Built Around the Inference Problem

Lower-Cost Clusters

VespeneAI clusters minimize capital expenditure where it makes sense. By optimizing hardware, memory capacity, and bandwidth around the actual requirements of inference, we can pursue significantly lower infrastructure costs while maintaining high performance.

Low cost also does not mean we sacrifice scalability. Our cluster is designed for high density and easy scalability. Our clusters are also designed to run in low cost facilities to reduce TCO even further.

Our focus is on maximizing useful inference performance per dollar invested, with cost optimization built into every level of the inference platform down to the hardware.

KV Cache and Context Access

We're developing a custom KV-cache system designed for inference superclusters, with tight integration between our hardware and runtime.

This allows us to optimize context access and bandwidth utilization across the cluster while minimizing interference with model execution.

The Evolution of Frontier Models

Frontier models are evolving toward larger contexts, larger models, and increasing sparsity. These developments are changing the balance between compute, memory, and bandwidth.

We're building around memory and compute disaggregation to capitalize on the next wave of hardware and model advancements, such as more efficient context usage and silicon photonics.

Runtime and Control Plane

Our runtime and control plane are being developed specifically for inference at supercluster scale. By coordinating specialized cluster topology, innovative context management, and resource allocation across both hardware and software, we can pursue optimizations that become increasingly effective as the cluster grows.

This is the foundation of our approach to superlinear scaling, combining architectural efficiency with software designed to extract more performance from the underlying hardware.

The Economics of Scale

Lower cluster capital costs give us a stronger foundation for competitive inference pricing. We expect lower inference costs to unlock substantial new demand. Our architecture is designed to capitalize on that demand by improving efficiency as the platform scales.

We intend to translate those improvements into increasingly competitive pricing, creating a cycle of lower costs, greater consumption, and continued expansion.