# Data Center İlgili Makaleler

HTX Haber Merkezi, kripto endüstrisindeki piyasa trendleri, proje güncellemeleri, teknoloji gelişmeleri ve düzenleyici politikaları kapsayan "Data Center" hakkında en son makaleleri ve derinlemesine analizleri sunmaktadır.

From 'Old Dogs' to 'New Darlings': How AI is Revaluing Old Infrastructure, from Dell to Nokia

"Old Dogs" Become AI's New Darlings: Revaluing Legacy Infrastructure The AI investment narrative is shifting. Beyond the spotlight on core chipmakers like Nvidia, a new wave of interest is rising for legacy tech companies—Dell, HPE, Nokia, Cisco, Corning, Western Digital—once labeled as slow-growth, outdated stories. This resurgence stems from AI's evolution from model development to real-world deployment, creating massive demand for physical infrastructure. As AI moves into data center construction and enterprise adoption, the focus turns to who can actually build and deliver complex systems. These established players hold decades of experience in supply chains, integration, networking, and enterprise delivery—assets now critical for scaling AI. The revaluation can be grouped into three key infrastructure areas: 1. **Servers & Integration (e.g., Dell, HPE):** They are becoming essential system integrators, transforming GPUs into full-scale AI servers with networking, power, and cooling, then delivering them to clients. Strong recent earnings and AI-specific revenue/order growth for Dell and HPE underscore this shift. 2. **Networking & Connectivity (e.g., Corning, Nokia, Cisco):** As AI clusters grow, high-speed data transfer becomes paramount. Corning benefits from fiber demand for data center links, Nokia is exploring AI-integrated wireless networks (AI-RAN), and Cisco sees surging orders for data center switches—all critical for efficient AI operations. 3. **Storage (e.g., Western Digital, Seagate):** The AI data explosion requires vast capacity. Beyond high-speed memory (HBM), there's growing need for high-capacity HDDs to store training data, logs, video, and cold/archival data cost-effectively. This revaluation, however, is not a blanket endorsement. True reassessment requires concrete proof: AI-driven orders and revenue growth, upward revisions to company guidance, and sustainable improvements in profit quality, not just top-line sales. In essence, AI is not turning all old tech firms into high-growth stocks; it is selectively re-pricing the "old assets" of companies that are mission-critical for building the new AI infrastructure, transforming their legacy capabilities into renewed growth engines.

marsbit4 saat önce

From 'Old Dogs' to 'New Darlings': How AI is Revaluing Old Infrastructure, from Dell to Nokia

marsbit4 saat önce

Standing in the Light: A Comprehensive Guide to the Optical Module and CPO Supply Chain

