# Сопутствующие статьи по теме Nvidia

Новостной центр HTX предлагает последние статьи и углубленный анализ по "Nvidia", охватывающие рыночные тренды, новости проектов, развитие технологий и политику регулирования в криптоиндустрии.

AI Defeated Intel, and AI Saved Intel

Intel, once the dominant force in semiconductors with its CPUs powering the PC era, faced a severe crisis by 2024. Its stock plummeted 60% that year, culminating in its removal from the Dow Jones Industrial Average, replaced by NVIDIA—a shift symbolizing the transition from CPU to GPU dominance in the AI training era. Intel’s decline was driven by missed opportunities in mobile and AI, exacerbated by strategic missteps under multiple CEOs. In 2025, new CEO Lip-Bu Tan took over, implementing a turnaround strategy: drastic cost-cutting, refocusing on core areas like Xeon server CPUs and foundry services, and forging key partnerships. NVIDIA invested $5 billion in Intel, Google expanded orders for Xeon processors, and Elon Musk integrated Intel into his Terafab project. These moves, coupled with the AI industry’s shift from training to inference, revitalized demand for CPUs, which are critical for task orchestration in agent-based workloads. By Q1 2026, Intel’s revenue grew 7% year-over-year, with non-GAAP net income surging 156%. However, challenges remain, including ongoing losses and the need to scale advanced manufacturing processes. Intel’s recovery, while promising, hinges on sustaining this momentum in a rapidly evolving market.

marsbit04/25 01:32

marsbit04/25 01:32

Not Just DeepSeek, Big Tech Companies Want to 'Abandon' NVIDIA

The article discusses how major tech companies are attempting to reduce their reliance on Nvidia, despite its dominant position in the AI chip market, where it enjoys a 75.2% GAAP gross margin. Companies like DeepSeek are adapting their models to run on domestic alternatives such as Huawei’s Ascend chips, while in the U.S., Google and Meta are developing their own AI chips (TPU and MTIA series) to complement external partnerships. Nvidia’s CEO, Jensen Huang, acknowledges that Moore’s Law is fading and that export restrictions may slow China’s AI development but could ultimately spur a self-sufficient ecosystem. Notably, Chinese firms are leading in open-source models, which could eventually challenge Nvidia’s monopoly. OpenAI is actively diversifying away from Nvidia, signing a $20 billion deal with Cerebras, a startup using wafer-scale chips to reduce latency and cost. Cerebras, founded by Andrew Feldman, aims to challenge Nvidia with its unique architecture but faces financial and competitive risks, including heavy dependence on OpenAI and geopolitical tensions. While competition is intensifying with players like AMD and Groq (which partnered with Nvidia), the overall demand for compute continues to grow. The market is shifting toward a diversified supplier model, though Nvidia remains a formidable force.

marsbit04/24 12:29

Not Just DeepSeek, Big Tech Companies Want to 'Abandon' NVIDIA

marsbit04/24 12:29

Illustrating the Capital Market After DeepSeek V4's Launch: Zhipu and MiniMax Plunge, NVIDIA Panics

DeepSeek V4, a 1T parameter MoE model with a 285B Flash version, has been fully open-sourced under Apache 2.0, triggering significant reactions across capital markets. Chinese AI chipmakers like Cambricon and Hygon saw major stock gains, with Cambricon rising 60% monthly. In contrast, Hong Kong-listed AI firms Zhipu and MiniMax dropped over 7%, facing heavy short-selling. NVIDIA’s shares dipped, with analysts noting a "decoupling" of Chinese and North American AI inference demand. The launch intensified competition in the AI model space, following 11 major releases in 30 days, including GPT-5.5 and Llama 4. Unlike others, V4’s permissive licensing and full open-source release challenged closed-source models on performance, cost, and accessibility. Critically, V4 announced Day-0 support for domestic chips like Huawei’s Ascend 950PR and Cambricon’s Siyuan 590, offering better cost-performance than NVIDIA counterparts. This shift reduces reliance on CUDA, aligning with NVIDIA CEO’s earlier concerns about Chinese AI chips threatening its dominance. The move signals a tangible step in China’s AI supply chain independence, redirecting compute demand to local manufacturers like Hua Hong Semiconductor.

