Tech **Future of NVIDIA & AI Robots** **앤비디아의 미래와 AI 로봇**
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Date 25-11-03 11:11
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**Future of NVIDIA & AI Robots**
**앤비디아의 미래와 AI 로봇**
---
### 1. English
NVIDIA’s core bet for the next 3–5 years is that **“digital AI → agentic AI → physical AI (robots)”** will be the next S-curve. Jensen Huang has already said “the next big thing is physical AI, AI with a body.” ([Future Nexus][1])
1. **Compute first, then robots**
* NVIDIA is pushing ever-bigger AI computers — Blackwell (B200/GB200), Blackwell Ultra (H2 2025), and the Rubin line (2026–27) to train large multimodal and robot foundation models at scale. This is what finances and enables robotics. ([NVIDIA][2])
* Hyperscalers (Azure, OCI, etc.) are already deploying “supercomputer-scale” GB300/Blackwell clusters so that partners like OpenAI and robot startups can train embodied models faster. ([Tom's Hardware][3])
* Even the power architecture (800V AI factories, Kyber/NVL racks up to 600 kW) is being redesigned because future robots will depend on massive cloud training cycles. ([NVIDIA Developer][4])
2. **Robot brains: Project GR00T → Isaac GR00T N1**
* In 2024 NVIDIA showed **Project GR00T**, a generalist foundation model for humanoid robots — learn from a few human demos, understand language, copy motion. ([NVIDIA Newsroom][5])
* In March 2025 they made it concrete as **Isaac GR00T N1**, the first open, customizable humanoid-robot foundation model, with simulation frameworks and even a new physics engine (with Google DeepMind & Disney). This is the start of “the age of generalist robotics.” ([NVIDIA Newsroom][6])
* By late 2025 they opened more models and Isaac Lab libraries, so researchers can break down complex instructions into executable robot skills. ([NVIDIA Newsroom][7])
* The logic: **LLM/VLM in the cloud → GR00T-style embodied policy → deploy on Jetson/Thor at the edge.** ([hackster.io][8])
3. **Robot bodies: partners will build them**
* NVIDIA is **not** going to mass-produce humanoids; instead it supplies the AI computers and the common model. Robot makers — Agility Robotics (Digit), Figure, 1X, NEURA, Apptronik, Boston Dynamics pilots — plug into Isaac + GR00T. ([agilityrobotics.com][9])
* This is the same “GPU ecosystem” playbook: make the platform so hundreds of startups can ship robots for factories, logistics, retail, eldercare, and defense. ([NVIDIA Blog][10])
4. **Why this is a big deal**
* Today, most robots are **task-specific** (AMRs, pick-and-place). NVIDIA wants **generalist robots** that can be re-taught in simulation and then updated OTA — just like we update LLMs. Isaac Sim/Omniverse gives realistic physics + synthetic data so robots learn before they ever touch the real world. ([NVIDIA Developer][11])
* If GR00T-class models become open/common, the cost to add “one more skill” (wipe a table, restock, carry, open door) falls sharply — and that’s when we start seeing “millions to billions of robots,” which NVIDIA itself has started mentioning. ([Le Monde.fr][12])
5. **NVIDIA’s future business shape**
* **Data-center AI (trillion-dollar run-rate by ~2028)** is still the engine. But… every robot will need training (cloud) + sim (Omniverse) + inference computer (Jetson/Thor). That’s 3 revenue streams per robot. ([AP News][13])
* Rubin/Rubin Ultra 2026–27 + Kyber megawatt racks 2027–28 are sized for exactly this “physical AI” wave. ([TechRadar][14])
6. **Risks / What to watch**
* Need cheaper power and cooling, otherwise megawatt AI factories slow adoption. ([NVIDIA Developer][4])
* Need safety, alignment, and content-filtering baked into robot models (a bad LLM reply is one thing; a bad arm motion is another).
* China, Korea, U.S. are all building national robot/embodied-AI bases — geopolitics and export controls can affect GPU supply. ([조선일보][15])
* Competition from Tesla Optimus, OpenAI-backed robotics, and national champions means NVIDIA must stay neutral and “platform-like.” ([Le Monde.fr][12])
**Bottom line (EN):** NVIDIA is trying to do for robots in 2025–30 what it did for AI in 2016–24: make the GPU/model/simulation stack so good and so cheap that everybody else builds on top of it.