"Standing in the Light: Understanding the Optical Module and CPO Industry Chain" This article analyzes the critical role of optical communication technology, specifically optical modules and Co-Packaged Optics (CPO), as the "nervous system" for modern AI data centers. With exponential growth in AI computational demands (e.g., NVIDIA's Vera Rubin architecture), traditional electrical interconnects using copper cables face severe bottlenecks in bandwidth, power consumption, and signal integrity over distance. The core function of an optical module is to act as a "translator," converting electrical signals from chips into optical signals for transmission over fiber (and vice-versa). Key internal components include lasers, modulators, photodetectors, drivers, and DSP chips. The industry is currently transitioning from 800G to 1.6T modules. However, the future lies in CPO. This next-generation technology integrates the optical engine directly with the switch ASIC/XPU on the same package substrate, drastically reducing power consumption (by ~3.5x according to NVIDIA), overcoming bandwidth density limits, and minimizing signal attenuation compared to traditional pluggable modules. Key challenges for CPO include advanced packaging capacity (dominated by TSMC), thermal management, repairability, and standardization. The article details the broader technology landscape, including Near-Packaged Optics (NPO, a pragmatic intermediate step), Linear-drive Pluggable Optics (LPO), Optical I/O (OIO for chip-level integration), and Optical Circuit Switches (OCS). A comprehensive CPO industry chain is mapped, highlighting shifting power dynamics: * **Architecture Definers:** NVIDIA, Broadcom, and Marvell now hold greater influence. * **Advanced Packaging & Manufacturing:** TSMC is central; Fabrinet is a key EMS player. * **Lasers ("The Heart"):** A strategic bottleneck. EML lasers are led by Lumentum and Coherent (both receiving major NVIDIA investments). CW lasers, favored for CPO/silicon photonics, see strong Chinese players like Source Photonics and Sicoya. * **Silicon Photonics Chips:** The mainstream path for CPO engines, with key players like Broadcom, Intel, Marvell, and China's Accelink. * **Fiber Connectivity Components:** A major new, high-growth market created by CPO, including Fiber Array Units (FAU), Polarization-Maintaining Fiber (PMF), and MPO connectors. Companies like Tianfu Communication and US Conec are leaders. * **Fiber & Cable:** Experiencing a super-cycle (e.g., Corning, Yangtze Optical Fiber). * **PCB/Substrates:** Requiring advanced materials (e.g., Shengyi Tech). * **DSP & SerDes:** Functions are integrated into switch ASICs in the CPO era (e.g., Broadcom, Astera Labs). * **Optical Module Makers:** Transitioning from standalone module suppliers to providers of optical engines and NPO/LPO solutions while riding the current pluggable boom (e.g., Zhongji Innolight, Eoptolink). The investment timeline is segmented: Short-term (2026-2027) features the "last feast" for pluggable modules and CPO's initial rollout. Medium-term (2027-2029) will see CPO expand and NPO peak. Long-term (2029-2032+) involves CPO/OIO penetration into intra-rack scaling. In conclusion, optical interconnects are fundamental to AI infrastructure. The competitive landscape sees US firms leading in architecture and high-end chips, TSMC in advanced packaging, and Chinese firms holding strong positions in modules, connectivity components, CW lasers, and fiber/cable. The future belongs to companies that can navigate the technological shift from "selling shovels" (modules) to "building highways" (CPO/OIO infrastructure).

marsbit18 saat önce

Standing in the Light: A Comprehensive Guide to the Optical Module and CPO Supply Chain

marsbit18 saat önce

Ten-Thousand-Word Analysis: From $10 to $290, MRVL Wins the Entire AI Era by 'Not Making GPUs'

Marvell Technology's stock price surged from under $10 in 2016 to a record $290 in June 2026, fueled not by making GPUs, but by dominating AI infrastructure connectivity. This analysis argues the market misvalues MRVL as merely a smaller Broadcom in custom AI chips, overlooking its true, unique position. Marvell's core strength lies in enabling high-speed data flow for AI clusters through three interconnected businesses. First, it holds a commanding ~70% market share in high-speed optical DSPs (essential for data center light modules), a deep-moat business with accelerating growth. Second, its custom AI chip design business serves hyperscalers like AWS, Microsoft, and Google, with a significant revenue pipeline despite lower margins. Third, stable cash flows come from Ethernet switch chips and enterprise storage controllers. Together, they form a full-stack "AI data movement" platform. CEO Matt Murphy's transformative leadership since 2016, involving strategic divestments, key acquisitions (like Inphi for optical DSPs), and securing long-term agreements with major cloud providers, repositioned the company. A pivotal $2 billion strategic investment from NVIDIA in 2026 underscored Marvell's critical role in the AI ecosystem, particularly through collaborations like NVLink Fusion. While Marvell faces risks—including client concentration (losing the Amazon Trainium3 design), lower-margin business mix, competitive threats, insider selling, and complex supply chains—its fundamentals remain strong. The optical interconnect moat is widening with the acquisition of Celestial AI (photonics fabric), and financial metrics show accelerating revenue growth and operating leverage. With a PEG ratio suggesting undervaluation relative to its growth, the thesis is that the market undervalues Marvell's monopolistic position in AI "plumbing" while overemphasizing its competitive custom chip segment. The story transcends investing, symbolizing how in any complex system—from the internet to AI—the value of "connection" ultimately surpasses that of individual "nodes."

