Architecture

What it takes to run Senua AI.

Senua AI runs locally: on premise, on the consumer hardware you already own, and all the way down to edge devices and wearable-class silicon. No cloud dependency, no GPU, no model too big to carry. This page is the proof: what it compiles to, what it needs, and what it costs to run: measured on the current build, checkable against our datasheets and validation notes. We don’t publish what’s inside (that’s patent-pending and stays ours) : and if these numbers look impossible for a neural network, that’s the point: it isn’t one.

Measured footprint

The numbers, as built.

Taken from the current development build: not projections.

9.3 MB

The entire cognition engine (reasoning, learning, memory, language) compiled to ONE native binary. The web console adds 0.5 MB; the CLI 0.2 MB. A full install is under 15 MB.

One binary, no runtime

Disk = the learned store

An Anima’s footprint matches what it has learned: there is no fixed model file. As a guide only: the current 1.6M-fact reference Anima is 2.2 GB on disk. Footprint scales with the corpus you feed it, and nothing else.

Guide: 1.6M facts ≈ 2.2 GB

4 vCPU

The cloud reference (cloud-1): ONE general-purpose node: 4 vCPU, 16 GB RAM, 500 GB disk : runs the full platform and is staging a 117 GB raw public corpus (866M facts) toward a large-scale Anima.

m7i.xlarge class · guide only

0 GPUs

No GPU, no accelerator, and no floating-point unit required: the arithmetic is integer-only end to end. No linear algebra, no per-token cloud bill.

CPU-native

2 vCPU arm64

The autonomous-flight reference: full missions flown from a 2-vCPU arm64 node (AWS Graviton). The onboard target is a quad-core Cortex-A53 with 512 MB RAM (Pi Zero 2 W class) inside a 1.3 W fly-and-monitor battery envelope: the one running metric that matters at the edge.

Battery envelope: see DS-001

2 architectures

Native machine code for x86_64 and arm64: the arm64 port is hardware-validated on AWS Graviton. macOS, Linux, and bare metal via SenuaOS.

x86_64 + arm64

Baseline hardware

Yours, not ours: the hardware ladder.

The floor is lower than you think, and every rung is hardware you own. Wearable-class silicon: a quad-core arm64 chip with 512 MB of RAM (Raspberry Pi Zero 2 W class: the same silicon class as today’s wearables) runs a device leaf, flying and monitoring a drone inside a 1.3 W power envelope. Consumer hardware: an ordinary laptop or a Pi CM5-class module runs the full assistant, console and all: private, offline, no account required. On premise: one commodity server puts the whole platform inside your firewall, air-gapped if you need it. Cloud, if you choose it: a single 4-vCPU general-purpose node serves the complete platform and builds the Anima. No cluster, no accelerator fleet, no special hardware anywhere in the stack: local is not the compromise; it is the design.

Proving grounds

Tested where you can check it.

The platform is exercised on AWS test harnesses that mirror real deployments: an arm64 Graviton flight rig where Senua AI flies full autonomous missions against a real autopilot with QGroundControl recording the ground truth; power and battery simulations cross-implemented in three independent code stacks with byte-identical outputs; and live host-metrics monitors across macOS, x86_64 Linux, and arm64 Linux. The results are published below as they land.

Want the envelope for your hardware?

Tell us the board, the box, or the instance type: we’ll tell you what fits. The business case is over at Why Senua.

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