---
### 2. 한국어
앤비디아는 앞으로 **“디지털 AI → 에이전트 AI → 물리 AI(로봇)”**로 넘어가는 전환을 회사의 다음 성장축으로 보고 있습니다. 젠슨 황이 2025년에도 “다음 큰 물결은 몸을 가진 AI, 즉 물리 AI”라고 못 박은 이유가 여기에 있습니다. ([Future Nexus][1])
1. **먼저 연산, 그다음 로봇**
* 블랙웰(B200/GB200), 2025년 하반기 블랙웰 울트라, 2026–27년 루빈(Rubin) 아키텍처까지 이어지는 초대형 GPU 로드맵은 결국 거대한 로봇·임베디드 모델을 학습시키기 위한 ‘발전소’입니다. ([NVIDIA][2])
* MS Azure, OCI 같은 곳이 이미 블랙웰/GB300 초대형 클러스터를 깔고 있는 이유도 파트너들이 로봇 모델을 훨씬 빨리 학습시키게 하려는 겁니다. ([Tom's Hardware][3])
* 심지어 800V AI 팩토리, 600kW급 Kyber 랙 같은 전력 구조까지 새로 짜고 있습니다. 로봇이 많아질수록 클라우드 학습 주기가 폭증하기 때문입니다. ([NVIDIA Developer][4])
2. **로봇의 ‘뇌’ : GR00T → Isaac GR00T N1**
* 2024년의 Project GR00T는 “사람이 몇 번 보여주면 따라 하고, 자연어를 이해하고, 사람 동작을 모방하는 범용 휴머노이드 모델”이었습니다. ([NVIDIA Newsroom][5])
* 2025년 GTC에서 이걸 실제 제품화한 게 **Isaac GR00T N1**이고, 구글 딥마인드·디즈니와 만든 뉴턴 물리엔진까지 붙여서 “일반 목적 로봇 시대”를 열겠다고 한 겁니다. ([NVIDIA Newsroom][6])
* 2025년 9월에 공개된 오픈 모델/시뮬레이션 라이브러리는 복잡한 지시를 여러 하위 동작으로 쪼개서 실행하는 데 초점을 둡니다. ([NVIDIA Newsroom][7])
* 구조는 간단합니다: **클라우드의 LLM/VLM → GR00T가 몸짓·조작 정책으로 변환 → 제트슨/Thor 같은 엣지 컴퓨터에 탑재.** ([hackster.io][8])
3. **몸체는 파트너가 만든다**
* 앤비디아는 휴머노이드를 대량 생산하지 않습니다. 대신 Agility(디짓), Figure, 1X, NEURA, Apptronik, 일부 보스턴 다이내믹스 파일럿들이 Isaac + GR00T 생태계에 들어오게 만들고 있습니다. ([agilityrobotics.com][9])
* 결국 GPU 때와 똑같이 “우리가 플랫폼이니까, 너희는 장비를 찍어라” 모델로 가는 것이죠. 물류, 공장, 리테일, 요양, 국방까지 노릴 수 있습니다. ([NVIDIA Blog][10])
4. **왜 중요한가**
* 지금 로봇은 대부분 한 가지 일만 잘합니다. 앤비디아는 시뮬레이션과 OTA 업데이트를 통해 “오늘은 창고, 내일은 쇼룸, 모레는 병원” 식으로 역할을 갈아끼우는 범용 로봇을 의도합니다. Omniverse/Isaac Sim이 그 가상 학습장을 제공하죠. ([NVIDIA Developer][11])
* GR00T급 모델이 업계 공통 언어가 되면, “기술 하나 더”의 비용이 떨어지면서 진짜로 수백만~수십억 대의 로봇 얘기를 할 수 있습니다. ([Le Monde.fr][12])
5. **앤비디아 사업 구조의 변화**
* 여전히 데이터센터 AI가 돈을 벌어주지만, 앞으로는 **로봇 1대 = (클라우드 학습 + 시뮬레이션 + 엣지 컴퓨터)** 3번 돈을 내는 구조가 됩니다. ([AP News][13])
* 그래서 2026–28년 라인업이 전부 물리 AI 확산기에 맞춰져 있습니다. ([The Next Platform][16])
