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Nvidia CEO Jensen Huang Spotted at COMPUTEX 2026
![Nvidia CEO Jensen Huang Spotted at COMPUTEX 2026](http://player.bilibili.com/player.html?bvid=BV12b7z6qERf&page=1" frameborder="0"></iframe></p><p>一年一度的COMPUTEX 2026于2026年6月2日至5日在台北举行。展会期间的某一天，黄仁勋与技嘉总经理李宜泰在官方活动结束后决定稍作休息。网上曝光的画面显示，两位高管直接坐在展区的地板上，在非正式的氛围中交谈，并举起啤酒瓶对饮。</p><p><img src="https://img.3dmgame.com/uploads/images/news/20260605/1780625120_618049.png)
💡See how Nvidia strengthens its AI hardware supply chain through key OEM partnerships at major industry events.
⚡ 30-Second TL;DR
What Changed
Jensen Huang attended COMPUTEX 2026 to engage with hardware partners.
Why It Matters
Stronger ties between Nvidia and major hardware OEMs like Gigabyte ensure faster deployment of AI-ready server infrastructure. This synergy is critical for scaling data center capacity globally.
What To Do Next
Review Gigabyte's latest server hardware specifications to align your infrastructure planning with upcoming Nvidia GPU releases.
Who should care:Enterprise & Security Teams
Key Points
- •Jensen Huang attended COMPUTEX 2026 to engage with hardware partners.
- •The interaction with Gigabyte underscores the importance of the AI hardware supply chain.
- •Informal industry networking remains a key driver for AI infrastructure partnerships.
🧠 Deep Insight
Web-grounded analysis with 30 cited sources.
🔑 Enhanced Key Takeaways
- •Jensen Huang's keynote at COMPUTEX 2026 introduced NVIDIA RTX Spark, a new platform designed to reinvent Windows PCs for personal AI agents, featuring a Blackwell RTX GPU and Grace CPU.
- •Nvidia is strategically expanding its AI infrastructure supply chain beyond semiconductors, exemplified by a recent multi-year partnership with Corning to boost U.S. manufacturing of advanced fiber-optic components essential for AI data centers.
- •Gigabyte, a long-standing Nvidia partner, showcased a comprehensive AI ecosystem at COMPUTEX 2026, including new servers based on NVIDIA's Blackwell and MGX platforms, and RTX AI PCs, demonstrating their deep integration into Nvidia's hardware strategy.
- •Nvidia is actively growing its global AI Cloud ecosystem by collaborating with partners like Firmus to establish 'AI factories' optimized for efficiency, aiming for the lowest cost per token and high throughput.
- •Huang's presence at COMPUTEX 2026 included direct visits to booths of key Taiwanese original equipment manufacturers (OEMs) and suppliers, such as Gigabyte, to strengthen partnerships across the entire AI supply chain, including optical and cooling solutions.
📊 Competitor Analysis▸ Show
| Feature/Category | Nvidia (as of 2026) | AMD (as of 2026) | Hyperscaler Custom Silicon (as of 2026) |
|---|---|---|---|
| AI Accelerator Market Share (Revenue) | ~80-86% | ~5-7% | Rising, projected 10-15% overall, 37% for inference |
| Key AI Accelerators | Blackwell (B200/GB200), Hopper (H100/H200), RTX Spark | Instinct MI300X, MI325X, MI350X | Google TPU, AWS Trainium, Broadcom ASICs |
| FP8 Compute Performance | B200: 4,600 TFLOPS (comparable to MI350X) | MI350X: 4,600 TFLOPS (comparable to B200) | Varies by design, optimized for specific workloads |
| Memory (HBM3E) | B200: 192GB | MI350X: 288GB | Varies by design |
| Software Ecosystem | CUDA (mature, 4M+ developers, 3000+ optimized apps) | ROCm (improving, day-zero support for major models) | Proprietary software stacks, optimized for internal use |
| Real-world Performance | Generally superior due to software maturity (50-55% MFU) | ~45% MFU, improving but still behind Nvidia in many workloads | Highly optimized for specific internal inference tasks |
🛠️ Technical Deep Dive
- NVIDIA RTX Spark Superchip: Features an NVIDIA Blackwell RTX GPU with 6,144 CUDA cores and fifth-generation Tensor Cores with FP4 precision. It is connected via NVIDIA NVLink-C2C chip-to-chip interconnect to a high-performance, 20-core NVIDIA Grace CPU. The platform offers up to 1 petaflop of AI compute and 128GB of unified memory.
