🏠IT之家•Stalecollected in 83m
Nvidia CEO Jensen Huang to meet Samsung leadership
💡Critical supply chain update: Nvidia's push for HBM memory could dictate the availability of future AI hardware.
⚡ 30-Second TL;DR
What Changed
Discussions will focus on HBM and next-generation memory technology for AI infrastructure.
Why It Matters
Strengthening ties with Samsung for HBM is critical for Nvidia to maintain its lead in the AI chip market amid high demand for GPU memory.
What To Do Next
Track the supply chain announcements from Nvidia and Samsung, as HBM availability directly impacts the deployment timeline of next-gen AI clusters.
Who should care:Founders & Product Leaders
Key Points
- •Discussions will focus on HBM and next-generation memory technology for AI infrastructure.
- •Jensen Huang is conducting a series of high-level meetings with major Korean tech firms including LG, SK, and Hyundai.
- •The meetings aim to strengthen Nvidia's supply chain for AI-driven hardware and robotics.
🧠 Deep Insight
Web-grounded analysis with 24 cited sources.
🔑 Enhanced Key Takeaways
- •Jensen Huang's visit is part of a broader strategy to build an "Nvidia-centered value chain" in South Korea, encompassing memory chips, robotics hardware, and gaming software, beyond just HBM supply.
- •Nvidia has certified HBM4 products from all three major memory manufacturers—Samsung, SK Hynix, and Micron—for its upcoming Vera Rubin AI accelerator platform, indicating a diversified supply strategy.
- •Samsung is accelerating its HBM development cycle from approximately two years to one year to align with the rapid annual rollout of new AI accelerators from major clients like Nvidia.
- •Nvidia is expanding its robotics partnerships beyond China to include the U.S., Europe, and South Korea, aiming to create a global robotics ecosystem powered by its AI infrastructure.
- •LG Group is investing in 10,000 Nvidia Blackwell GPUs and expanding its partnership with Nvidia into "physical AI," including robotics and smart factories, and co-developing domain-specific AI models.
📊 Competitor Analysis▸ Show
| Feature/Company | SK Hynix | Samsung | Micron |
|---|---|---|---|
| HBM Market Share (Q2 2025) | ~62% | ~17% | ~21% |
| HBM Generations | HBM3E, HBM4, iHBM (thermal design) | HBM3E, HBM4, HBM4E, HBM5 (mockup) | HBM3E, HBM4 |
| HBM4 Certification for Nvidia Rubin | Certified | Certified | Certified |
| HBM4 Bandwidth (per stack) | Up to 3.3 TB/s (HBM4) | Up to 3.6 TB/s (HBM4E) | Exceeding 2.8 TB/s (HBM4) |
| HBM4 Interface Width | 2048-bit | 2048-bit | 2048-bit |
| HBM4 Capacity (per stack) | Up to 64GB (16-Hi stacks) | Up to 64GB (16-layer configurations) | 36GB (12-Hi HBM3E), HBM4 higher |
| HBM4 Power Efficiency | Improved over HBM3E | Improved over HBM4 (HBM4E) | Superior power-efficiency specs |
| HBM Supply Status (2026) | Entire 2026 capacity sold out | Plans 50% capacity surge in 2026, delivering HBM4 samples to Nvidia | Entire 2026 capacity sold out |
🛠️ Technical Deep Dive
- HBM4 Architecture: HBM4 represents a fundamental architectural shift from HBM3e, doubling the memory interface to 2048-bit and integrating a logic base die.
- Bandwidth: HBM4 targets over 2.0 TB/s per stack, with advanced configurations reaching up to 3.3 TB/s, a significant increase from HBM3e's 1.2 TB/s.
- Capacity: HBM4 supports up to 64GB per stack through 16-Hi stacks and 32Gb layers, enabling larger models.
- Power Efficiency: HBM4 aims for lower operating voltage (0.9-1.0V vs. 1.1V in HBM3e) and improved efficiency, targeting ~120 GB/s/W.
- Interposer Redesign: The wider 2048-bit interface of HBM4 requires next-generation interposer technologies with sub-micron routing features to manage increased signal density and prevent crosstalk.
- Logic Base Die: The HBM4 base die is built on a logic process (e.g., 12nm/5nm), requiring the host GPU or ASIC controller to be updated to leverage its capabilities.
- HBM4E: Samsung's HBM4E samples deliver a stable pin speed of 14 Gbps, scalable up to 16 Gbps, providing memory bandwidth of up to 3.6 TB/s per stack.
- Cooling Innovations: Both Samsung (Heat Path Block - HPB) and SK Hynix (iHBM) are developing in-package cooling structures to address thermal bottlenecks in the die-to-die interface of HBM stacks.
🔮 Future ImplicationsAI analysis grounded in cited sources
The HBM supply shortage will persist through 2026 and potentially until 2030.
The demand for HBM from the AI sector is rapidly increasing, with next-generation AI accelerators requiring significantly more memory, and manufacturing capacity cannot scale fast enough to meet this demand.
Nvidia's diversified HBM supplier strategy will reduce supply chain risks for its next-generation AI accelerators.
By certifying HBM4 from all three major manufacturers (SK Hynix, Samsung, Micron), Nvidia ensures greater flexibility, reduces dependence on a single supplier, and prepares for the next wave of AI infrastructure investment.
South Korea is becoming a central hub for Nvidia's "physical AI" ecosystem development.
Jensen Huang's extensive meetings with major Korean conglomerates (Samsung, SK, LG, Hyundai) across memory, robotics, and automotive sectors indicate a strategic focus on leveraging Korean expertise for AI and robotics.
⏳ Timeline
2015
Hyundai Motor Group begins partnership with NVIDIA for intelligent systems.
2024-07
JEDEC announces preliminary specifications for HBM4.
2025-10
NVIDIA and SK Group announce plans to build an AI factory in South Korea and deepen collaboration on HBM.
2026-03
NVIDIA expands partnerships with global robotics leaders for physical AI development.
2026-04
LG Group and NVIDIA agree to expand technical cooperation for next-generation, domain-specific AI models and ecosystems.
2026-06
Samsung begins shipping industry-first 12-layer HBM4E samples to major global customers.
2026-06
NVIDIA certifies HBM4 from SK Hynix, Samsung, and Micron for its upcoming Vera Rubin platform.
📎 Sources (24)
Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.
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