The Memory Problem Is Geometry: Ventiva on Cooling for Local AI

As AI hardware evolves, bringing memory closer to compute has become a critical design priority. Carl Schlachte, chairman, president and CEO of Ventiva, calls it the investment thesis of the decade. But it requires rethinking the physical space occupied by conventional cooling systems.

Carl returned to In the Arena with TechArena’s Allyson Klein at the 2026 AI Infra Summit, following a conversation last year, when he previewed a significant demonstration planned for CES 2026.

Ventiva develops thermal management technology for applications ranging from consumer electronics to data centers. Its solid-state airflow technology offers a highly modular cooling solution, creating new opportunities for component placement and system design.

Rethinking Laptop Cooling

Conventional laptop motherboards typically accommodate two fans measuring approximately 55 millimeters in diameter, with semicircular cutouts on either side of the board. This configuration consumes valuable space that could otherwise support memory, connectivity, and other components.

At CES 2026, Ventiva demonstrated an alternative architecture featuring a rectangular motherboard with cooling concentrated toward the back. The design delivered comparable thermal performance to a conventional fan-cooled laptop while operating silently, with no moving parts.  

The improvement extended beyond acoustics and thermal performance: eliminating the fan cutouts also freed motherboard space for additional components. For Ventiva, this represented a shift from solving a thermal management challenge to enabling new possibilities in system architecture.

Generating Airflow Without Moving Parts

Ventiva uses electrohydrodynamic (EHD) flow to generate airflow without mechanical fans. Its compact rectangular ionic cooling modules recover the physical board space used by conventional fans. This gives designers the ability to add more memory, storage, battery capacity, or other new components. For local AI, that flexibility helps address a critical design constraint: finding room for more memory closer to the processor.

Why Local AI Depends on Memory Proximity

The growing importance of inference and agentic AI is changing how system architects think about memory capacity, bandwidth, and physical placement. Carl argued that many major advances in AI infrastructure, including optical interconnects and new communication architectures, address the same underlying challenge: reducing the distance and communication overhead between memory and compute.

For local inference, that constraint becomes especially significant. According to Carl, memory supporting effective on-device AI needs to sit approximately 15 to 25 millimeters from the processor complex. Conventional laptop fans compete for that space, particularly as CPU and GPU packages become larger and more power-intensive.

But processor specifications alone don't tell the full story. A laptop may offer substantial AI compute capability, measured in trillions of operations per second (TOPS), while its practical inference performance also depends on memory capacity, bandwidth, and how efficiently it can access model data.  

Ventiva’s ultra-thin modular cooling solution creates room to position additional memory closer to the processor, potentially supporting larger models without increasing the device's external dimensions.

Carl sees this as an opportunity to run more AI workloads locally rather than relying exclusively on centralized infrastructure: “You can build a data center $3,000 at a time if you did it the right way.”

The idea is to equip individual machines with sufficient compute and memory to run appropriately sized language models. Rather than sending every inference request to the cloud, users could execute suitable workloads directly on their devices, reserving remote resources for more demanding tasks.

Ventiva doesn't replace established thermal technologies such as vapor chambers or heat pipes. Its solid-state airflow works alongside them, potentially improving heat removal while freeing space for other components.

The economics could be equally significant. Local inference can reduce recurring cloud computing costs by using hardware already deployed to end users. As more workloads become practical on client devices, organizations may be able to handle routine AI tasks without paying for remote compute on every request.

From Thermal Component to Platform Architecture

Carl described a broader change in how Ventiva engages with laptop manufacturers. Rather than discussing cooling as an isolated subsystem, the company is increasingly involved in conversations about motherboard layout and overall platform design.

A rectangular motherboard can simplify manufacturing by eliminating large cutouts associated with conventional fan assemblies. It may also allow more direct routing between I/O components and the processor, reducing the need for signal repeaters and potentially lowering system costs.

By changing where cooling hardware sits, manufacturers gain greater flexibility in how they allocate space, route connections, and integrate memory.

Carl also expects local AI capabilities to influence laptop purchasing and replacement cycles. As devices handle more inference workloads, buyers may place greater value on performance and longevity. This could challenge traditional three-year refresh cycles and the role of laptops as terminals for cloud-based applications.

The external form factor may remain familiar. Carl expects the clamshell design to persist, with more substantial changes occurring inside the chassis.

Addressing the Thermal Orphans in AI Servers

Ventiva’s solid-state cooling technology also has applications in data centers. As AI server power densities increase, CPUs, GPUs, and high-bandwidth memory increasingly rely on cold plates and direct-to-chip liquid cooling. This transition reduces the need for conventional fans around high-power processors, but creates challenges for components that still depend on airflow.

In traditional server architectures, air moves from the front of the chassis toward the rear, absorbing heat as it passes through the system. Reducing fan capacity can leave downstream components with both less airflow and higher inlet temperatures. Carl calls these components thermal orphans: boot-optimized storage, SSDs, and other devices toward the rear of the server that may not justify dedicated liquid cooling but still require effective thermal management.

Failures in these components can affect the availability of the entire system. Carl noted that even a 5% failure rate in rear-mounted storage can undermine overall server availability, regardless of the performance of the GPUs at the front.  

Ventiva’s approach delivers localized airflow directly to these underserved devices without requiring major changes to existing hardware designs. Form factor is particularly important, as manufacturers of rear-mounted cards are reluctant to redesign established mechanical configurations. A cooling solution that fits within existing dimensions and delivers a 5% to 10% improvement could offer a compelling advantage.

In the server, the goal is to supplement liquid cooling, not replace it. Targeted solid-state ionic cooling modules can operate alongside cold plates, addressing thermal requirements that direct-to-chip systems do not cover.

Carl described initial skepticism from thermal engineers accustomed to designing around mechanical fans, but once they saw airflow delivered into previously impractical locations, the discussion shifted toward additional applications.

Bringing Solid-State Cooling to the Data Center

Ventiva's data center business is focused on targeted improvements rather than replacing established cooling infrastructure. Carl identified rear-of-rack components as a practical entry point, since these devices can often be replaced independently of broader server or facility upgrade cycles. This creates an opportunity to improve thermal performance without waiting for the next platform generation.

A replacement card that operates more reliably or delivers better performance within the same mechanical envelope could justify a price premium, particularly when component failures threaten server availability. For Ventiva, this offers a potentially faster path to adoption than integration into new server architectures requiring multiyear design and qualification cycles.

The TechArena Take

Ventiva's technology demonstrates how thermal management decisions increasingly influence AI system architecture.

Using ultra-thin cooling modules in laptops can free motherboard space for memory closer to the processor, addressing a critical constraint on local inference. In data centers, targeted solid-state airflow can support components that remain dependent on air even as high-power processors transition to liquid cooling.

The broader opportunity is to give system designers greater freedom to allocate space according to computational requirements rather than the dimensions of conventional cooling hardware.

The implications for local AI are particularly significant. As more inference and agentic workloads become viable on client devices, AI infrastructure could become increasingly distributed across enterprise laptops and other endpoints.

Realizing that potential requires manufacturers to consider motherboard layouts, memory integration, and thermal architecture together. Ventiva's ongoing discussions with OEMs suggest that this design conversation is already underway.

Listen to the full In the Arena episode featuring Carl Schlachte.