- Gigabyte NVIDIA GB300 NVL72: A fully liquid-cooled, rack-scale design that integrates 72 NVIDIA Blackwell Ultra GPUs and 36 Arm-based NVIDIA Grace CPUs. This platform is optimized for test-time scaling inference, providing a 50x higher output for reasoning model inference compared to the NVIDIA Hopper platform. It supports up to 17 TB of LPDDR5X memory and up to 20 TB of HBM3E, utilizing 5th Gen NVIDIA NVLink technology for 1.8TB/s GPU-GPU interconnect.
- Gigabyte NVIDIA HGX B200/B300 Servers: These servers support eight NVIDIA Blackwell GPUs interconnected with NVIDIA NVLink. The B300 platform offers up to 2.3TB of HBM3E memory and delivers 15X faster real-time inference on trillion-parameter models compared to previous generations.
- AI Infrastructure Components: Nvidia's foundational AI platform relies on technologies such as Blackwell GPUs, Spectrum-X networking, NVLink interconnects, and the CUDA software stack.
- Cooling Requirements: AI data centers are increasingly adopting liquid cooling solutions as AI racks surpass 100kW power consumption, a necessity for maintaining optimal performance and energy efficiency.
🔮 Future ImplicationsAI analysis grounded in cited sources
Nvidia will further solidify its 'AI factory' vision by integrating more physical infrastructure components into its supply chain.
The partnership with Corning for fiber optics and the focus on cooling and power partners at COMPUTEX indicate a strategic move to control and optimize the entire physical layer of AI infrastructure.
The market for AI PCs, driven by platforms like RTX Spark, will see significant growth and new form factors.
Nvidia's collaboration with Microsoft and major PC manufacturers (ASUS, Dell, HP, Lenovo, MSI, Acer, Gigabyte) for RTX Spark laptops and desktops signals a concerted effort to bring powerful on-device AI capabilities to mainstream consumers and professionals.
Nvidia's dominance in AI accelerators will face increasing pressure from hyperscaler custom silicon, leading to a slight erosion of market share percentage, but continued absolute revenue growth due to market expansion.
While Nvidia maintains a large market share, hyperscalers are developing their own chips for inference, and AMD is gaining traction, suggesting a more diversified competitive landscape in the coming years.
⏳ Timeline
2021-08
Gigabyte partners with VMware and Nvidia to certify server hardware for NVIDIA AI Enterprise suite, enabling GPU virtualization.
2024-02
Gigabyte promotes its GPU server portfolio, emphasizing collaboration with NVIDIA for AI and HPC computing.
2024-06
Jensen Huang announces at COMPUTEX that ASRock Rack, ASUS, GIGABYTE, and other manufacturers will deliver Blackwell architecture-powered systems.
2024-11
Nvidia begins mass production of GB200 chips.
2025-02
Gigabyte is included in the priority supply list for GB200, with shipments expected to commence in March.
2025-07
Giga Computing announces four new GIGABYTE servers built on the NVIDIA HGX B200 platform.
2026-05
Nvidia announces a multi-year partnership with Corning to expand U.S.-based production of optical connectivity systems for AI infrastructure.
2026-06
Jensen Huang delivers keynote at COMPUTEX 2026, unveiling NVIDIA RTX Spark and DLSS 4.5.
2026-06
Gigabyte officially unveils its Infinity series and other devices at COMPUTEX 2026, highlighting its AI ecosystem and gaming innovation.
📎 Sources (30)
Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.
- nvidia.com
- tomshardware.com
- youtube.com
- salmanrazak.com
- supplychaindigital.com
- substack.com
- nvidia.com
- gigabyte.com
- gigabyte.com
- gigabyte.com
- gigacomputing.com
- slyd.com
- gigabyte.com
- indiatimes.com
- nvidia.com
- sedaily.com
- siliconanalysts.com
- siliconanalysts.com
- companieshistory.com
- insiderfinance.io
- mlq.ai
- thecuberesearch.com
- businessengineer.ai
- asbis.com
- futurumgroup.com
- gigabyte.com
- techpowerup.com
- nvidia.com
- digitimes.com
- gadgetmatch.com
📰
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