marsbit04/24 11:37

Illustrating the Capital Market After DeepSeek V4's Launch: Zhipu and MiniMax Plunge, NVIDIA Panics

marsbit04/24 11:37

Intel Soars 20%, CPUs Return to Center Stage in the Agent Era

Intel's stock surged 20% after reporting exceptional Q1 2026 results, with revenue of $13.6 billion (up 7% YoY) and non-GAAP EPS of $0.29, beating expectations by 29x. The rebound is driven by the resurgence of CPUs in the AI era, particularly as workloads shift from training to inference and agent-based applications. Intel’s Data Center and AI (DCAI) division hit a record $5.1 billion in revenue, up 22% YoY, marking a U-shaped recovery since mid-2025. The growth is attributed to strong demand for Xeon 6 "Granite Rapids" processors and increased AI infrastructure refresh cycles. While NVIDIA and AMD dominated the AI training phase, Intel is benefiting from the focus on AI agents, where CPU performance becomes critical—accounting for 50-90% of workflow latency in agent orchestration. This shift, coupled with management changes and strategic refocus on CPUs (including canceling the Falcon Shores GPU project), has repositioned Intel. New CEO Lip-Bu Tan emphasized that CPUs are re-establishing themselves as essential infrastructure in the AI era.

marsbit04/24 04:39

Intel Soars 20%, CPUs Return to Center Stage in the Agent Era

marsbit04/24 04:39

Jensen Huang Publicly Challenges Google and Amazon, Is the Chip Business Entirely Sustained by Anthropic?

In a candid interview, Nvidia CEO Jensen Huang challenged competitors like Google and Amazon, admitted past strategic errors, and criticized U.S. export controls on AI chips. He framed Nvidia’s role as turning “electricity in, tokens out,” emphasizing the complexity and value of AI inference. Huang dismissed rival custom chips like Google’s TPU and Amazon’s Trainium as inflexible and niche, claiming their growth relies heavily on clients like Anthropic. He also acknowledged missing early investment opportunities in OpenAI and Anthropic. On China, Huang warned that export restrictions risk pushing the country toward self-sufficiency and could cost U.S. leadership in AI. Finally, he explained Nvidia’s acquisition of Groq as a move to serve premium, low-latency token markets. Throughout, Huang emphasized ecosystem trust and Nvidia’s central role in global AI infrastructure.

marsbit04/21 07:00

Jensen Huang Publicly Challenges Google and Amazon, Is the Chip Business Entirely Sustained by Anthropic?

marsbit04/21 07:00

The DeepSeek You've Been Waiting For Has Long Changed

The article discusses the delayed release of DeepSeek V4, a highly anticipated AI model in China, and explores the reasons behind its slowed development. Initially a leader in the global AI race, DeepSeek has fallen behind competitors like OpenAI, Anthropic, and Google, which release major updates every few months. A key factor is DeepSeek's shift in focus due to national strategic priorities. In early 2025, the Chinese government encouraged the company to use Huawei’s Ascend processors instead of NVIDIA’s GPUs, aligning with broader efforts to achieve technological self-reliance. DeepSeek attempted to train its models on Huawei’s Ascend 910C chips but faced technical challenges, including instability and communication issues during distributed training. As a result, the company continued using NVIDIA hardware for training while only using Ascend chips for inference. In 2026, DeepSeek prioritized adapting V4 to Huawei’s new Ascend 950PR and Cambricon chips, aiming for a full migration from NVIDIA’s CUDA to Huawei’s CANN framework. This adaptation process, particularly ensuring precision alignment across hardware, consumed significant time and resources, slowing down model iteration. The delay also reflects DeepSeek’s evolving role from a purely market-driven entity to a "national mission-oriented" company. This shift has come at a cost: the model now lags behind competitors in areas like code generation and multimodal capabilities, and the company has faced talent drain, with key researchers leaving for better-paying opportunities at larger tech firms. Despite these challenges, V4’s release is seen as a potential milestone for China’s AI industry, demonstrating that advanced models can run on domestic hardware ecosystems. While it may not be a groundbreaking model in terms of performance, its success could validate China’s broader strategy for AI independence.

marsbit04/15 10:32

The DeepSeek You've Been Waiting For Has Long Changed

marsbit04/15 10:32

NVIDIA Starts Selling the Method of Making Shovels

NVIDIA is accelerating its own GPU design process with AI tools, reducing tasks that took 8 engineers 1010 months to a single GPU running overnight, while achieving better results. However, it is not using AI to fully design GPUs yet. Instead, it is embedding its technology across the chip industry. A key move is its $2020 billion investment in Synopsys, a leading EDA (Electronic Design Automation) tool provider, and partnerships with Cadence and Siemens. This integrates NVIDIA’s hardware acceleration into essential chip design software, making its GPUs the fastest platform for EDA tools. Competitors like AMD and Intel now face a dilemma: use slower tools or rely on NVIDIA’s hardware to design competing chips. Chinese GPU firms, despite high market valuations, struggle with massive losses and dependence on restricted foreign EDA tools. NVIDIA’s strategy extends from AI-enhanced design to manufacturing, creating a self-reinforcing cycle where its hardware becomes essential for developing faster chips—even for those aiming to compete with it.

