got from the first question, ask a second question. At any point in that process, if you're giving it the wrong context, the wrong information, it's going to make a wrong choice." This means vector search and hybrid search architectures are no longer optional—they're essential infrastructure components for production agent deployments.
The power and cooling requirements are also shifting development considerations. Kannan Soundarapandian from Texas Instruments revealed that current 48-54 volt delivery systems require "200 kilograms of copper" per rack, scaling to "200,000" kilograms for gigawatt data centers. The move to 800-volt delivery systems isn't just about efficiency—it's about making AI infrastructure physically deployable at scale, which affects where and how developers can access compute resources.
Deep Tech
8 citationsFor analysts, investors, and infrastructure architects
Deep Tech: The Memory Wall Breaks Open
The inference era has fundamentally shifted the AI infrastructure battleground from training clusters to memory-centric architectures, creating a trillion-dollar market realignment that's redefining datacenter physics and competitive positioning across the entire stack.
The most significant development at GTC 2026 wasn't another GPU announcement—it was NVIDIA's explicit acknowledgment that memory, not compute, has become the primary constraint in AI production systems. As Val Bercovici from WEKA observed, "March 2026 is a fundamentally different 180 degree different conversation where there's huge demand... placing acute pressure on this thing called KV cache, which is very, very memory centric." This shift represents a complete inversion of traditional datacenter economics, where storage was historically an afterthought relegated to cost optimization discussions.
The technical drivers behind this transformation are stark. Ace Stryker from Solidigm laid out the mathematical reality: "Before long, you're into the petabytes or dozens of petabytes of context memory that these models need to be able to keep track of to continue delivering value... even the new Vera Rubin stuff that's coming online this year from NVIDIA, look at their NVL72 system. I think between GPU memory and CPU memory, you have like 70 terabytes, which is a lot, right? But when you're talking about context memory into the petabytes, that's where you hit the wall." The emergence of NVIDIA's CMX (Context Memory Extension) platform represents their acknowledgment that GPU-centric architectures cannot scale to meet inference demands without fundamental storage integration.
This memory wall crisis is creating unprecedented opportunities for infrastructure providers who can deliver storage-memory arbitrage. WEKA and Firmus demonstrated a proof-of-concept showing "6.5 times more, so 550% more tokens" from the same CapEx and OpEx investment through intelligent KV cache management. As Val Bercovici explained, "It's as if in a macro scenario, you just created five and a half new data centers out of thin air to serve agents." This isn't incremental improvement—it's architectural disruption that makes existing GPU deployments obsolete without storage co-design.
The competitive implications extend far beyond traditional storage vendors. Andy Pernsteiner from VAST Data revealed they're seeing "10X improvement in inference capability out of a single GPU server" through their Dynamo integration with NVIDIA. Meanwhile, companies like Runpod are reporting that "GPUs paired with high quality storage... increases our margins by 12%" because "GPUs make more money when they have storage attached to them." This creates a new tier of infrastructure competition where storage performance directly translates to GPU utilization economics.
The supply chain ramifications are already visible. CT Sun from AIC described the emergence of "G3.5, we call it content extended memory storage" as a new tier between local SSDs and network storage, specifically designed for KV cache overflow. Pompey Nagra from Solidigm noted they're "building various different form factors, E1.S, E3.S" to accommodate these new storage tiers, while the company has announced ambitions to "double" their 122 terabyte SSD density "in the near future."
However, the power and cooling constraints remain formidable. Kannan Soundarapandian from Texas Instruments warned that current 48-54 volt power delivery requires "200 kilograms of copper" per rack, scaling to "200,000" kilograms for gigawatt datacenters. The solution involves "going to higher voltages" like 800V systems, but this requires fundamental electrical infrastructure redesign that most operators haven't planned for.
The broader market signal is unmistakable: the AI infrastructure stack is undergoing its most significant architectural shift since the introduction of GPUs for training. As Kevin Cochrane from Vultr noted regarding Jensen's trillion-dollar infrastructure prediction, "A trillion dollars in one year for AI infrastructure, it doesn't actually blow my mind... Everything that we know and do today in the digital world and in the physical world, it's all going to get rebuilt." The companies positioning themselves at the intersection of memory, storage, and inference orchestration are capturing disproportionate value in this rebuild.
The inference era demands a complete rethinking of datacenter architecture around memory-centric design. The winners will be those who can deliver storage-memory arbitrage at scale, while traditional GPU-only deployments become stranded assets in the new economics of AI production.
C-Suite
4 citationsFor executives making bet-the-company calls
C-Suite: The Memory Wall Is Breaking — And It's Creating Trillion-Dollar Opportunities
The AI industry has hit an inflection point where memory and storage architecture will determine who wins the inference era. Context memory demands are exploding from single-shot prompts to multi-agent workflows requiring petabytes of persistent state. This isn't just a technical challenge — it's a fundamental economic shift that's creating new value tiers and forcing enterprises to rethink their entire AI infrastructure stack.