6. **주의할 점**
* 전력·냉각 문제
* 실제 환경에서의 안전·규제
* 미·중·한의 로봇/반도체 전략 변화
* 테슬라·오픈AI 연계 로봇과의 경쟁
**결론(한국어):** 앤비디아는 “AI가 말을 잘하게 된” 그 순간을, “로봇이 몸으로 잘 하게 되는” 순간까지 끌고 가려는 중입니다.
---
### 3. 中文（简体）
英伟达未来几年的关键词可以概括成：**算力驱动 → 通用模型 → 物理化（具身）AI → 机器人规模化**。黄仁勋多次说过：“下一个大趋势是有身体的AI。” ([Future Nexus][1])
1. **先把算力做到极致**
* Blackwell、Blackwell Ultra 到 2026–27 年的 Rubin 系列，其实都是为了训练更大、能理解语言+视觉+动作的机器人基础模型。([NVIDIA][2])
* 微软、甲骨文等正在上马超大规模 GB300 / Blackwell 集群，就是为了把这类“具身模型”的训练时间从几个月压到几周。([Tom's Hardware][3])
* 为了喂饱这些模型，英伟达还提出 800V AI 工厂、电力到机架 600kW 的设计，说明它在为“机器人时代的训练洪峰”铺基础设施。([NVIDIA Developer][4])
2. **机器人通用大脑：Project GR00T → Isaac GR00T N1**
* 2024 年的 Project GR00T 想解决两个核心问题：会听人话（自然语言）+ 会看人做然后模仿（少样本示范学习）。([NVIDIA Newsroom][5])
* 2025 年英伟达把它做成开源、可定制的 **Isaac GR00T N1**，再配合新物理引擎、仿真数据流水线，让任何一家机器人公司都能“拿来就训、拿来就用”。这是它说“通用机器人时代来了”的底气。([NVIDIA Newsroom][6])
* 9 月又开放更多 Isaac/GR00T 资源，重点是让机器人把一条复杂指令拆成一系列可执行动作。([NVIDIA Newsroom][7])
3. **外形和场景交给生态伙伴**
* 英伟达不造机器人本体，而是让 Agility（Digit）、Figure、1X、NEURA、Apptronik 等来造，再用 Isaac + GR00T 做“统一大脑”。([agilityrobotics.com][9])
* 这跟当年它做 GPU 平台的打法一模一样：我把最难的芯片、驱动、工具、仿真、模型都准备好，你们去做垂直场景。([NVIDIA Blog][10])
4. **为什么说是临界点**
* 以往机器人一换场景就得重新写代码；现在可以先在 Omniverse / Isaac Sim 里生成合成数据、做物理仿真，再一次性下发到很多机器人上，这大大降低了单位技能成本。([NVIDIA Developer][11])
* 如果 GR00T 这一类模型真的成了行业通用层，运输、仓储、制造、零售、医疗、养老都会很快出现“泛用型”机器人。([Le Monde.fr][12])
5. **商业上的想象空间**
* 每一台机器人的背后，都会对应：①云端训练/推理（卖 GPU）+ ②仿真/数据生成（卖 Omniverse/Isaac 服务）+ ③边缘部署（卖 Jetson/Thor 模块）。这是一个多次收费的模式。([AP News][13])
* 到 2028 年英伟达认为数据中心业务有望达万亿美元规模，其中相当一部分其实是“为了让机器人动得更好”所付出的算力费用。([AP News][13])
6. **要注意的挑战**
* 能源成本、冷却和机房选址
* 机器人安全标准尚未统一
* 地缘政治下的芯片/机器人产业链分块化
* 特斯拉、OpenAI 以及本土机器人阵营的竞争
**总结（中文）：** 英伟达要把“会说话的AI”扩展成“会干活的AI”，把软件智能注入到真实世界的机器之中，这正是它 2025–2030 年的主线。
---
### 4. 日本語
NVIDIA は今後の成長を「デジタルで完結するAI」から「自律エージェントAI」、そして「物理世界で動くAI＝ロボット」へと広げようとしています。ジェンスン・フアンが「次はボディを持つAIだ」と繰り返しているのはこのためです。([Future Nexus][1])
1. **まず計算基盤を最大化**
* Blackwell、2025年後半の Blackwell Ultra、さらに 2026–27年の Rubin は、より大きな具身AI・ロボット基盤モデルを回すための“発電所”です。([NVIDIA][2])
* Azure や OCI が超大規模GB300/Blackwellクラスターを入れているのも、パートナーがロボット用モデルを数週間で学習できるようにするためです。([Tom's Hardware][3])
* そのために 800V AIファクトリーや 600kW ラックといった電力インフラまで再設計しています。([NVIDIA Developer][4])
2. **ロボットの頭脳：Project GR00T → Isaac GR00T N1**
* 2024年の Project GR00T は「少ない人間デモで新しい作業を覚える」「自然言語を理解する」「人間の動きの模倣」をねらった汎用ヒューマノイド向けモデルでした。([NVIDIA Newsroom][5])
* 2025年GTCでこれを **Isaac GR00T N1** として公開し、Google DeepMind・Disneyと開発した新しい物理エンジン、シミュレーション基盤も合わせて出して「汎用ロボット時代の入口」と位置づけました。([NVIDIA Newsroom][6])
* 2025年9月には Isaac/GR00T のオープンモデルが拡張され、長い指示を「つかむ→運ぶ→置く」などのサブタスクに自動で分解できるようにしています。([NVIDIA Newsroom][7])
3. **ボディはパートナーが作る**
* NVIDIA 自身はロボット量産をせず、Agility Robotics（Digit）、Figure、1X、NEURA、Apptronik などが NVIDIA Isaac + GR00T を使ってヒューマノイドを作る構図です。([agilityrobotics.com][9])
* これは GPU エコシステムと同じで、「頭脳と開発環境は NVIDIA が用意するから、各社は用途特化ロボットを量産してね」というモデルです。([NVIDIA Blog][10])
4. **なぜ今が分岐点なのか**
* これまでは環境が変わるたびにロボット用コードを書き直す必要がありました。Omniverse / Isaac Sim で事前に物理的にリアルなシーンをつくり、合成データで学習し、そのまま複数の実機に配信できるようになると、1スキルあたりのコストが一気に下がります。([NVIDIA Developer][11])
* その結果、物流・製造・小売・医療・介護など多分野で「汎用に近いロボット」を導入しやすくなります。([Le Monde.fr][12])
5. **ビジネスとしてのかたち**
* 1台のロボットの背後に「クラウドでの学習・推論」「シミュレーション／データ生成」「エッジ用のJetson/Thor」がそれぞれ存在するので、NVIDIA は1台につき複数ポイントで収益化できます。([AP News][13])
* 2026–28年に予定されている Rubin / Kyber メガラックは、まさにこの“物理AIラッシュ”に合わせた容量拡張です。([The Next Platform][16])
6. **課題**
* 電力・冷却コスト
* 実世界での安全・規制
* 米中などの地政学的リスク
* テスラやOpenAI連合との競争
**まとめ（日本語）：** NVIDIA は「ロボットにとってのAndroid」を狙っており、クラウドの巨大AI・シミュレーション・エッジGPUをひとつのロードマップに束ねることで、2030年ごろにくるロボット大量導入の波を自社プラットフォームに乗せようとしています.
