Exa Compute.
Exa Compute orchestrates AI workloads across distributed infrastructure — optimizing compute in real time for performance, cost, and energy.
Scope
Year
We make intelligent compute orchestration sovereign, efficient, and accessible — from data centers to the edge.
At Exa Compute, we are building the coordination engine that optimizes how compute is allocated, scaled, and monetized — from sovereign data centers to edge devices. We combine deep reinforcement learning, real-time telemetry, and multi-agent systems to route workloads across fragmented infrastructure with precision and adaptability.
A Multi-Layered Learning Engine
Exa Compute uses a hybrid of value-based and policy-gradient reinforcement learning models to dynamically schedule tasks. Unlike static heuristics, our system continuously learns from live execution data — latency, power, memory — to make forward-looking decisions about how and where to deploy compute.
Inspired by hierarchical scheduling frameworks like EdgeTimer, Exa Compute operates across multiple timescales: long-term controllers manage macro-level resource allocation across data centers, while lightweight agents dispatch tasks at the edge in real time. This enables seamless coordination across centralized, distributed, and offline-first environments.
Designed for Sovereignty and Scalability
Our architecture is built for the realities of emerging infrastructure — sovereign, heterogeneous, and often resource-constrained. Whether across regional data centers, contributor nodes, or mobile edge devices, Exa Compute uses graph-based, multi-agent reinforcement learning to optimize for energy efficiency, throughput, and resilience. Over time, it learns network topology, task priority, and infrastructure constraints — adjusting in real time to changing environments.
Optimized for SLMs. Scalable for LLMs.
Exa Compute is designed to serve the evolving needs of AI-native infrastructure. It supports small language models (SLMs) trained on localized, domain-specific datasets — allowing for private, efficient inference directly at the edge. Simultaneously, it scales LLMs and multimodal models intelligently, allocating GPU capacity only where necessary. This dual optimization reduces cost, increases uptime, and makes advanced inference accessible across environments — from rural clinics to enterprise clusters.
Towards a General Scheduling Intelligence
Our long-term vision is a general-purpose, agentic scheduler — one that understands infrastructure constraints, workload semantics, and user intent. Exa Compute is the first step toward that vision: an adaptive, sovereign-first orchestration layer that transforms idle infrastructure into high-leverage opportunity.
Our portfolio of ventures designed to layer, and compliment each other to create impact, innovation, and sustainability.