• Memory arbitrage is delivering 6.5x more tokens from the same hardware investment. Proof-of-concept deployments show that intelligent storage-to-memory arbitrage can extract 550% more performance from existing GPU investments, effectively creating "five and a half new data centers out of thin air" without additional CapEx.
• The inference era demands a complete rethink of storage economics. With context windows exploding to millions of tokens and thousands of concurrent users, traditional 48-54 volt power delivery requires 200 kilograms of copper per rack. Moving to 800-volt systems dramatically reduces material costs while enabling the petabyte-scale context memory that agents demand.
• Enterprise AI adoption follows a predictable pattern: organizations with strong ML cultures are seeing material revenue impact. Companies that invested in experimentation and engineering capabilities 3-4 years ago now view AI as a scaling mechanism for profitability, not headcount reduction. The time to reach use case 50 is meaningfully shorter than reaching use case one.
• GPU utilization is the new operational imperative. Idle GPUs are economically unacceptable when paired with high-quality storage increases margins by 12%. The focus has shifted from GPU access to GPU efficiency, with storage becoming the primary enabler of sustained high utilization.
Decision Framework: Evaluate your AI infrastructure investments through three lenses: Can you achieve 10x inference improvements through storage optimization? Do you have the engineering culture to iterate rapidly on AI use cases? Are you designing for the agent era's memory requirements, not today's single-shot prompts?
The strategic implication is clear: the companies that master the memory-storage-compute triangle will capture disproportionate value in the inference economy. This isn't about buying more GPUs — it's about architecting systems that can scale context memory economically while maintaining sub-millisecond response times.
Primary-source citations
"What's new now is the third job. It feels like storage kind of got a promotion this year, right? And that third job is new dedicated nodes specifically for storing context memory or KV cache."
"you're able to get out of the same CapEx and OpEx, the same GPUs and energy costs 6.5 times more, so 550% more tokens"
"if you can offload previously computed attention data from an LLM session, if you put it onto storage, yes, it's slower to initially fetch it, but what it means is that the GPU can be used for more active sessions"
"G1 is actually inside the GPU, so it's the HPM. And also G2 is a system memory. So G3 is a local SSD... But those KV cache, when you create those content, it will keep increase and increase. And one day overflow, you need still have something to keep it. So you have G3.5, we call it content extended memory storage."
"when you start to let that agent loose to call these tools in a loop, run retrieval, take the answer that it got from the first question, ask a second question. At any point in that process, if you're giving it the wrong context, the wrong information, it's going to make a wrong choice"
"current technology delivers power into those server racks at about 48 to 54 kind of volts, and today, to do that for a single rack, you're looking at about 200 kilograms of copper"
"March 2026 is a fundamentally different 180 degree different conversation where there's huge demand... placing acute pressure on this thing called KV cache, which is very, very memory centric."
"Before long, you're into the petabytes or dozens of petabytes of context memory that these models need to be able to keep track of to continue delivering value... even the new Vera Rubin stuff that's coming online this year from NVIDIA, look at their NVL72 system. I think between GPU memory and CPU memory, you have like 70 terabytes, which is a lot, right? But when you're talking about context memory into the petabytes, that's where you hit the wall."
"you're able to get out of the same CapEx and OpEx, the same GPUs and energy costs 6.5 times more, so 550% more tokens. So it's as if in a macro scenario, you just created five and a half new data centers out of thin air to serve agents"
"we see a 10X improvement in inference capability out of a single GPU server"
"GPUs paired with high quality storage, we use VAST very heavily, that increases our margins by 12%. That's a very repeatable playbook... GPUs make more money when they have storage attached to them."
"So you have G3.5, we call it content extended memory storage"
"current technology delivers power into those server racks at about 48 to 54 kind of volts, and today, to do that for a single rack, you're looking at about 200 kilograms of copper... If you scale that up to gigawatt kind of data centers, you are talking about 200,000 and just very large amounts of such materials"
"A trillion dollars in one year for AI infrastructure, it doesn't actually blow my mind... Everything that we know and do today in the digital world and in the physical world, it's all going to get rebuilt."
"you're able to get out of the same CapEx and OpEx, the same GPUs and energy costs 6.5 times more, so 550% more tokens. So it's as if in a macro scenario, you just created five and a half new data centers out of thin air"
"GPUs paired with high quality storage, we use VAST very heavily, that increases our margins by 12%. That's a very repeatable playbook."
"for our customers that we started this years ago, three, four years ago, they now look at this as a material impact to their revenue, to their FTEs, not in the sense that they want to reduce their headcount, but they view it as a scaling mechanism"
"current technology delivers power into those server racks at about 48 to 54 kind of volts, and today, to do that for a single rack, you're looking at about 200 kilograms of copper"