OpenAI and Dell Push Codex Into Enterprise Stacks

TurboQuant may be one of the most important AI infrastructure breakthroughs most people have not heard of yet. Google Research just introduced TurboQuant, and the headline is simple: Up to 6x less RAM needed for long-context AI workloads. That is not a small optimization. That is a platform-level unlock. Because in enterprise AI, the bottleneck is not always the model. It is memory. It is GPU availability. It is inference cost. It is the brutal reality that every longer prompt, every larger context window, every smarter agent, and every production AI workflow needs more infrastructure behind the curtain. TurboQuant goes after one of the big culprits: the key-value cache. That may sound deeply technical, because it is. But the business impact is easy to understand. If you need 6x less memory, you can potentially: ✅ Run larger AI workloads on the same hardware ✅ Serve more users without immediately buying more GPUs ✅ Make long-context AI more affordable ✅ Reduce infrastructure pressure in a constrained GPU market ✅ Push more AI projects from lab experiments into production ✅ Improve the economics of agents, copilots, retrieval, and enterprise search This matters right now because the AI hardware crunch is real. Every enterprise wants AI. Every team wants bigger context windows. Every developer wants faster responses. Every CFO wants to know why the GPU budget looks like a moon mission with purchase orders. TurboQuant will not magically solve every infrastructure constraint. But a 6x memory efficiency gain is the kind of breakthrough that changes planning conversations. Instead of asking, “How much more hardware do we need?” Teams can start asking, “How much smarter can our architecture become?” When will this show up in everyday AI projects? Probably quietly. First in hyperscale AI platforms. Then inside inference engines, vector systems, model-serving frameworks, and long-context optimization stacks. Then eventually in the tools developers and enterprises already use every day. Most users may never see the name TurboQuant in a product UI. But they may feel the impact through faster AI, cheaper AI, longer context windows, and more production-ready enterprise systems. The future of AI is not just bigger models. It is smarter infrastructure. And TurboQuant is a very loud signal that the next wave of AI progress may come from making the stack dramatically more efficient. Tiny bits. Huge leverage. #TurboQuant #AI #GenerativeAI #EnterpriseAI #GPU #Infrastructure #LLM #DataCenter #CloudComputing #AIOps https://lnkd.in/gfhBr7Rk

Everyone is talking about AI. Far fewer people talk about what makes it work: High Performance Computing (HPC). HPC isn't just about "big servers" or fancy code; it's the engineering discipline behind systems that process massive data volumes with low latency, high concurrency, and predictable performance. At Dataiva Solutions, HPC is an integral part of our ecosystem. Low-level languages such as C++ and Rust allow us to be closer to the metal and fully utilise modern multi-core hardware (whether it’s CPUs or GPUs). This control allows for the difference between systems that respond in 50 milliseconds and 500 microseconds. These are crucial for real-world applications. Whether our product is scoring events in real time, executing queries across billions of rows, solving combinatorial layout problems with tight latency budgets, or streaming geospatial data, the importance of HPC is clear. Every one of these workloads is bound by the same fundamental constraints: memory bandwidth, network latency, lock contention, cache coherence, and data partitioning. A model is only as fast as the data it can be fed, and only as useful as the infrastructure that delivers the results. When pipelines bottleneck on serialisation, queries fan out poorly across nodes, or event streams can't keep up with peak load, your "AI strategy" stalls regardless of the model chosen. Remember, the compute graph is only the last few per cent of the work, built on the performance of everything beneath. This is exactly why we keep investing in C++ and Rust-based services, distributed compute architectures, graph processing, streaming analytics, and low-latency storage engines. It's not just because they're impressive technologies; it's because they're the substrate on which everything else, including AI, depends. The pressure to solve these challenges is greatest in industries where milliseconds matter: fraud detection, cybersecurity, quantitative finance, defence, genomics, logistics, mining, and autonomous systems. Ironically, instead of diminishing the need for HPC, the rise of AI is further elevating its importance. The future won’t favour those who have the best AI models or workflows. It will favour organisations that can process, move, secure, and analyse data faster than anyone else. The true competitive edge is defined by relentless speed and unwavering reliability.

