Alloy Enterprises’ Metal Stacks Rethink AI Server Cooling to Break Energy and Scaling Constraints
Alloy Enterprises has introduced a new additive manufacturing technique for creating metal stacks that dramatically enhance liquid cooling in AI data centers as of late 2025. This approach extends liquid cooling beyond traditional locations, embedding it deeper into server racks where AI training hardware faces critical heat dissipation challenges. The company’s patented metal stacks aim to reduce cooling infrastructure bottlenecks that have capped AI workload scaling and driven soaring energy costs.
Why Traditional Data Center Cooling Caps AI Scaling
AI model training pushes GPUs and specialized chips to sustained peak performance, generating 250-300 watts of heat per chip under load. Conventional cooling strategies rely on airflow and external liquid cooling loops focused at the chassis edges or heat sinks. These methods struggle to remove heat effectively once power density surpasses roughly 30 kW per rack. For instance, Nvidia’s recent AI-supercharged DGX systems still rely heavily on chilled water fed to the rack edges, capped by pump capacity and tubing complexity.
This limitation creates a hard constraint: excess heat leads to throttling or expensive overprovisioning of cooling infrastructure that drives energy costs beyond the $0.10-0.15 per kWh baseline. Overcoming this has typically required costly retrofits or abandoning liquid cooling for immersion tanks, which demands wholesale redesign of data center workflows.
How Alloy’s Metal Stacks Break Through Heat Dissipation Barriers
Instead of placing cooling systems around existing server components, Alloy Enterprises uses metal additive manufacturing to produce integrated cooling stacks inside the server racks themselves. Their process builds microscopic channels within metal layers that are assembled vertically to create a high surface-area interface directly in contact with heat sources. This allows liquid coolant to flow through parts of the rack previously inaccessible to standard piping.
These metal stacks improve cooling efficiency by targeting heat at its generation point rather than merely managing ambient air temperature or external chilled water supply. Early tests show a 30-40% improvement in heat removal per watt of power density compared to leading-edge conventional liquid cooling systems, enabling racks to sustain up to 45 kW without throttling. This expands AI workload density by at least 50% without requiring additional energy-intensive cooling infrastructure.
Changing the Constraint from Cooling Infrastructure to Compute Density
The critical leverage shift Alloy creates is moving the bottleneck from traditional cooling systems—which require complex plumbing, redundant pumps, and costly chillers—to the metal stack itself, which is a solid-state, passive part of the server rack. Because these stacks are additive manufactured, they can be mass-produced rapidly and tailored to server designs, enabling OEMs and hyperscalers to integrate liquid cooling without massive facility upgrades.
By embedding cooling channels inside the metal structure, Alloy reduces dependency on moving parts and external plumbing, which typically fail or require frequent maintenance—one of the largest ongoing OpEx costs in data centers. This means once installed, these metal stacks operate without active intervention, a rare characteristic in data center thermal management.
Positioning Against Alternatives: Immersion and Traditional Cooling
Immersion cooling—submerging servers in dielectric liquids—is an alternative that some AI data centers pursue. However, immersion requires re-architecting server components, complicates hardware upgrades, and has safety and leakage risks. It also demands new facility designs which can cost $50-100 million for large datacenters.
Alloy Enterprises’ approach avoids these trade-offs by keeping standard server designs intact while embedding cooling channels directly. This maintains compatibility with current server procurement and upgrade cycles. Traditional liquid cooling systems rely on expensive copper piping and complex manifold designs that can cost up to $10,000 per rack and require teams of engineers to maintain.
Because Alloy’s metal stacks are additive manufactured, scaling production comes down to print time and raw metal filament costs, which are dropping rapidly across industries. This reduces cost per rack cooling infrastructure by an estimated 20-30% compared to copper tube retrofits, allowing both startups and hyperscalers to leverage improved cooling without major CAPEX spikes. It also opens new form factors in server design.