marsbit22 saat önce

Ten-Thousand-Word Analysis: From $10 to $290, MRVL Wins the Entire AI Era by 'Not Making GPUs'

marsbit22 saat önce

ByteDance Adopts Arm CPUs, Jensen Huang: So Sad I Didn't Buy Arm

**Summary:** At Computex 2026, Arm CEO Rene Haas announced that ByteDance and Oracle have adopted Arm's self-designed Arm AGI data center CPU. The company expects significant revenue growth from this product, projecting $20 billion in demand for the 2027/2028 fiscal years. Haas noted that restricting AI-capable CPUs from the US to China is nearly impossible due to their widespread applications. Arm's stock has surged dramatically this year, notably rising 16% after NVIDIA's Arm-based Vera CPU and RTX Spark announcements. A highlight was the informal, humorous on-stage conversation between Haas and NVIDIA CEO Jensen Huang. Huang joked about NVIDIA's failed attempt to acquire Arm and playfully lamented selling his Arm shares. Both executives showed a clear sense of camaraderie and shared regret over the missed merger. Key technical topics were discussed: 1. **AI PC Design:** Huang explained NVIDIA's RTX Spark superchip (with a 20-core Arm CPU) is designed for future AI agents that will autonomously run and use tools on PCs, blending local and cloud processing. 2. **Agent vs. OS:** Huang emphasized the operating system remains crucial, as AI agents rely on its APIs and tools to function. 3. **Growth Constraints:** He identified the shift to "useful AI" that generates profitable tokens as a primary driver for immense, almost limitless, computational demand. Haas outlined Arm's strategy across PC and data centers. For PCs, Arm collaborates with partners like NVIDIA and MediaTek, offering its compute subsystem (CSS) for custom SoCs. In data centers, its Arm AGI CPU (built on TSMC's 3nm process) has gained major partners including OpenAI, Meta, and now ByteDance and Oracle. Arm presented a multi-year roadmap for its in-house CPU line. The article concludes that while GPUs dominated the AI training race, the explosion of AI agents is shifting significant focus to CPUs for inference, state management, and tool orchestration. The industry is trending towards vertical integration, with companies like cloud providers designing chips and chip/IP firms offering full solutions, all competing to deliver more efficient computing per watt.

marsbitDün 04:51

ByteDance Adopts Arm CPUs, Jensen Huang: So Sad I Didn't Buy Arm

marsbitDün 04:51

After Marvell's 32% Surge, the Chinese Chip Family Behind It Emerges

The stock price of Marvell Technology surged 32.5% on June 2nd, driven by NVIDIA CEO Jensen Huang highlighting its custom ASICs and optical interconnects as core to AI data center architecture. This event brought attention to the Chinese semiconductor family behind Marvell: the Dai siblings. The story centers on three siblings, all UC Berkeley graduates, whose three-decade entrepreneurial journey aligns with major semiconductor industry shifts. In 1995, youngest sister Dai Wei Li co-founded Marvell with her husband Sehat Sutardja and his brother, focusing on storage controllers. Eldest brother Dai Wei Min founded EDA company Ultima, later sold to Cadence, and later founded VeriSilicon (芯原) in China, becoming a leading semiconductor IP provider. Second brother Dai Wei Jin co-founded EDA firm Silicon Perspective (sold to Cadence) and GPU IP company Vivante, later acquired by VeriSilicon. The combined "Dai-Sutardja" family network extends beyond Marvell. Their ventures and investments form a comprehensive ecosystem for the post-Moore's Law, chiplet era. Key holdings include: Dream Big Semiconductor (AI SuperNICs, acquired by Arm), Alphawave (high-speed SerDes IP, acquired by Qualcomm), and Silicon Box (a chiplet advanced packaging foundry). VeriSilicon itself thrives on the AI ASIC and IP boom in China. Collectively, the family's AI infrastructure-related portfolio is estimated at over $22 billion. Their strategy represents a distinct path: building critical components for open standards and key manufacturing capacity in the chiplet era, rather than pursuing standalone AI chip dominance. While this path may not create the next NVIDIA, it has enabled repeated successful exits and sustained influence within the global semiconductor industry.