[1]: https://www.heyfuturenexus.com/the-humanoid-era-5-leaders-defining-physical-ai/?utm_source=chatgpt.com "The Humanoid Era: 5 Leaders Defining Physical AI"
[2]: https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/?utm_source=chatgpt.com "NVIDIA Blackwell Architecture"
[3]: https://www.tomshardware.com/tech-industry/artificial-intelligence/microsoft-deploys-worlds-first-supercomputer-scale-gb300-nvl72-azure-cluster-4-608-gb300-gpus-linked-together-to-form-a-single-unified-accelerator-capable-of-1-44-pflops-of-inference?utm_source=chatgpt.com "Microsoft deploys world's first 'supercomputer-scale' GB300 NVL72 Azure cluster - 4,608 GB300 GPUs linked together to form a single, unified accelerator capable of 92.1 exaFLOPS of FP4 inference"
[4]: https://developer.nvidia.com/blog/nvidia-800-v-hvdc-architecture-will-power-the-next-generation-of-ai-factories/?utm_source=chatgpt.com "NVIDIA 800 VDC Architecture Will Power the Next ..."
[5]: https://nvidianews.nvidia.com/news/foundation-model-isaac-robotics-platform?utm_source=chatgpt.com "NVIDIA Announces Project GR00T Foundation Model for ..."
[6]: https://nvidianews.nvidia.com/news/nvidia-isaac-gr00t-n1-open-humanoid-robot-foundation-model-simulation-frameworks?utm_source=chatgpt.com "NVIDIA Announces Isaac GR00T N1 — the World's First ..."
[7]: https://nvidianews.nvidia.com/news/nvidia-accelerates-robotics-research-and-development-with-new-open-models-and-simulation-libraries?utm_source=chatgpt.com "NVIDIA Accelerates Robotics Research and Development ..."
[8]: https://www.hackster.io/news/nvidia-s-project-gr00t-running-on-the-jetson-thor-aims-to-deliver-embodied-ai-for-humanoid-robots-a02741d94811?utm_source=chatgpt.com "NVIDIA's Project GR00T, Running on the Jetson Thor, Aims ..."
[9]: https://www.agilityrobotics.com/content/agility-robotics-expands-relationship-with-nvidia?utm_source=chatgpt.com "Agility Robotics Expands Relationship with NVIDIA"
[10]: https://blogs.nvidia.com/blog/automatica-robotics-2025/?utm_source=chatgpt.com "NVIDIA and Partners Highlight Next-Generation Robotics ..."