The Energy and Scale Impact for AI Operators
Data centers run on tight energy budgets, and cooling can account for up to 40% of total facility energy consumption. By increasing cooling efficiency by about 35%, Alloy’s technology could cut energy expenses by millions annually for hyperscale AI training hubs.
This unlocks a feedback loop: better cooling means operators can pack 50% more GPUs per rack, increasing compute capacity without needing new data halls or doubling energy bills. At scale, this could reduce marginal training cost per AI model from $X to $0.6X, shifting the economics of AI model experimentation and deployment. It also mitigates the risk of hitting physical infrastructure ceilings—a growing problem covered in our look at Lambda’s specialized AI hardware partnerships.
Implications Beyond AI: Leveraging Additive Manufacturing for Systemic Efficiency
Alloy’s use of additive manufacturing to embed liquid cooling changes system design from a patchwork of add-on parts to a structural, built-in feature. This mirrors trends seen in automotive supply chain redesigns, where embedding capabilities directly into core components simplifies downstream complexity and reduces maintenance overhead.
This approach points to a broader leverage principle: replacing distributed, failure-prone subsystems with integrated, passive structures that scale with production volume and improve durability without adding ongoing operational burden.
Extending This Insight to AI Infrastructure Strategy
For AI cloud providers and enterprises, Alloy’s metal stacks signal a shift in how to approach the costly thermal scaling bottleneck. Instead of squeezing incremental gains from existing cooling loops or accepting throttled compute, operators can rethink server thermal design as a scalable manufacturing problem. This could reshape vendor relationships and capital allocations—firms that adopt this technology early might reduce dependency on familiar but limited cooling suppliers.
Compare this to the cloud security embedded defense shift seen in Google’s Wiz acquisition, where embedding capabilities directly into core systems changed maintenance and upgrade cycles fundamentally. Alloy is replicating this kind of structural repositioning for heat management in servers.
Frequently Asked Questions
How does liquid cooling improve AI data center performance?
Liquid cooling efficiently removes heat directly at the source, allowing AI server racks to sustain higher power densities up to 45 kW without throttling. This leads to a 30-40% improvement in heat removal compared to traditional cooling, supporting denser AI workloads and reducing energy costs.
What are the limitations of traditional data center cooling methods?
Traditional cooling relies on airflow and chilled water at rack edges, which struggles above 30 kW power density per rack. This causes throttling or expensive overprovisioning, pushing energy costs beyond $0.10-0.15 per kWh and limiting AI workload scaling.
How do additive manufacturing metal stacks enhance server cooling?
They embed microscopic liquid cooling channels inside metal stacks within server racks, allowing coolant to reach heat sources directly. This reduces reliance on external plumbing and pumps, increases cooling efficiency by up to 40%, and enables racks to handle up to 45 kW without throttling.
What are the energy cost benefits of improved cooling technology for AI operators?
By improving cooling efficiency by around 35%, energy expenses for hyperscale AI centers can be reduced by millions annually. Enhanced cooling also allows packing 50% more GPUs per rack without extra facility expansion or doubling energy bills.
How does Alloy's cooling solution compare cost-wise to traditional copper piping retrofits?
Alloy's additive manufactured metal stacks reduce cost per rack cooling infrastructure by an estimated 20-30% compared to traditional copper tube retrofits, which can cost up to $10,000 per rack and require extensive maintenance.
What are the challenges of immersion cooling compared to embedded liquid cooling?
Immersion cooling requires re-architecting servers, has safety and leakage risks, and demands costly new facility designs costing $50-100 million for large data centers. Embedded liquid cooling preserves standard server designs and easier upgrades.
Why is shifting the cooling bottleneck important for AI infrastructure scalability?
Moving the bottleneck from external liquid cooling systems to embedded metal stacks allows passive, mass-producible cooling that scales with server designs. This supports higher compute densities without complex plumbing or costly infrastructure upgrades.
How does integrating cooling channels inside server racks affect maintenance costs?
Embedding cooling inside metal stacks reduces dependency on moving parts and external plumbing that frequently fail or need maintenance, lowering ongoing operational expenses significantly for data centers.