Why shared memory matters
Accelerator memory limits most local AI work before raw compute does. This architecture expands practical model and context options in a portable footprint.
Portable edge reference system
A real local AI system that travels to the work.
R.A.I.S.E. runs today on an ASUS ROG Flow Z13-class platform with AMD Ryzen AI Max+ 395 and 64GB of shared memory: our portable development, demonstration, and edge reference system.
Active reference system · not the platform ceilingRyzen AIMax+ 395
64GBShared memory
PortableLab + field
OneDeployment profile
The point
The Z13 proves portability. The workload determines production infrastructure.
Shared memory makes meaningful local model experimentation possible in a compact system. That helps with development, controlled demonstrations, field discovery, and some edge workloads. It does not make one device the answer for every client or model.
Why portability matters
The system supports founder-led demonstrations, on-site discovery, and edge experiments without moving source material into a cloud service.
What it proves
The active platform demonstrates a working local R.A.I.S.E. pipeline and helps measure model, memory, latency, thermal, and workflow tradeoffs.
What comes next
Production hardware can step up to workstations, secure servers, multi-GPU systems, or clusters, sized by model, concurrency, availability, facility, and budget.
Work with us
See the edge reference system, then size the real deployment.
A technical conversation can include a controlled local demonstration and an infrastructure discussion grounded in your actual workload.