Dashboards defined the last era of enterprise data. Agentic AI defines the next. Today marks a new chapter. Dell Technologies just announced new enhancements to the Dell AI Data Platform with NVIDIA—a purpose-built foundation for the agentic AI era, where data fuels continuous reasoning and real‑time intelligence. This isn’t a refresh. It’s a shift. The future of AI isn’t deploying models. It’s building systems that never stop learning from data. 👉 Read the blog to see what’s new—and why it matters now: https://del.ly/6047BBrpRL #iwork4dell

Cached-read pricing is the current frontier of tokenomics competition. 💸 WEKA's Val Bercovici dives into the factors shaping token market rates in this great read from VentureBeat's Matt Marshall 👇

It looks increasingly like Western frontier labs are building a multi-billion dollar infrastructure trap for themselves. Funding massive cloud data centers by selling proprietary tokens is a broken business model. DeepSeek AI’s open-weights V4 Pro and V4 Flash match frontier intelligence while undercutting output costs by 17x to 25x. The premium markup on closed Western APIs (like OpenAI and Anthropic) stops being a security premium – it becomes a corporate liability. The catalyst? U.S. chip sanctions accidentally forced the most disruptive architectural inversion Silicon Valley has seen. Blocked from buying top-tier NVIDIA GPUs, DeepSeek bypassed the hardware monopoly via software. By crushing a 1-million-token context memory footprint down to a microscopic 5.48 GB of HBM (compared to 80-100+ GB on standard Western models), they broke the hardware bottleneck. This allows frontier-grade models to thrive on sanctioned Chinese processors (like Huawei’s Ascend series) by offloading heavy data lookups to cheap commodity storage like NAND flash and enterprise SSDs. The enterprise rebellion against expensive tokens is moving fast because baseline unit economics have inverted: • The Budget Crisis: Tech teams running high-volume autonomous agents are hitting a wall. @Uber torched its entire annual AI budget for coding tools in just four months because recursive background loops are mathematically unsustainable at proprietary Western rates • The Buyer Panic: VentureBeat’s Q1 2026 tracker data confirms cost tracking has become a boardroom emergency. "Cost per token or licensing model" grew from 25.4% in January to 36.7% in March, trailing only raw performance, as the primary selection criterion for technology buyers. • The Open-Weights Blueprint: High-volume tech teams are actively abandoning closed ecosystems. In our latest podcast, Pinterest CTO Matt Madrigal confirms the company went all-in on open weights, post-training Alibaba's Qwen weights to drive their assistant core at a staggering 90% reduction in scale costs. The plumbing of corporate AI is being rewritten in real-time. Popular open-source inference frameworks like vLLM, SGLang, and Nvidia TensorRT-LLM are being stretched to their absolute limits to natively support this memory-offloading database layout. As Val Bercovici (Chief AI Officer at WEKA) notes, DeepSeek's 89x price undercut on cache-reads has officially set the industry on notice. When automated background workflows can be handled by highly intelligent open weights like DeepSeek V4-Pro and V4-Flash for zero marginal token cost, defending a premium price point for raw cloud computing ceases to be a defensible strategy. Read our full investigative breakdown on VentureBeat: https://lnkd.in/ge_pJ_fW Hat-tip to Menlo Ventures and Andreessen Horowitz for their work in this area. #HardwareEngineering #Semiconductors

David Vellante and David Floyer just dropped one of the most architecturally significant analyses I’ve read this year — and it deserves the attention of every Enterprise Architect navigating the AI transition. Their core thesis: Nvidia isn’t just selling GPUs. It’s quietly assembling a replacement enterprise platform — where the rack becomes the computer, tokens become the unit of economic value, and frontier models become the migration engine that pulls legacy x86 estates into an accelerated, AI-native fabric. A few points that should resonate with anyone doing serious EA work today: 🔹 The “Deterministic Myth” — Enterprises don’t actually run clean, deterministic systems. They run fragmented application jungles held together by human semantic reasoning. The AI factory’s real promise is automating that coordination layer. 🔹 x86 Absorption, not replacement — There is no plausible rip-and-replace path. The migration is stage-by-stage, domain-by-domain, with deterministic workloads preserved while the AI fabric grows around them. This is exactly the architectural pragmatism EAs need to internalize. 🔹 The new cloud is federated and sovereign — AI factories won’t live only in hyperscale regions. Sovereignty, latency, locality, and regulated-industry requirements demand a federated AI control plane spanning public cloud, on-prem, edge, and sovereign environments. This is where Private AI strategy becomes existential — not optional. 🔹 Data becomes the real-time truth substrate — The five-layer cake is missing a layer. Without a real-time semantic data foundation (a System of Intelligence above systems of record), agentic outcomes simply won’t be trustworthy at enterprise scale. 🔹 Recovery becomes semantic, not transactional — When agents are operating across workflows, “restart the system” doesn’t cut it. The platform has to restore reasoning state, policy context, and human approval chains. For CXOs and Enterprise Architects, the action item is clear: identify where your organization is still held together by human coordination — reconciling data, interpreting exceptions, approving workflows — and begin shifting that work into an AI-mediated semantic layer. Fund AI factories with discipline. Build the federated control plane. Demand a clear line of sight from AI CapEx to productivity, revenue, and resilience outcomes. This is no longer an IT modernization conversation. It's an operating model transition. Subscribe to The Enterprise Architect Newsletter → https://lnkd.in/gFUgy8ee #EnterpriseArchitecture #ArtificialIntelligence #DigitalTransformation #Nvidia #PrivateAI