[11]: https://developer.nvidia.com/isaac?utm_source=chatgpt.com "NVIDIA Isaac - AI Robot Development Platform"
[12]: https://www.lemonde.fr/en/economy/article/2025/03/15/artificial-intelligence-google-openai-meta-and-amazon-turn-to-robotics_6739162_19.html?utm_source=chatgpt.com "Artificial intelligence: Google, OpenAI, Meta and Amazon turn to robotics"
[13]: https://apnews.com/article/457e9260aa2a34c1bbcc07c98b7a0555?utm_source=chatgpt.com "Nvidia CEO Jensen Huang unveils new Rubin AI chips at GTC 2025"
[14]: https://www.techradar.com/pro/megawatt-class-ai-server-racks-may-well-become-the-norm-before-2030-as-nvidia-displays-600kw-kyber-rack-design?utm_source=chatgpt.com "Megawatt-class AI server racks may well become the norm before 2030 as Nvidia displays 600kW Kyber rack design"
[15]: https://www.chosun.com/english/industry-en/2025/10/09/332GMHUOMBENFADSVRXUSXIVZM/?utm_source=chatgpt.com "China Builds Robot Training Base, U.S. Advances AI Brains"
[16]: https://www.nextplatform.com/2025/03/19/nvidia-draws-gpu-system-roadmap-out-to-2028/?utm_source=chatgpt.com "Nvidia Draws GPU System Roadmap Out To 2028"
**앤비디아의 미래와 AI 로봇**
---
### 1. English
NVIDIA’s core bet for the next 3–5 years is that **“digital AI → agentic AI → physical AI (robots)”** will be the next S-curve. Jensen Huang has already said “the next big thing is physical AI, AI with a body.” ([Future Nexus][1])
1. **Compute first, then robots**
* NVIDIA is pushing ever-bigger AI computers — Blackwell (B200/GB200), Blackwell Ultra (H2 2025), and the Rubin line (2026–27) to train large multimodal and robot foundation models at scale. This is what finances and enables robotics. ([NVIDIA][2])
* Hyperscalers (Azure, OCI, etc.) are already deploying “supercomputer-scale” GB300/Blackwell clusters so that partners like OpenAI and robot startups can train embodied models faster. ([Tom's Hardware][3])
* Even the power architecture (800V AI factories, Kyber/NVL racks up to 600 kW) is being redesigned because future robots will depend on massive cloud training cycles. ([NVIDIA Developer][4])
2. **Robot brains: Project GR00T → Isaac GR00T N1**
* In 2024 NVIDIA showed **Project GR00T**, a generalist foundation model for humanoid robots — learn from a few human demos, understand language, copy motion. ([NVIDIA Newsroom][5])
* In March 2025 they made it concrete as **Isaac GR00T N1**, the first open, customizable humanoid-robot foundation model, with simulation frameworks and even a new physics engine (with Google DeepMind & Disney). This is the start of “the age of generalist robotics.” ([NVIDIA Newsroom][6])
* By late 2025 they opened more models and Isaac Lab libraries, so researchers can break down complex instructions into executable robot skills. ([NVIDIA Newsroom][7])
* The logic: **LLM/VLM in the cloud → GR00T-style embodied policy → deploy on Jetson/Thor at the edge.** ([hackster.io][8])
3. **Robot bodies: partners will build them**
* NVIDIA is **not** going to mass-produce humanoids; instead it supplies the AI computers and the common model. Robot makers — Agility Robotics (Digit), Figure, 1X, NEURA, Apptronik, Boston Dynamics pilots — plug into Isaac + GR00T. ([agilityrobotics.com][9])
* This is the same “GPU ecosystem” playbook: make the platform so hundreds of startups can ship robots for factories, logistics, retail, eldercare, and defense. ([NVIDIA Blog][10])
4. **Why this is a big deal**
* Today, most robots are **task-specific** (AMRs, pick-and-place). NVIDIA wants **generalist robots** that can be re-taught in simulation and then updated OTA — just like we update LLMs. Isaac Sim/Omniverse gives realistic physics + synthetic data so robots learn before they ever touch the real world. ([NVIDIA Developer][11])
* If GR00T-class models become open/common, the cost to add “one more skill” (wipe a table, restock, carry, open door) falls sharply — and that’s when we start seeing “millions to billions of robots,” which NVIDIA itself has started mentioning. ([Le Monde.fr][12])
5. **NVIDIA’s future business shape**
* **Data-center AI (trillion-dollar run-rate by ~2028)** is still the engine. But… every robot will need training (cloud) + sim (Omniverse) + inference computer (Jetson/Thor). That’s 3 revenue streams per robot. ([AP News][13])
* Rubin/Rubin Ultra 2026–27 + Kyber megawatt racks 2027–28 are sized for exactly this “physical AI” wave. ([TechRadar][14])
6. **Risks / What to watch**
* Need cheaper power and cooling, otherwise megawatt AI factories slow adoption. ([NVIDIA Developer][4])
* Need safety, alignment, and content-filtering baked into robot models (a bad LLM reply is one thing; a bad arm motion is another).
* China, Korea, U.S. are all building national robot/embodied-AI bases — geopolitics and export controls can affect GPU supply. ([조선일보][15])
* Competition from Tesla Optimus, OpenAI-backed robotics, and national champions means NVIDIA must stay neutral and “platform-like.” ([Le Monde.fr][12])
**Bottom line (EN):** NVIDIA is trying to do for robots in 2025–30 what it did for AI in 2016–24: make the GPU/model/simulation stack so good and so cheap that everybody else builds on top of it.
