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embodiOS

EMBODIOS

The AI IS the Kernel

The world's first bare-metal AI operating system. No Linux. No abstractions. Just pure silicon-to-synapse intelligence.
View on GitHub
Boot Time

0s

Cold boot to AI-ready state

Inference Latency

0ms

Token generation overhead

Memory Overhead

0%

vs traditional OS stack

Code Size

0KB

Minimal kernel footprint

THE IMPACT

Why It Matters

Linux was built for humans using computers. EMBODIOS was built for AI controlling hardware. Every abstraction layer we removed is a millisecond saved in real-time control.

47ms

vs 5 minutes
Boot Time

From power-on to AI-ready. NVIDIA Jetson takes 5 minutes. We take a heartbeat.

6,383x faster

±0.5ms

vs ±20ms
Inference Jitter

Deterministic timing for safety-critical applications. No surprises, no variance.

40x more consistent

<100MB

vs 2-4GB
OS Overhead

More RAM for your models, less for operating system bloat.

20-40x less overhead

<50KB

fits in L2 cache
Kernel Size

The entire kernel fits in CPU cache. Zero cold misses during inference.

Cache-resident
Why Not Just Patch Linux?

“Patching Linux for AI is like patching DOS for the internet. Linux thinks in processes and files. AI thinks in tensors and memory hierarchies. You can't syscall your way to efficient inference — the abstraction layer is wrong, not just slow.”

SEE IT IN ACTION

Interactive Demos

Experience the speed difference yourself. Watch real-time inference and hardware control.

Simulated demos for illustration

hand-control-demo

Cloud AI

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Cloud API latency

Ollama

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Local inference

Embodios

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Bare-metal kernel
COMMAND LOG

Waiting for commands...

Same command • Same hardware • Different latencySame command • Different latency
CAPABILITIES

Built for AI, Not Adapted

Every component designed from the ground up for machine learning workloads. No legacy baggage. No unnecessary layers.

Zero-Copy Inference

DMA transfers data directly to model tensors. No kernel copies, no user-space transitions, no wasted cycles.

Sub-millisecond Latency

Deterministic scheduling ensures inference requests are processed with minimal jitter and maximum throughput.

Ollama/GGUF Compatible

Run the same quantized models you use locally. Full GGUF format support with llama.cpp compatible inference.

Multi-Architecture

Native support for x86_64 and ARM64. From workstations to Raspberry Pi to data center GPUs.

Real-time Processing

Priority-based scheduling with interrupt handling designed for continuous inference workloads.

Minimal Footprint

The entire kernel fits in L2 cache. More memory for models, less for operating system bloat.

USE CASES

Where We Deploy

EMBODIOS powers the next generation of intelligent devices across industries, from healthcare to industrial automation.

Robotics

Industrial robots, collaborative robots (cobots), and humanoid robots with real-time AI control and sub-15ms response times.

Healthcare

Medical devices, diagnostic equipment, surgical robots, and patient monitoring systems with on-device AI inference.

Drones & UAVs

Autonomous drones for inspection, delivery, surveillance, and agriculture with edge AI for real-time decision making.

Industrial Automation

Smart manufacturing, predictive maintenance, quality control, and process optimization with embedded AI.

Smart Agriculture

Precision farming equipment, autonomous tractors, crop monitoring, and livestock management with AI vision.

Automotive & AV

Advanced driver assistance, autonomous vehicles, in-vehicle AI assistants, and predictive diagnostics.

DEVELOPMENT

Roadmap

Our path to production-ready bare-metal AI. Each phase brings us closer to the vision.

Phase 1: Foundation
Q3 2025

Core kernel infrastructure and boot process

Progress100%
x86_64 and ARM64 boot
Memory management (PMM/VMM)
Basic console I/O
Build system with CI/CD
Phase 2: AI Integration
Q1 2026

Minimal AI runtime and inference pipeline

Progress40%
Interrupt handling and timer
Cooperative task scheduler
AI model loading framework
Basic inference pipeline
Phase 3: Production Runtime
Q2 2026

Full GGUF support and optimizations

Progress0%
Complete GGUF parser
Quantization support (Q4/Q8)
KV cache management
Batch inference
Phase 4: Hardware Acceleration
Q3 2026

GPU and NPU integration

Progress0%
CUDA/ROCm support
NPU drivers (RPi AI Kit)
Multi-device inference
Memory pooling
Phase 5: MCP & Multiagent
Q4 2026

MCP integration and multiagent orchestration

Progress0%
Model Context Protocol support
Agent-to-agent communication
Multiagent task orchestration
Distributed inference coordination

v1.0 Target: Q2 2026

Production-ready bare-metal AI operating system with full hardware acceleration

GET IN TOUCH

Contact & Community

Have questions? Want to contribute? Reach out through any of these channels.

GitHub

Explore the code, report issues, contribute

github.com/dddimcha/embodiOS
500+ developers on the waitlist
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