314 | Breaking Analysis | Nvidia, AI factories and the transition to accelerated computing Co-authored with David Floyer The biggest enterprise AI story is not about the current boom in semis. It is that AI factories and the intelligence they produce will begin to replace the human reconciliation layer that keeps companies running today. Most large enterprises do not operate from a clean, unified system of truth. They operate through a maze of ERP, CRM, finance, supply chain, HR, security, analytics and industry-specific applications - each with its own data model, workflows, exceptions and version of reality. Determinism today is a myth. The reality is deterministic systems require human adjudication. *People reconcile conflicting data and interpret exceptions. *People chase approvals. *People translate between systems. *People resolve broken workflows. *People know which report is “right.” *People understand what the business process really means versus what the software says it means. This is the hidden operating model of the enterprise. It is expensive and slow. The promise of AI factories - and the important applications that must be built on top of them - is that they do not just generate tokens. They produce intelligence that can inspect systems, infer meaning, map workflows, diagnose conflicts, build integrations, operate agents and continuously improve how the business runs. In this scenario, frontier models become the semantic operating layer of the enterprise. They analyze codebases, database schemas, APIs, logs, tickets, documents and human procedures to understand how the company actually works. They collaborate with experts to define shared semantics. They identify where systems conflict. They orchestrate agents under policy. And over time, they begin to automate the reconciliation work that today depends on tribal knowledge and human intervention. That is the real operating model shift. We see the existing x86 infrastructure being absorbed into the AI factory story. Legacy systems will not disappear overnight. They will be surrounded, interpreted and gradually pulled into AI factory architectures built on GPUs, CPUs, DPUs, high-speed fabrics, context storage, semantic databases and policy-aware control planes. That is the technical “how.” The larger “why” is that enterprises want to collapse the distance between fragmented applications and real-time truth. The next decade of enterprise AI will be defined by platforms that can replace human semantic glue with machine-scale intelligence - not by eliminating people, but by moving them out of endless reconciliation and into higher-value judgment, design and governance. That is why AI factories are more profound than people realize. They are the foundation for a new enterprise operating model. In this Breaking Analysis we break this down using NVIDIA's roadmap as a guide to the future. Full slide deck here: https://bit.ly/4uCJwzB Full research in the comments.

Data management is about to change forever. AI inference is rooted in delivering real-time intelligence at scale. The challenge is not simply training models, it is serving data to models fast enough, consistently enough and economically enough to support production workloads. Getting inference right is what will help companies soar above and beyond all others who fail to follow the blueprint. Simply put, Silk is recognized as the performance and economic control plane for true enterprise AI infrastructure. When inference scales: - GPUs sit idle waiting for data - latency spikes under concurrency - throughput collapses when workloads collide It’s not a compute problem. It’s a data delivery problem. And it gets worse when: → analytics, AI, and OLTP all hit the same datasets → infrastructure has to serve mixed, unpredictable IO patterns This is the part most architectures weren’t built for but there is now a viable solution which will change the way the modern-day enterprise delivers unprecedented performance at scale.

