Small Models.
Big Intelligence.

Rax AI was born from a bold question: why does AI need to be so big? While the industry races to build trillion-parameter models requiring massive data centers, we're focused on hardware-level efficiency — proving that intelligence doesn't require size.

Instead of relying on brute-force compression that guts accuracy, we invest in compiler-level engineering. Rax AI compiles models down to native machine code instead of running through heavy Python inference stacks, which is where most of the latency in typical AI deployments comes from. We pair that with 2-bit and 4-bit quantization and dynamic sparsity — skipping computation the model doesn't need for a given input — to cut memory and compute cost while keeping accuracy close to full precision. The result runs efficiently in the cloud or fully offline on local hardware, so sensitive data never has to leave your network.

To guarantee high availability and scale, we run cloud infrastructure on AWS across North America, Europe, and Asia, alongside an on-premises server node in Nakuru, Kenya. That local node keeps data processing and infrastructure spend within the region instead of routing everything through foreign cloud providers, while also giving us very low latency for users nearby.

We're also committed to cultivating local talent. In partnership with Kisii University, we train students in advanced compiler engineering, helping build Kenya's next generation of systems-level engineers. Whether you're a student or an enterprise, Rax AI delivers state-of-the-art performance without the compute bloat.