---
### 2. 한국어
앤비디아는 앞으로 **“디지털 AI → 에이전트 AI → 물리 AI(로봇)”**로 넘어가는 전환을 회사의 다음 성장축으로 보고 있습니다. 젠슨 황이 2025년에도 “다음 큰 물결은 몸을 가진 AI, 즉 물리 AI”라고 못 박은 이유가 여기에 있습니다. ([Future Nexus][1])
1. **먼저 연산, 그다음 로봇**
* 블랙웰(B200/GB200), 2025년 하반기 블랙웰 울트라, 2026–27년 루빈(Rubin) 아키텍처까지 이어지는 초대형 GPU 로드맵은 결국 거대한 로봇·임베디드 모델을 학습시키기 위한 ‘발전소’입니다. ([NVIDIA][2])
* MS Azure, OCI 같은 곳이 이미 블랙웰/GB300 초대형 클러스터를 깔고 있는 이유도 파트너들이 로봇 모델을 훨씬 빨리 학습시키게 하려는 겁니다. ([Tom's Hardware][3])
* 심지어 800V AI 팩토리, 600kW급 Kyber 랙 같은 전력 구조까지 새로 짜고 있습니다. 로봇이 많아질수록 클라우드 학습 주기가 폭증하기 때문입니다. ([NVIDIA Developer][4])
2. **로봇의 ‘뇌’ : GR00T → Isaac GR00T N1**
* 2024년의 Project GR00T는 “사람이 몇 번 보여주면 따라 하고, 자연어를 이해하고, 사람 동작을 모방하는 범용 휴머노이드 모델”이었습니다. ([NVIDIA Newsroom][5])
* 2025년 GTC에서 이걸 실제 제품화한 게 **Isaac GR00T N1**이고, 구글 딥마인드·디즈니와 만든 뉴턴 물리엔진까지 붙여서 “일반 목적 로봇 시대”를 열겠다고 한 겁니다. ([NVIDIA Newsroom][6])
* 2025년 9월에 공개된 오픈 모델/시뮬레이션 라이브러리는 복잡한 지시를 여러 하위 동작으로 쪼개서 실행하는 데 초점을 둡니다. ([NVIDIA Newsroom][7])
* 구조는 간단합니다: **클라우드의 LLM/VLM → GR00T가 몸짓·조작 정책으로 변환 → 제트슨/Thor 같은 엣지 컴퓨터에 탑재.** ([hackster.io][8])
3. **몸체는 파트너가 만든다**
* 앤비디아는 휴머노이드를 대량 생산하지 않습니다. 대신 Agility(디짓), Figure, 1X, NEURA, Apptronik, 일부 보스턴 다이내믹스 파일럿들이 Isaac + GR00T 생태계에 들어오게 만들고 있습니다. ([agilityrobotics.com][9])
* 결국 GPU 때와 똑같이 “우리가 플랫폼이니까, 너희는 장비를 찍어라” 모델로 가는 것이죠. 물류, 공장, 리테일, 요양, 국방까지 노릴 수 있습니다. ([NVIDIA Blog][10])
4. **왜 중요한가**
* 지금 로봇은 대부분 한 가지 일만 잘합니다. 앤비디아는 시뮬레이션과 OTA 업데이트를 통해 “오늘은 창고, 내일은 쇼룸, 모레는 병원” 식으로 역할을 갈아끼우는 범용 로봇을 의도합니다. Omniverse/Isaac Sim이 그 가상 학습장을 제공하죠. ([NVIDIA Developer][11])
* GR00T급 모델이 업계 공통 언어가 되면, “기술 하나 더”의 비용이 떨어지면서 진짜로 수백만~수십억 대의 로봇 얘기를 할 수 있습니다. ([Le Monde.fr][12])
5. **앤비디아 사업 구조의 변화**
* 여전히 데이터센터 AI가 돈을 벌어주지만, 앞으로는 **로봇 1대 = (클라우드 학습 + 시뮬레이션 + 엣지 컴퓨터)** 3번 돈을 내는 구조가 됩니다. ([AP News][13])
* 그래서 2026–28년 라인업이 전부 물리 AI 확산기에 맞춰져 있습니다. ([The Next Platform][16])