marsbitDün 11:16

After Marvell's 32% Surge, the Chinese Chip Family Behind It Emerges

marsbitDün 11:16

CPU, Quietly Returning to the Center of the AI Computing Power Stage

Over the past three years, AI computing power narratives have been dominated by GPUs. However, starting in 2026, this story began to shift. While training large models remains GPU-intensive, the rapid growth of inference and AI agent workloads, which require high levels of task orchestration, concurrency, and data flow management, has highlighted a renewed critical role for CPUs. These are tasks GPUs are not designed to handle. Intel's recent launch of the Xeon 6+ processor, built on its Intel 18A process and featuring up to 288 efficiency cores (E-cores), exemplifies this strategic pivot. It is positioned not as a mere companion to GPUs but as the essential "control plane" for AI infrastructure, optimized for high-density, energy-efficient, and high-throughput workloads characteristic of AI agents and inference. This "CPU resurgence" is not about CPUs outperforming GPUs in raw computation. It reflects a systemic bottleneck: as AI scales from training single models to deploying countless intelligent agents, the demand for coordination and data handling surges. Major cloud providers are also developing their own high-density ARM-based server CPUs for similar workloads. However, Intel's success with this strategy faces significant challenges. Competition includes NVIDIA's integrated CPU-GPU solutions, the expanding adoption of cloud vendors' in-house ARM CPUs, and the crucial market test of Intel's 18A manufacturing process against rivals like TSMC's N2. In conclusion, CPUs are indeed reclaiming a central, though redefined, role in AI compute—managing the complex orchestration that enables massive-scale AI deployment. While the trend is clear, which company will ultimately lead this CPU resurgence remains an open question to be decided in the data centers of 2027 and beyond.

marsbitDün 10:42

CPU, Quietly Returning to the Center of the AI Computing Power Stage

marsbitDün 10:42

AMD Stock Price Forecast 2026 to 2030: Wall Street Signals a Major $657 Target!

AMD's stock has seen a dramatic rally, rising over 130% year-to-date to around $510 as of mid-2026. This surge is driven by a fundamental shift in the company's business, with its Data Center segment now the core contributor, posting a record $5.8 billion in Q1 2026 revenue (up 57% YoY) and exceeding 50% of total revenue for the first time. CEO Dr. Lisa Su raised the long-term server CPU Total Addressable Market (TAM) forecast to over $120 billion annually by 2030. Analyst sentiment is bullish. The consensus rating is a "Strong Buy" with a 12-month average price target of $472.17, though several firms have recently set higher targets, with Barclays issuing a street-high target of $665. Long-term models project a 2030 price range of approximately $493 to $822, with a baseline scenario near $657, anchored to AMD's expanded TAM guidance. Key growth catalysts include strong demand for EPYC server CPUs and Instinct AI GPUs, partnerships with OpenAI and Meta, and the upcoming MI450 series and Helios rack-scale platform. However, significant risks remain, including a high forward P/E ratio exceeding 169x and increased competition, notably from NVIDIA's planned entry into the server CPU market. Investors must weigh the strong execution and growth trajectory against these substantial valuation and competitive pressures.

marsbit2 gün önce 13:12

AMD Stock Price Forecast 2026 to 2030: Wall Street Signals a Major $657 Target!

marsbit2 gün önce 13:12

Huang Renxun and Marvell CEO Discuss on Stage: The Future of AI Competition is Not Computing Power but Connectivity, 'Use Copper Where You Can, Use Optics Only Where You Must'

