OmniStack Framework · v1

Core Architecture

OmniStack operates as a dynamic overlay, hooking into the Linux kernel via eBPF and custom kmods. It adapts to the host distribution without requiring a custom kernel recompile per distro.

The stack at a glance

L3Orchestration & Data Center
NETNetworking Fabric
L2Adaptive OS Layer
KERNKernel Hooks
L1Hardware Abstraction
Layer 1

Hardware Abstraction & Silicon Translation

Instead of native drivers fighting for resources, OmniStack acts as a traffic controller for heterogeneous compute.

NVIDIA CUDA / TensorRT

Intercepts matrix math and routes it to available GPU cores dynamically.

Intel OpenVINO

Edge CPU and Intel NPU optimization via OneAPI hooks.

AMD ROCm

Translates standard ML primitives into AMD-specific instructions.

Quantum QPU Subsystem

Holds quantum states and offloads to IBM Qiskit, Google Cirq, or a local simulator.

Layer 2

Adaptive OS Layer (Distro Agnostic)

Ships as user-space daemons and kernel headers tailored per distribution family.

Enterprise

RHEL, SUSE SLES, Ubuntu Pro. Stability, NUMA-awareness, high availability.

Bleeding edge

Fedora. Testing ground for new scheduler algorithms.

Hypervisors

VMware ESXi via VIBs for passthrough AI/Quantum acceleration to VMs.

Layer 3

Orchestration & Data Center

Multi-tenant, multi-rack, multi-region — auto-tuned to the hardware it lands on.

Equinix Metal + K8s

Auto-discovery agents spin up Kubernetes clusters tuned to the silicon they land on.

HCI

Hyperconverged storage and compute pooling via Ceph and NVMe-oF for AI datasets.

Networking Fabric · the nerve center

ComponentIntegration strategy
SONiCBGP EVPN control-plane integration to prioritize AI tensor-flow traffic across switches.
Arcos OSDeep routing analytics for low-latency, lossless RoCEv2 transfer.
HPC Leaf & SpineZero-trust non-blocking topologies using RDMA over Converged Ethernet, tuned for Supermicro AI chassis.

Toolkit distributions

OmniStack Enterprise

Data Center & HPC
Deployment
Helm charts for K8s · RPM/DEB packages for bare-metal
Scale
Multi-node distributed AI training and hybrid-quantum algorithms
Hardware
Supermicro GPU/NPU multi-rack clusters · Equinix data centers
Features
VMware vSphere integration, live workload migration, automated failover, SLA-driven allocation

OmniStack Lite

Student & Developer Community
Deployment
Docker Desktop extensions · Snap / Flatpak · Yocto BitBake images for Pi
Scale
Single-node optimization and simulation
Hardware
Consumer laptops · integrated GPUs · Google Coral and similar TPUs
Features
Simulated QPU · unified API wrapper: write once, compare CUDA vs. OpenVINO

Build & deployment strategy

01 · Edge & embedded

Yocto Project (BitBake) recipes

Custom BitBake layers meta-omnistack-ai and meta-omnistack-qpu strip the kernel to essentials, compiling only the silicon drivers needed for edge TPU/NPU devices.

02 · Kernel layer

eBPF traffic-control optimization

eBPF programs intercept memory allocation requests. A matrix-multiply allocation is seamlessly redirected to GPU/NPU VRAM.

03 · Orchestration

Kubernetes Device Plugins

Custom plugins teach K8s to see, request and limit CUDA cores, OpenVINO threads, and quantum qubits — not just CPU and memory.

04 · HPC / SONiC

Network OS overlay packaging

Networking daemons compiled as containerized apps that run natively inside SONiC and Arcos OS switches, managing Leaf/Spine congestion for AI telemetry.