🧠📶 The Next Era of Accelerated Computing: HBM4–HBM8, Liquid Cooling, and the Future of Thermal-Aware GPU Architectures

Inspired by an original article by: Korea Advanced Institute of Science and Technology & Tera (Terabyte Interconnection and Package Laboratory)

Summary by: Nick Florous, Ph.D.

The future of high-performance computing is not being defined by clock speeds or Moore’s Law—it’s being rewritten through radical advances in memory packaging, thermal design, and co-packaged interconnects. The Korea Advanced Institute of Science & Technology (KAIST), in collaboration with Tera Laboratory, has unveiled a comprehensive roadmap spanning HBM4 to HBM8, revealing the contours of our next decade of computing.

This evolution is not merely incremental. It is paradigmatic, converging silicon scaling limits with AI’s insatiable memory demands—and it's ushering in an era of thermally aware, structurally integrated, and liquid-cooled chip architectures. Below is an executive summary of what’s coming—and why it matters.


📊 I. Memory Bandwidth, Capacity, and Thermal Density Are Scaling Nonlinearly

🚀 Highlights Across the HBM4–HBM8 Roadmap:

GenBandwidth/StackStack HeightMax Capacity/StackCoolingLaunchHBM42.0–2.5 TB/s12–16-Hi48 GBD2C

💧2026HBM54.0 TB/s16-Hi80 GBImmersion

🫧2029HBM68.0 TB/s16–20-Hi120 GBImmersion

💧2032HBM724.0 TB/s20–24-Hi192 GBEmbedded

🧊~2036HBM864.0 TB/s24-Hi240 GBEmbedded 🧊~2038

Each generation sees exponential growth in memory bandwidth, capacity, and power density—requiring revolutionary cooling and packaging approaches.


Thermal Management & Cooling Methods for Next-Gen HBM [Source: Terabyte Interconnection & Package Laboratory]

🔬 II. Direct-to-Chip Cooling Becomes Industry Standard

❄️ Liquid Cooling: The End of Cold Air?

KAIST and Tera Laboratory confirm what many in hyperscale and HPC have predicted:

Direct-to-Chip Liquid Cooling (D2C) becomes mandatory by 2026

Cold air cooling becomes obsolete—unsustainable for >800W dies

Immersion cooling finds niche adoption in modular deployments

Embedded cooling becomes essential from HBM6 onward

We are entering a thermal-first architecture design paradigm, where cooling is no longer peripheral but embedded directly into the chip package, interposer, and memory stack.


Technical trends & Roadmaps of AI-based HBM in AI Industry [Source: Terabyte Interconnection & Package Laboratory]

🧠 III. Next-Gen GPU Packages: Power, Density & Integration

📐 NVIDIA Rubin (HBM4)

  • 8–16 HBM sites, 384 GB memory

  • 728 mm² die, 2200W total package power

  • Cold plate D2C liquid cooling standard

  • Target: MI400, Rubin Ultra GPUs (2026)

🔋 NVIDIA Feynman (HBM5)

  • 8 HBM5 sites, 400–500 GB memory

  • 750 mm² die, 4400W TDP

  • Immersion cooling with decoupling capacitor die stacks

  • Projected launch: 2029

🧊 Post-Feynman (HBM6)

  • 16 HBM6 sites, up to 1920 GB VRAM

  • Bandwidth: up to 256 TB/s

  • Power: up to 5920W per GPU package

  • Cooling: Multi-tower immersion with hybrid interposers

These power densities are orders of magnitude beyond what conventional air-cooled systems can manage—cementing D2C and immersion as permanent industry fixtures.


Next Generation HBM Roadmap [Source: KAST Terabyte Interconnection & Package Laboratory]

🧩 IV. Packaging Innovations & Interposer Architecture

📦 Microbump (MR-MUF) → Standard through HBM5 🔩 Bump-less Cu–Cu Direct Bonding → HBM6–HBM8 🪟 Glass & Silicon Interposers → Hybrid usage from 2032 🔌 Coaxial TSVs and full 3D HBM–GPU stacking → HBM8 ⚙️ Embedded network switches and bridge dies → Integral to memory routing in multi-GPU architectures

This signals a fundamental transition: memory and compute are no longer separate systems but are co-architected in monolithic, thermally integrated designs.


🧠 V. HBF & LLM Memory Architectures

🌐 KAIST also introduces High-Bandwidth Flash (HBF): a NAND-based companion memory optimized for large language model inference and memory-intensive AI workloads.

  • Up to 1 TB of NAND flash per stack

  • HBF–HBM bridging via TSV interconnects

  • Bidirectional 128 GB/s links across mainboard

  • Up to 6144 GB of hybrid memory per GPU in HBM8-class packages

This design radically shifts how memory hierarchies are conceived, merging DRAM + Flash + LPDDR + CXL into an extensible, high-throughput fabric.


🧭 VI. Strategic Industry Implications

For Semiconductor Leaders:

  • Memory vendors must co-innovate with packaging and cooling firms

  • Interposer and TSV engineering becomes a front-line innovation domain

For Data Center Architects:

  • Thermal budgets and cooling constraints will define rack density

  • Cold plate and immersion infrastructures will dominate new builds

For Investors:

  • Liquid cooling, interposer manufacturing, and thermal simulation tools will see exponential value

  • Companies building thermal-aware, vertically integrated stacks stand to win


💡 Final Thought: From Chips to Systems

The future isn’t just faster—it’s denser, hotter, and more integrated. Thermal design will dictate compute architecture. Bandwidth will scale through stack height, and packaging will become the new frontier of innovation.

Those who think like system architects—not just chip designers—will define the next decade of computing.


#HBM #LiquidCooling #GPUArchitecture #DataCenter #AIAcceleration #MemoryBandwidth #ThermalEngineering #SemiconductorStrategy #HBM4 #HBM5 #HBM6 #HBM7 #HBM8 #NVIDIA #AMD #Interposers #TSV #Innovation #KAIST #TeraLab

📈🧊🔬⚙️💧🧠🌐📦🧪🚀

LS Wu

Semiconductor, Communication, Medical, AI, on Venture, and Operation

3mo

thanks

Like
Reply

To view or add a comment, sign in

Others also viewed

Explore content categories