Summary: At Computex 2024, NVIDIA CEO Jensen Huang joined Marvell CEO Matt Murphy on stage, highlighting the strategic partnership between their companies. The core theme was that the next decisive battleground for AI infrastructure is not compute or memory, but connectivity. As AI models evolve into vast agent-based systems, the ability to connect millions of processors efficiently is becoming the critical bottleneck. Huang announced NVIDIA's strategic $20 billion investment in Marvell, reflecting the deep integration between their technologies for AI data centers. A key discussion point was the transition from copper to optical interconnects within racks. The guiding principle, articulated by Huang, is: "You use optics wherever you must, you use copper wherever you can." While copper remains cost-effective for short distances, its physical limits are being reached as bandwidth demands double. When moving to 400Gbps, copper can no longer fully connect an entire rack. This shift necessitates innovations like Co-Packaged Optics (CPO), which integrates optical engines directly into the chip package to solve density and power challenges. Marvell demonstrated its 51.2T CPO-based switch, eliminating copper traces on the PCB. The future vision is a "distance-free data center," where optical connectivity removes physical constraints. This allows for fully disaggregated, dynamic architectures where compute, memory, and storage pools can be combined on-demand based on workload requirements, rather than being limited by connection boundaries. Marvell, positioned as a neutral "Switzerland" in the ecosystem with a comprehensive portfolio across all connectivity distances, is central to enabling this next era of AI infrastructure.

marsbit2 gün önce 09:41

Huang Renxun and Marvell CEO Discuss on Stage: The Future of AI Competition is Not Computing Power but Connectivity, 'Use Copper Where You Can, Use Optics Only Where You Must'

marsbit2 gün önce 09:41

What's New in Jensen Huang's 'Agent Factory'?

In a keynote at COMPUTEX 2026, NVIDIA CEO Jensen Huang shifted the company's focus from hardware "full-stack" solutions to the era of AI Agents. The centerpiece is the Vera Rubin platform, now in production, which is designed specifically for Agent workloads and offers 10x the efficiency of its predecessor. The platform features the new Vera CPU, built for AI, and incorporates Spectrum-X Ethernet Photonics with CPO technology for improved networking and energy efficiency. NVIDIA introduced DSX, an integrated toolkit for designing, simulating, and operating AI data centers, aiming to streamline "AI factory" deployment and management. For end-user deployment, the company unveiled DGX Station for Windows, a desktop AI supercomputer for running Agents locally, and the RTX Spark SoC for AI PCs. On the software front, NVIDIA launched the 550B-parameter Nemotron 3 Ultra model for enterprise Agents and the Cosmos 3 foundation model for physical AI, unifying visual reasoning and action prediction. In robotics, a partnership with Unitree yielded the H2 Plus, a reference humanoid robot built on the Isaac GR00T platform to lower development barriers. Security was emphasized with enhanced confidential computing for Vera Rubin and new data path security features for the BlueField-4 STX storage platform. The presentation highlighted a strategic pivot: NVIDIA is reorganizing its entire technology stack—from chips and data centers to models, software, and robots—around the emerging ecosystem of autonomous, practical AI Agents.

marsbit06/01 12:04

What's New in Jensen Huang's 'Agent Factory'?

marsbit06/01 12:04

NVIDIA Launches DSX Platform, Expanding into AI Factory Infrastructure

NVIDIA has unveiled the DSX platform at its GTC Taipei event, marking a strategic expansion from GPU sales into comprehensive AI factory infrastructure solutions. The platform addresses challenges like power supply, cooling, and resource orchestration as AI models scale, shifting the industry focus from single-chip performance to overall infrastructure efficiency. DSX integrates NVIDIA's chips, systems, software, and partner technologies to cover the entire AI factory lifecycle—from design and simulation to deployment and operations. It aims to accelerate deployment, improve reliability and operational efficiency, and reduce the cost per generated token in AI inference. The software suite includes DSX MaxLPS, which uses 45°C liquid cooling and rack-level optimization to allow up to 40% more GPUs per megawatt, and DSX OS, an open-source platform for AI factory operations. The platform also encompasses reference designs, digital twin simulation (DSX Sim), dynamic workload adjustment based on grid conditions (DSX Flex), and data exchange between systems. Early adopters include cloud providers like CoreWeave and Lambda. Major hardware partners, including Dell, HPE, Lenovo, and Supermicro, are developing DSX-ready systems. Pilot projects for DSX Flex are underway with energy providers. Strategically, DSX represents NVIDIA's ongoing transition from an AI chip supplier to a full-stack AI infrastructure platform provider, aiming to set industry standards and solidify its market leadership.

marsbit06/01 04:27

NVIDIA Launches DSX Platform, Expanding into AI Factory Infrastructure

marsbit06/01 04:27

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