marsbit04/15 10:04

NVIDIA Starts Selling the Method of Making Shovels

marsbit04/15 10:04

NVIDIA's Market Share in China Drops Below 60%, Domestic AI Chips Seize Market with 1.65 Million Units Delivered Annually

Nvidia's market share in China's AI accelerator card market has declined significantly, dropping from approximately 95% to 55% in 2025, according to IDC data. During the same period, domestic Chinese manufacturers collectively captured 41% of the market, shipping 1.65 million units out of a total market of 4 million units. Huawei led the domestic suppliers with 812,000 units shipped, representing nearly half of the local market share. This shift is driven by both U.S. export controls and China’s aggressive domestic substitution policies. In November 2025, Beijing mandated that state-funded data centers must use domestic AI chips, accelerating the adoption of local alternatives. Huawei recently launched the Atlas 350 accelerator card, claiming 2.87 times the inference performance of Nvidia’s H20 in low-precision computing, though direct comparisons are complicated by architectural differences. While Chinese chips still lag behind in training large-scale AI models—estimated to be 5-10 years behind Nvidia—they have reached a "good enough" level for many commercial applications like inference tasks. The main challenge remains software ecosystem development, as Nvidia’s CUDA platform remains the industry standard. Chinese firms are responding with compatibility efforts and open-source initiatives. Several domestic AI chip companies are now pursuing IPOs, and Huawei continues heavy R&D spending to reduce foreign dependency. Even if U.S. export policies ease, the structural move toward domestic AI chips appears irreversible.

marsbit04/03 05:51

NVIDIA's Market Share in China Drops Below 60%, Domestic AI Chips Seize Market with 1.65 Million Units Delivered Annually

marsbit04/03 05:51

The $59 Billion Illusion: How the Female Version of Buffett Fell from Grace?

"Cathie Wood, once hailed as the 'next Warren Buffett' and a star among millennial investors, saw her flagship ARKK ETF soar to a peak of $59 billion in assets under management (AUM) by February 2021. Her strategy of betting on disruptive technologies like Tesla, genomics, and AI—while publicly sharing her research and daily trades—initially delivered staggering returns, with ARKK surging 152% in 2020 as she doubled down during the COVID crash. However, rising interest rates exposed the fragility of her high-growth, unprofitable tech holdings. ARKK plummeted nearly 75% from its peak, erasing over $50 billion in AUM by 2026. Critics labeled her approach—essentially applying venture capital (VC) logic to public markets—as fundamentally flawed. Unlike VC, where losses are absorbed by private gains, public markets impose real-time pricing and liquidity pressures, accelerating losses during downturns. Ironically, while Wood correctly predicted the AI revolution, she sold NVIDIA early—missing out on over $1.2 billion in gains—to maintain her "anti-consensus" brand and focus on smaller, speculative names. Her daily transparency and massive scale turned her into a target, as markets anticipated her moves. Despite recent pivots back into gene editing and AI infrastructure, her assets remain a fraction of their peak, underscoring the gap between predicting trends and profiting from them."

marsbit04/02 04:13

The $59 Billion Illusion: How the Female Version of Buffett Fell from Grace?

marsbit04/02 04:13

Nvidia Loses 40% of Chinese Market in Three Years | Rewire Morning News

Nvidia's market share in China's AI GPU market has dropped from 95% to 55% over three years, with Huawei capturing 20% of the market. The U.S. Federal Reserve warns that AI hype itself may be driving inflation by boosting consumer spending and corporate investment. Intel repurchased a factory in Ireland for $14.2 billion, signaling a strategic shift toward manufacturing sovereignty. Wall Street firms like JPMorgan and Paradigm are entering the prediction markets, indicating growing institutional interest. In DeFi, Drift Protocol on Solana suffered a $285 million hack due to a private key leak. Other notable news includes Block’s plan to replace middle management with AI, Anthropic’s DMCA takedown mishap, and a record $16.1 billion in Chinese VC funding for Q1 focused on AI and semiconductors.

marsbit04/02 02:25

Nvidia Loses 40% of Chinese Market in Three Years | Rewire Morning News