NVIDIA 주가 $295까지 오를 전망…AI 추론 시장에서 74% 점유율 확보
Great News for Nvidia Investors: Wall Street Says the Stock Could Soar to $295
한국 증권사 애널리스트가 독자에게 설명하듯 쓰는 전문가 코멘트. 존댓말 사용. AI 추론 시장에서 NVIDIA의 GPU 점유율이 74%로 확대되고, 업계 전문가들이 목표 주가 $295로 상향 조정하면서, 단기적인 상승세 지속 가능성을 시사합니다.
핵심 요약
NVIDIA의 목표 주가가 295달러로 상향 조정되며, 현재 주가 대비 42% 상승 가능성을 보입니다.
핵심요약
- NVIDIA 주가는 2023년 1월 이후 1,300% 상승
- 월스트리트 목표 주가 265달러에서 295달러로 상향 조정
- AI 추론 시장에서 74% 점유율 확보 (전년 대비 8% 증가)
- GPU의 유연성으로 인해 커스텀 칩 대비 경쟁력 유지
도입
NVIDIA의 최근 주가 상승과 월스트리트 분석가들의 목표 주가 상향 조정은 AI 인프라 수요 증가에 따른 긍정적인 전망을 반영합니다. 특히 AI 추론 시장에서의 점유율 확대는 장기적인 성장 가능성을 시사하며, 투자자들에게 중요한 정보입니다.
본문 1: AI 추론 시장 점유율 확대의 배경
NVIDIA의 GPU는 AI 추론 시장에서 74%의 점유율을 차지하며, 이는 전년 대비 8% 증가한 수치입니다. 이는 GPU의 유연성과 커스텀 칩 대비 다양한 알고리즘 적용 가능성에 기인합니다. NVIDIA의 GPU는 새로운 AI 기술이 개발되면 즉시 적용할 수 있는 장점을 가지고 있으며, 이는 AI 추론 시장에서의 경쟁력을 높이는 주요 요인입니다. 이러한 점유율 확대는 NVIDIA의 장기적인 수익 성장을 지원할 것으로 기대됩니다.
본문 2: 월스트리트 목표 주가 상향 조정의 의미
월스트리트 분석가들은 최근 90일 동안 NVIDIA의 목표 주가를 265달러에서 295달러로 상향 조정했습니다. 이는 현재 주가 대비 42% 상승 가능성을 시사하며, 이는 AI 인프라 수요 증가와 NVIDIA의 시장 점유율 확대에 대한 긍정적인 전망을 반영한 것입니다. 이러한 목표 주가 상향 조정은 투자자들에게 NVIDIA의 잠재적 성장 가능성을 강조하며, 향후 주가 상승의 동력을 제공할 것으로 기대됩니다.
결론
NVIDIA의 AI 추론 시장 점유율 확대는 장기적인 성장 가능성을 시사하며, 월스트리트 목표 주가 상향 조정은 이러한 전망을 반영한 것입니다. 향후 NVIDIA의 주가 동향과 AI 인프라 수요 변화에 주목할 필요가 있습니다.
Original Article
Great News for Nvidia Investors: Wall Street Says the Stock Could Soar to $295
Nvidia ( NVDA 3.99% ) has been one of the biggest winners from the artificial intelligence (AI) infrastructure build-out. The stock has advanced more than 1,300% since January 2023. But most Wall Street analysts still believe Nvidia is deeply undervalued.
In fact, the consensus target price has increased from $265 per share to $295 per share in the last 90 days, according to LSEG. That implies 42% upside from the current share price of $209.
Here's what investors need to know.
Nvidia is gaining market share in AI inference workloads
Nvidia graphics processing units (GPUs) are the industry standard in artificial intelligence (AI) accelerators, chips that assist CPUs by handling repetitive mathematical tasks. Nvidia accounts for more than 80% of AI accelerator sales, but some analysts expected the company to lose significant market share as the industry shifted toward inference .
To elaborate, AI training is a discrete event in which models learn to perform certain tasks, but AI inference is a continuous process wherein models are used to generate outputs. Inference accounts for about two-thirds of AI workloads today, up from about one-third in 2023, and the shift will only intensify in the future as more models are deployed.
Companies like Alphabet and Amazon have designed custom AI accelerators in an effort to reduce their dependence on Nvidia GPUs. In certain scenarios, those custom chips are actually more efficient, but Nvidia's inference market share still increased eight percentage points to 74% over the past year, according to The Information.
Why? GPUs are general-purpose accelerators, while custom chips are designed for specific workloads. That makes them very efficient in certain situations, but it also means they are much less flexible (i.e., they run fewer algorithms). Venture Beat explains, "If a new AI technique is invented tomorrow, a GPU will run it immediately." That is not necessarily true for custom AI accelerators.
Beyond that, Nvidia has a competitive advantage in its vertically integrated business . The company not only designs GPUs but also CPUs, networking, and software that together form a turnkey solution for AI infrastructure. That translates into cost savings for customers. "Nvidia compute is not just the highest performance AI infrastructure, it is the most economic," says CEO Jensen Huang.
Nvidia is gaining market share in other categories of AI infrastructure
While Nvidia is best known for its GPUs, the company is actually gaining share in other AI infrastructure categories. Networking revenue has at least doubled in each of the last three quarters, and it nearly tripled in the most recent quarter, because customers want tightly integrated systems. Nvidia recently became the largest networking company in the world.
Meanwhile, demand for Nvidia's next-generation Vera CPU is already immense ahead of its launch later this year. Vera is twice as efficient as x86-based alternatives (CPUs designed by AMD and Intel ). CFO Colette Kress recently told analysts, "We have visibility to nearly $20 billion in total CPU revenue this year, setting us up to become the world-leading CPU supplier."
AI infrastructure spending is projected to quadruple by the end of the decade
To summarize, Nvidia is gaining share within the inference category of the AI accelerator market. That's important because inference has already surpassed training in terms of workload volume, and it will become an even larger part of the market in the future.
Meanwhile, Nvidia is also gaining share in networking equipment and CPUs as customers prioritize tightly integrated systems. Collectively, that puts the company in a good position. CEO Jensen Huang thinks AI infrastructure spending could hit $4 trillion annually by 2030, up from about $1 trillion today. Grand View Research has published similar numbers.
Here's the big picture: Multiple industry experts expect AI infrastructure spending to grow by 36% annually through the end of the decade. Nvidia is gaining share across multiple categories in that market, suggesting its earnings could grow even faster than 36% annually. That makes the current valuation of 32 times earnings look cheap. Patient investors should feel comfortable buying a small position today.