6. **주의할 점**
* 전력·냉각 문제
* 실제 환경에서의 안전·규제
* 미·중·한의 로봇/반도체 전략 변화
* 테슬라·오픈AI 연계 로봇과의 경쟁
**결론(한국어):** 앤비디아는 “AI가 말을 잘하게 된” 그 순간을, “로봇이 몸으로 잘 하게 되는” 순간까지 끌고 가려는 중입니다.
---
### 3. 中文（简体）
英伟达未来几年的关键词可以概括成：**算力驱动 → 通用模型 → 物理化（具身）AI → 机器人规模化**。黄仁勋多次说过：“下一个大趋势是有身体的AI。” ([Future Nexus][1])
1. **先把算力做到极致**
* Blackwell、Blackwell Ultra 到 2026–27 年的 Rubin 系列，其实都是为了训练更大、能理解语言+视觉+动作的机器人基础模型。([NVIDIA][2])
* 微软、甲骨文等正在上马超大规模 GB300 / Blackwell 集群，就是为了把这类“具身模型”的训练时间从几个月压到几周。([Tom's Hardware][3])
* 为了喂饱这些模型，英伟达还提出 800V AI 工厂、电力到机架 600kW 的设计，说明它在为“机器人时代的训练洪峰”铺基础设施。([NVIDIA Developer][4])
2. **机器人通用大脑：Project GR00T → Isaac GR00T N1**
* 2024 年的 Project GR00T 想解决两个核心问题：会听人话（自然语言）+ 会看人做然后模仿（少样本示范学习）。([NVIDIA Newsroom][5])
* 2025 年英伟达把它做成开源、可定制的 **Isaac GR00T N1**，再配合新物理引擎、仿真数据流水线，让任何一家机器人公司都能“拿来就训、拿来就用”。这是它说“通用机器人时代来了”的底气。([NVIDIA Newsroom][6])
* 9 月又开放更多 Isaac/GR00T 资源，重点是让机器人把一条复杂指令拆成一系列可执行动作。([NVIDIA Newsroom][7])
3. **外形和场景交给生态伙伴**
* 英伟达不造机器人本体，而是让 Agility（Digit）、Figure、1X、NEURA、Apptronik 等来造，再用 Isaac + GR00T 做“统一大脑”。([agilityrobotics.com][9])
* 这跟当年它做 GPU 平台的打法一模一样：我把最难的芯片、驱动、工具、仿真、模型都准备好，你们去做垂直场景。([NVIDIA Blog][10])
4. **为什么说是临界点**
* 以往机器人一换场景就得重新写代码；现在可以先在 Omniverse / Isaac Sim 里生成合成数据、做物理仿真，再一次性下发到很多机器人上，这大大降低了单位技能成本。([NVIDIA Developer][11])
* 如果 GR00T 这一类模型真的成了行业通用层，运输、仓储、制造、零售、医疗、养老都会很快出现“泛用型”机器人。([Le Monde.fr][12])
5. **商业上的想象空间**
* 每一台机器人的背后，都会对应：①云端训练/推理（卖 GPU）+ ②仿真/数据生成（卖 Omniverse/Isaac 服务）+ ③边缘部署（卖 Jetson/Thor 模块）。这是一个多次收费的模式。([AP News][13])
* 到 2028 年英伟达认为数据中心业务有望达万亿美元规模，其中相当一部分其实是“为了让机器人动得更好”所付出的算力费用。([AP News][13])
6. **要注意的挑战**
* 能源成本、冷却和机房选址
* 机器人安全标准尚未统一
* 地缘政治下的芯片/机器人产业链分块化
* 特斯拉、OpenAI 以及本土机器人阵营的竞争
**总结（中文）：** 英伟达要把“会说话的AI”扩展成“会干活的AI”，把软件智能注入到真实世界的机器之中，这正是它 2025–2030 年的主线。
---
### 4. 日本語
NVIDIA は今後の成長を「デジタルで完結するAI」から「自律エージェントAI」、そして「物理世界で動くAI＝ロボット」へと広げようとしています。ジェンスン・フアンが「次はボディを持つAIだ」と繰り返しているのはこのためです。([Future Nexus][1])
1. **まず計算基盤を最大化**
* Blackwell、2025年後半の Blackwell Ultra、さらに 2026–27年の Rubin は、より大きな具身AI・ロボット基盤モデルを回すための“発電所”です。([NVIDIA][2])
* Azure や OCI が超大規模GB300/Blackwellクラスターを入れているのも、パートナーがロボット用モデルを数週間で学習できるようにするためです。([Tom's Hardware][3])
* そのために 800V AIファクトリーや 600kW ラックといった電力インフラまで再設計しています。([NVIDIA Developer][4])
2. **ロボットの頭脳：Project GR00T → Isaac GR00T N1**
* 2024年の Project GR00T は「少ない人間デモで新しい作業を覚える」「自然言語を理解する」「人間の動きの模倣」をねらった汎用ヒューマノイド向けモデルでした。([NVIDIA Newsroom][5])
* 2025年GTCでこれを **Isaac GR00T N1** として公開し、Google DeepMind・Disneyと開発した新しい物理エンジン、シミュレーション基盤も合わせて出して「汎用ロボット時代の入口」と位置づけました。([NVIDIA Newsroom][6])
* 2025年9月には Isaac/GR00T のオープンモデルが拡張され、長い指示を「つかむ→運ぶ→置く」などのサブタスクに自動で分解できるようにしています。([NVIDIA Newsroom][7])
3. **ボディはパートナーが作る**
* NVIDIA 自身はロボット量産をせず、Agility Robotics（Digit）、Figure、1X、NEURA、Apptronik などが NVIDIA Isaac + GR00T を使ってヒューマノイドを作る構図です。([agilityrobotics.com][9])
* これは GPU エコシステムと同じで、「頭脳と開発環境は NVIDIA が用意するから、各社は用途特化ロボットを量産してね」というモデルです。([NVIDIA Blog][10])
4. **なぜ今が分岐点なのか**
* これまでは環境が変わるたびにロボット用コードを書き直す必要がありました。Omniverse / Isaac Sim で事前に物理的にリアルなシーンをつくり、合成データで学習し、そのまま複数の実機に配信できるようになると、1スキルあたりのコストが一気に下がります。([NVIDIA Developer][11])
* その結果、物流・製造・小売・医療・介護など多分野で「汎用に近いロボット」を導入しやすくなります。([Le Monde.fr][12])
5. **ビジネスとしてのかたち**
* 1台のロボットの背後に「クラウドでの学習・推論」「シミュレーション／データ生成」「エッジ用のJetson/Thor」がそれぞれ存在するので、NVIDIA は1台につき複数ポイントで収益化できます。([AP News][13])
* 2026–28年に予定されている Rubin / Kyber メガラックは、まさにこの“物理AIラッシュ”に合わせた容量拡張です。([The Next Platform][16])
6. **課題**
* 電力・冷却コスト
* 実世界での安全・規制
* 米中などの地政学的リスク
* テスラやOpenAI連合との競争
**まとめ（日本語）：** NVIDIA は「ロボットにとってのAndroid」を狙っており、クラウドの巨大AI・シミュレーション・エッジGPUをひとつのロードマップに束ねることで、2030年ごろにくるロボット大量導入の波を自社プラットフォームに乗せようとしています.
