Samsung Negotiates $700 HBM4 Prices
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Samsung Negotiates $700 HBM4 Prices

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💡HBM4 prices hitting $700 (up 30%) flags surging AI memory costs for GPU builds.

⚡ 30-Second TL;DR

What changed

Samsung negotiating HBM4 at ~$700/unit, 20-30% above HBM3E

Why it matters

Rising HBM4 prices signal strong AI hardware demand, potentially increasing costs for GPU makers like Nvidia and raising barriers for AI training/inference scaling. This could squeeze margins for AI infrastructure builders amid booming needs.

What to do next

Query Samsung and SK Hynix suppliers for HBM4 quotes to budget next-gen AI GPU clusters.

Who should care:Enterprise & Security Teams

🧠 Deep Insight

Web-grounded analysis with 7 cited sources.

🔑 Key Takeaways

  • Samsung began mass production of HBM4 on February 12, 2026, becoming the first manufacturer to achieve this milestone, with commercial shipments already underway to customers[3]
  • HBM4 pricing has surged dramatically within six months—SK Hynix quoted mid-$500 range in August 2025, but Samsung now negotiates at $700, representing a 20-30% premium over HBM3E[2]
  • Samsung's HBM4 delivers industry-leading performance at 11.7 Gbps (up to 13 Gbps under optimal conditions), 37% faster than the JEDEC 8 Gbps standard and 22% ahead of HBM3E's 9.6 Gbps[4]
📊 Competitor Analysis▸ Show
MetricSamsung HBM4SK Hynix HBM4Micron HBM4
Production StatusMass production started Feb 12, 2026[3]Trial operations planned Feb-Mar 2026[6]Shipping started, pre-sold entire 2026 capacity[3]
Pricing~$700/unit[2]Mid-$500 (Aug 2025), expected to align at $700[2]Not publicly disclosed
Data Speed11.7 Gbps (up to 13 Gbps)[3]Not yet disclosedNot yet disclosed
Memory Bandwidth3.3 TB/s per stack[3]Not yet disclosedNot yet disclosed
Capacity Range24-36 GB (48 GB planned)[3]Not yet disclosedNot yet disclosed
Process Node4nm foundry + 1c DRAM[4]12nm foundry + 1b DRAM (estimated)[4]Not disclosed
Market Share (2026)Mid-20%[4]Mid-50%[4]~20%[4]
Key CustomersNVIDIA Vera Rubin[3]NVIDIA, Microsoft (exclusive for in-house AI chips)[7]NVIDIA Vera Rubin[3]

🛠️ Technical Deep Dive

Architecture: Samsung combines 4nm foundry process base die with 10nm-class 6th-generation (1c) DRAM, representing the most advanced pairing in the industry compared to competitors using 12nm foundry and 1b DRAM[4] • Performance Metrics: Achieves 11.7 Gbps standard speed with capability to reach 13 Gbps under optimal conditions, exceeding JEDEC standard of 8 Gbps by 37% and HBM3E's 9.6 Gbps by 22%[4] • Memory Bandwidth: Single-stack bandwidth reaches 3.3 terabytes-per-second, approximately 2.4 times higher than HBM3E predecessor[4] • Stacking Configuration: Currently ships with 12-high stacking for 36 GB capacity; 16-high stacking planned to reach 48 GB[4] • Thermal Management: Enhanced thermal resistance by 10% and heat dissipation by 30% compared to HBM3E[3] • Energy Efficiency: 40% more energy efficient than HBM3E, reducing power consumption and heat generation[3] • Production Yield: Samsung's advanced process nodes (4nm foundry, 1c DRAM) introduce higher production costs and greater yield uncertainty compared to competitors, though Samsung reports HBM4 yield is on track[3][4] • Manufacturing Capacity: Samsung establishing new 1c DRAM production line at P4 factory with planned capacity of 100,000-120,000 wafers per month for HBM[6]

🔮 Future ImplicationsAI analysis grounded in cited sources

The HBM4 pricing surge to $700 signals a structural shift in semiconductor economics driven by AI infrastructure demand. With both Samsung and SK Hynix projected to achieve record 30 trillion won operating profits in Q1 2026, the memory industry is entering a 'mega cycle' where HBM, DRAM, and NAND prices remain elevated through at least Q3 2026[2][6]. Samsung's first-to-market advantage and superior technical specifications (11.7 Gbps vs. industry standard) position it to capture premium pricing despite smaller market share, while SK Hynix's larger capacity and long-term supply agreements with NVIDIA and Microsoft provide volume stability[7]. The accelerated production timelines at both Samsung (P4 fab completion moved to Q4 2026) and SK Hynix (Yongin Phase 1 trial operations in Feb-Mar 2026) indicate manufacturers are betting heavily on sustained AI-driven demand[6]. NVIDIA's Vera Rubin GPU launch in Q2 2026 will be a critical demand inflection point, as it requires HBM4 from both Samsung and SK Hynix[3]. However, the high production costs and yield risks associated with advanced process nodes (4nm foundry, 1c DRAM) mean profitability gains are concentrated among the two Korean leaders, potentially widening the competitive moat against other memory manufacturers.

⏳ Timeline

2025-08
SK Hynix sets HBM4 pricing for NVIDIA at mid-$500 range
2026-02-12
Samsung begins mass production of HBM4, becoming first manufacturer to achieve this milestone
2026-02-13
Samsung and Micron announce HBM4 shipments have started; Micron confirms pre-sold entire 2026 capacity
2026-02-18
Samsung HBM4 pricing reported at $700/unit, 20-30% higher than HBM3E; SK Hynix expected to align pricing

📎 Sources (7)

Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.

  1. sammyfans.com
  2. chosun.com
  3. theregister.com
  4. trendforce.com
  5. gurufocus.com
  6. chosun.com
  7. morningstar.com

Samsung is negotiating HBM4 high-bandwidth memory chip prices at around $700 per unit, 20-30% higher than HBM3E due to surging demand. SK Hynix previously priced HBM4 supplies to Nvidia at $550 but may raise to match Samsung as mass production starts. Analysts predict Q1 operating profits of 30 trillion KRW for both firms.

Key Points

  • 1.Samsung negotiating HBM4 at ~$700/unit, 20-30% above HBM3E
  • 2.SK Hynix set Nvidia HBM4 at $550 last August, may increase
  • 3.Demand surge driving price hikes for next-gen AI memory
  • 4.SK Hynix starting HBM4 mass production soon
  • 5.Q1 profits forecasted at 30 trillion KRW for both Samsung and SK Hynix

Impact Analysis

Rising HBM4 prices signal strong AI hardware demand, potentially increasing costs for GPU makers like Nvidia and raising barriers for AI training/inference scaling. This could squeeze margins for AI infrastructure builders amid booming needs.

Technical Details

HBM4 offers higher bandwidth than HBM3E for AI accelerators. Price reflects advanced manufacturing and yield challenges in mass production.

📰

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Original source: 36氪

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