MultiCortex OS is an AI‑focused operating system that natively schedules and balances large‑language‑model inference across CPUs, GPUs, NPUs and other accelerators within a single node using a unified driver stack and dynamic workload allocator. It maximizes hardware throughput, lowers token‑per‑second costs, and provides enterprise‑grade security and AWS Marketplace integration for plug‑and‑play deployment of pre‑optimized models.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises running large‑language‑model workloads often rely on a single type of accelerator (GPU or CPU), which leads to under‑utilized hardware, higher inference latency, and vendor lock‑in. Existing server operating systems lack native support for orchestrating heterogeneous compute resources, limiting productivity and increasing operational costs.
Solution
MultiCortex OS is an AI‑focused operating system that natively schedules and balances workloads across CPUs, GPUs, NPUs and other accelerators within a single node. By exposing a unified driver stack and a dynamic scheduler, the OS maximizes hardware throughput and reduces token‑per‑second costs for inference. The platform offers a customizable interface and APIs that let developers deploy any LLM—such as Phi‑4, Gemma 3, LLaMa 3.2, DeepSeek R‑1 or Mistral—without rewriting code for each accelerator. MultiCortex OS runs on an openSUSE base, integrates with AWS Marketplace for plug‑and‑play deployment, and enforces enterprise‑grade security and compliance, enabling organizations to accelerate AI services while avoiding technology lock‑in.
Target Audience
Primary customers are large enterprises and AI service providers in sectors such as finance, healthcare, and cloud‑based SaaS, who require high‑performance, cost‑effective inference and training across mixed‑accelerator fleets.
Features
- Heterogeneous scheduler that partitions model inference across CPU, GPU, and NPU pipelines in real time.
- Unified driver abstraction layer supporting major accelerator vendors (Intel, NVIDIA, AMD, custom ASICs).
- Dynamic workload allocator that optimizes token‑per‑second throughput based on current resource availability.
- Extensible kernel modules and SDK for custom model integration and fine‑grained resource control.
- Built‑in security framework with role‑based access, encrypted model artifacts, and compliance reporting.
- AWS Marketplace integration delivering plug‑and‑play containers (no token fees) for a catalog of pre‑optimized LLMs.
- Open‑source‑compatible API (REST/gRPC) for seamless CI/CD pipelines and orchestration tools.