[1]: https://www.heyfuturenexus.com/the-humanoid-era-5-leaders-defining-physical-ai/?utm_source=chatgpt.com "The Humanoid Era: 5 Leaders Defining Physical AI"
[2]: https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/?utm_source=chatgpt.com "NVIDIA Blackwell Architecture"
[3]: https://www.tomshardware.com/tech-industry/artificial-intelligence/microsoft-deploys-worlds-first-supercomputer-scale-gb300-nvl72-azure-cluster-4-608-gb300-gpus-linked-together-to-form-a-single-unified-accelerator-capable-of-1-44-pflops-of-inference?utm_source=chatgpt.com "Microsoft deploys world's first 'supercomputer-scale' GB300 NVL72 Azure cluster - 4,608 GB300 GPUs linked together to form a single, unified accelerator capable of 92.1 exaFLOPS of FP4 inference"
[4]: https://developer.nvidia.com/blog/nvidia-800-v-hvdc-architecture-will-power-the-next-generation-of-ai-factories/?utm_source=chatgpt.com "NVIDIA 800 VDC Architecture Will Power the Next ..."
[5]: https://nvidianews.nvidia.com/news/foundation-model-isaac-robotics-platform?utm_source=chatgpt.com "NVIDIA Announces Project GR00T Foundation Model for ..."
[6]: https://nvidianews.nvidia.com/news/nvidia-isaac-gr00t-n1-open-humanoid-robot-foundation-model-simulation-frameworks?utm_source=chatgpt.com "NVIDIA Announces Isaac GR00T N1 — the World's First ..."
[7]: https://nvidianews.nvidia.com/news/nvidia-accelerates-robotics-research-and-development-with-new-open-models-and-simulation-libraries?utm_source=chatgpt.com "NVIDIA Accelerates Robotics Research and Development ..."
[8]: https://www.hackster.io/news/nvidia-s-project-gr00t-running-on-the-jetson-thor-aims-to-deliver-embodied-ai-for-humanoid-robots-a02741d94811?utm_source=chatgpt.com "NVIDIA's Project GR00T, Running on the Jetson Thor, Aims ..."
[9]: https://www.agilityrobotics.com/content/agility-robotics-expands-relationship-with-nvidia?utm_source=chatgpt.com "Agility Robotics Expands Relationship with NVIDIA"
[10]: https://blogs.nvidia.com/blog/automatica-robotics-2025/?utm_source=chatgpt.com "NVIDIA and Partners Highlight Next-Generation Robotics ..."
[11]: https://developer.nvidia.com/isaac?utm_source=chatgpt.com "NVIDIA Isaac - AI Robot Development Platform"
[12]: https://www.lemonde.fr/en/economy/article/2025/03/15/artificial-intelligence-google-openai-meta-and-amazon-turn-to-robotics_6739162_19.html?utm_source=chatgpt.com "Artificial intelligence: Google, OpenAI, Meta and Amazon turn to robotics"
[13]: https://apnews.com/article/457e9260aa2a34c1bbcc07c98b7a0555?utm_source=chatgpt.com "Nvidia CEO Jensen Huang unveils new Rubin AI chips at GTC 2025"
[14]: https://www.techradar.com/pro/megawatt-class-ai-server-racks-may-well-become-the-norm-before-2030-as-nvidia-displays-600kw-kyber-rack-design?utm_source=chatgpt.com "Megawatt-class AI server racks may well become the norm before 2030 as Nvidia displays 600kW Kyber rack design"
[15]: https://www.chosun.com/english/industry-en/2025/10/09/332GMHUOMBENFADSVRXUSXIVZM/?utm_source=chatgpt.com "China Builds Robot Training Base, U.S. Advances AI Brains"
[16]: https://www.nextplatform.com/2025/03/19/nvidia-draws-gpu-system-roadmap-out-to-2028/?utm_source=chatgpt.com "Nvidia Draws GPU System Roadmap Out To 2028"