🚀 𝐓𝐡𝐞 𝐒𝐡𝐢𝐟𝐭 𝐟𝐫𝐨𝐦 𝐀𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭𝐬 𝐭𝐨 𝐀𝐠𝐞𝐧𝐭𝐬: 𝐆𝐨𝐨𝐠𝐥𝐞 & 𝐈𝐁𝐌 𝐢𝐧 𝟐𝟎𝟐𝟔 If 2024 was the year of "Chat," 2026 is the year of "𝐃𝐨." We are officially moving past AI that Just answers questions to 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 that orchestrates entire business outcomes. Google and IBM just signalled where the next trillion dollars of value will be created. Here’s the breakdown: 𝟏. 𝐓𝐡𝐞 𝐄𝐫𝐚 𝐨𝐟 𝐭𝐡𝐞 "𝐀𝐠𝐞𝐧𝐭𝐢𝐜 ��𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞" (𝐆𝐨𝐨𝐠𝐥𝐞’𝐬 𝐅𝐨𝐜𝐮𝐬) Google Cloud Next ‘26 has made it clear: agents are now active members of the team. 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬: New agents are sustaining reasoning across multi-step tasks—from threat hunting in SecOps to natural language interfaces for 40-year-old legacy SAP systems. 𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐀𝐈: AI is stepping out of the browser. Using environmental sensors and live video, it’s now monitoring factory floor safety and shelf inventory in real-time. 𝐓𝐡𝐞 𝐀𝐈 𝐇𝐲𝐩𝐞𝐫𝐜𝐨𝐦𝐩𝐮𝐭𝐞𝐫: With 8th Gen TPUs (TPU 8t/8i), the infrastructure for near-zero latency inference is finally here to support these massive agentic fleets. 𝟐. 𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞 𝐢𝐬 𝐍𝐨 𝐋𝐨𝐧𝐠𝐞𝐫 "𝐅𝐮𝐭𝐮𝐫𝐞" (𝐈𝐁𝐌’𝐬 𝐅𝐨𝐜𝐮𝐬) At IBM Think 2026, CEO Arvind Krishna dropped a bombshell: 𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞 𝐢𝐬 𝐡𝐞𝐫𝐞 𝐭𝐡𝐢𝐬 𝐲𝐞𝐚𝐫. 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐟𝐢𝐜 𝐁𝐫𝐞𝐚𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡𝐬: Using the 156-qubit IBM Quantum Heron processor, researchers have already simulated massive 12,000+ atom protein complexes—a feat previously impossible for classical supercomputers. 𝐒𝐨𝐯𝐞𝐫𝐞𝐢𝐠𝐧 𝐀𝐈: IBM’s new 𝐒𝐨𝐯𝐞𝐫𝐞𝐢𝐠𝐧 𝐂𝐨𝐫𝐞 is the answer for highly regulated industries. It allows enterprises to run advanced AI in fully air-gapped or localized environments without sacrificing control. 𝟑. 𝐓𝐡𝐞 𝐆𝐫𝐞𝐚𝐭 𝐂𝐨𝐧𝐯𝐞𝐫𝐠𝐞𝐧𝐜𝐞 The most exciting project isn't one company—it’s the 𝐆𝐨𝐨𝐠𝐥𝐞 + 𝐈𝐁𝐌 𝐏𝐚𝐫𝐭𝐧𝐞𝐫𝐬𝐡𝐢𝐩. They are currently working to integrate Google’s 𝐆𝐞𝐦𝐢𝐧𝐢 𝐦𝐨𝐝𝐞𝐥𝐬 directly with IBM’s software portfolio (like watsonx.data). The goal? To combine Google's developer-centric design with IBM’s hybrid cloud muscle to remove the friction of AI adoption. 𝐓𝐡𝐞 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲: We are moving toward a "Quantum-AI Continuum." Quantum will uncover what AI cannot yet compute, and AI will learn from those quantum insights. 𝐀𝐫𝐞 𝐲𝐨𝐮 𝐫𝐞𝐚𝐝𝐲 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐄𝐫𝐚? 𝐎𝐫 𝐢𝐬 𝐲𝐨𝐮𝐫 𝐭𝐞𝐚𝐦 𝐬𝐭𝐢𝐥𝐥 𝐬𝐭𝐮𝐜𝐤 𝐢𝐧 "𝐂𝐡𝐚𝐭" 𝐦𝐨𝐝𝐞? #GoogleCloud #IBMThink #AgenticAI #QuantumComputing #DigitalTransformation #AI2026 #FutureOfTech