Man Made Diamonds: The Coolest Material for Red Hot AI Chips

Introduction

AI accelerators are getting hotter–literally.

As NVIDIA, AMD, and other vendors push GPUs into the 700–1,000 W range, traditional copper cooling struggles to keep up. That’s where man made diamonds (lab grown, CVD diamond) step in. With record-breaking thermal conductivity, diamond heat spreaders are emerging as one of the most promising solutions for AI chip cooling and sustainable data center operations.

Why AI Chips Run Hotter

Unlike CPUs, which are optimized for latency and serial tasks, AI accelerators are built for throughput. Thousands of cores plus specialized tensor and matrix engines devour power as they process enormous parallel workloads.

High‑Bandwidth Memory (HBM) stacked near the die to feed those cores with terabytes‑per‑second of bandwidth–orders of magnitude beyond commodity DDR. Architecturally, this shifts the bottleneck from compute to power density and heat removal.

• NVIDIA’s H100 (Hopper) lists up to 700 W TDP for the SXM module; AMD’s MI300X lists 750 W TBP. Both rely on HBM3 with multi‑TB/s bandwidth–wonderful for training, unforgiving for thermals.
• The next wave (NVIDIA Blackwell B200) is designed for 1,000 W TDP parts in 8‑GPU HGX systems, forcing servers and facilities to re‑think cooling envelopes.
• Meanwhile, advanced packaging (TSMC 3DFabric, Intel Foveros) stacks silicon vertically and brings memory on‑package, increasing hot‑spot heat flux near the die. That makes the “near‑junction” thermal path (microns from transistor to spreader) mission‑critical.

Bottom line: AI chips aren’t just “faster CPUs.” They’re different machines with different physics–and their thermal design drives performance, reliability, and cost.

The Power and Heat Reality

In 2025, single AI servers often consume multiple kilowatts, with racks aggregating to hundreds of kilowatts. Cooling them with just air is impossible; operators are rapidly shifting to direct liquid cooling (DLC), two-phase cooling, and immersion systems.

But as AI demand surges, so does electricity consumption. The IEA projects data center power use could more than double by 2030, driven largely by AI. Utilities are struggling to keep up, making every watt saved critical–not just for performance but also for grid stability and sustainability.

Copper vs. Diamond Thermal Conductivity

Copper has long been the workhorse of chip cooling, with ~400 W/m·K thermal conductivity. But CVD diamond is in a different league: ~1,700–2,000 W/m·K, with isotopically pure diamond exceeding 3,000 W/m·K. That’s up to 5× better heat conduction.

Unlike graphite or graphene, which are anisotropic, diamond conducts heat equally well in all directions. This isotropy makes diamond uniquely effective for pulling heat vertically from the die into heat spreaders or cold plates. Studies show diamond can lower hot spot temperatures by 10–20°C compared to copper.

Liquid Cooling + Diamond: Better Together

Liquid cooling is already essential for AI chips, but even DLC has limitations. Coolant flow rates, pressure drops, and interface resistances all create bottlenecks. By adding a diamond heat spreader at the near-junction level, operators can combine the macro benefits of liquid cooling with the micro-level efficiency of diamond.

The diamond layer reduces local hot spots before heat even reaches the cold plate, improving pump efficiency and lowering overall facility cooling energy. In other words, DLC handles the bulk load while diamond optimizes the thermal path at the chip itself–a one-two punch against runaway temperatures.

This synergy has major implications for sustainable AI growth. Facilities that integrate diamond into AI cooling stacks could reduce power usage effectiveness (PUE), cut fan and pump energy, and even extend component life by reducing thermal cycling. For hyperscale operators fighting rising electricity costs and environmental impact, that combination is compelling.

Where Diamonds Are Used Today

1. Packaged heat spreaders – Element Six and others supply metallized CVD diamond heat spreaders for high power electronics.
2. AI servers – Akash Systems has launched “Diamond Cooled” servers, reporting 10–20°C GPU temperature reductions even alongside DLC.
3. GaN on diamond & near-junction research – Academic teams are developing GaN-on-diamond structures and wafer-level diamond integration to improve heat removal in HBM-rich 3D stacks.
4. Diamond composites and TIMs – Diamond-metal composites help with thermal expansion, while diamond-filled thermal interface materials lower boundary resistance.

While mainstream GPUs like H100, MI300X, and B200 don’t yet list diamond in their bills of materials, integrators are already embedding diamond layers in advanced thermal stacks. This approach adds cooling headroom without waiting for new chip packaging standards.

Environmental Impact of AI Cooling

AI’s carbon footprint is rising quickly, with cooling responsible for a large fraction of data center energy. By flattening hot spots and lowering junction temperatures, diamond reduces the need for aggressive liquid cooling, lowering pump and fan loads. Combined with facility strategies like heat reuse (e.g., Microsoft’s Denmark projects) and renewable pairing, diamond can help AI infrastructure scale without pushing grids past their limits.

What’s Next for Cooling Materials

Man made diamonds aren’t the only material innovation on the horizon. GaN-on-diamond structures promise even higher efficiency for power devices. Metal-diamond composites could improve reliability by balancing conductivity with thermal expansion. And advanced TIMs (thermal interface materials) with engineered nanostructures aim to further cut resistance between die and spreader. As packaging technologies like TSMC 3DFabric and Intel Foveros pack more logic and HBM3 into smaller footprints, these material advances will be essential.

Practical Guidance for Evaluating Diamond

If you’re deploying GPUs at 700–1,000 W and hitting thermal or energy efficiency walls, diamond may be worth exploring. Ask vendors about:

• Metallization compatibility with your thermal stack.
• Verified thermal boundary resistance (TBR) numbers.
• System-level ΔT results compared to copper.
• Long-term reliability tests (cycling, delamination).
• Sustainability impacts (energy and carbon savings).

FAQ