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D-DAO

AI RUNTIME

ONE RUNTIME.EVERY AI WORKLOAD.

EXECUTION LAYER

A unified execution layer engineered to run enterprise AI workloads across distributed GPU infrastructure.

Enterprise AI Runtime Core with five concentric execution layers

UNIFIED RUNTIME STACK

ONE EXECUTION STACK.FROM REQUEST TO GPU.

One integrated runtime stack connects AI applications to distributed GPU execution through a unified set of runtime interfaces, execution services, and accelerator-native resource layers.

  1. 01

    AI Applications

    Training, inference, AI agents, and enterprise AI services enter the runtime through a common execution layer.

    Workload Layer
  2. 02

    Runtime APIs

    Unified interfaces submit, control, inspect, and observe enterprise AI execution.

    Execution Interface
  3. 03

    Execution EngineENTERPRISE RUNTIME CORE

    Launches and executes distributed AI processes across nodes, accelerators, and runtime environments.

    Runtime Core
  4. 04

    GPU Resource Manager

    Manages accelerator allocation, memory, GPU topology, affinity, and runtime resource access.

    Accelerator Resources
  5. 05

    GPU Runtime

    Provides the accelerator-native execution environment required by models, frameworks, and distributed AI workloads.

    GPU Execution
  6. 06

    AI Infrastructure

    GPU clusters, compute fabric, storage, and physical AI Factory systems provide the execution foundation.

    Physical Foundation

Execution Native

Built around actual AI process execution, not generic workload management.

GPU Aware

Understands accelerators, memory, topology, and runtime affinity.

Distributed by Design

Executes enterprise AI across multi-node GPU systems.

RUNTIME EXECUTION FLOW

ONE EXECUTION ENGINE.CONTINUOUS AI EXECUTION.

Every AI workload follows one execution path through the runtime, accelerator-native resource layers, and distributed GPU infrastructure.

  1. AI Workload

    Training, inference, AI agents, and distributed jobs enter the runtime.

  2. Execution Engine

    ENTERPRISE RUNTIME CORE

    Launches and executes AI processes across nodes and accelerators.

  3. GPU Resource Manager

    Provides access to accelerators, memory, topology, and runtime resources.

  4. GPU Runtime

    Runs accelerator-native frameworks, models, and distributed AI processes.

  5. Distributed GPU Fabric

    Connects execution across high-bandwidth multi-node GPU systems.

  6. Execution Telemetry

    Captures metrics, traces, logs, profiling, and runtime state.

  7. AI Results

    Returns completed execution output to enterprise AI applications.

Execution Native

Built around actual AI process execution.

GPU Aware

Understands accelerators, memory, topology, and affinity.

Observable

Every runtime stage produces measurable execution state.

Distributed

Runs across multi-node enterprise GPU systems.

RUNTIME ENGINE COMPONENTS

THE TECHNOLOGIESBEHIND EVERY EXECUTION.

Six runtime technologies work together to deliver distributed, accelerator-native, observable, secure, and high-performance enterprise AI execution.

Distributed Execution Engine

RUNTIME CORE

Executes AI processes reliably across nodes, accelerators, and distributed runtime environments.

Parallel ExecutionProcess RecoveryMulti-node Runtime

GPU Resource Manager

RUNTIME CORE

Manages accelerators, memory, topology, affinity, and runtime resource allocation.

GPU TopologyMemory AllocationRuntime Affinity

Runtime Isolation

Provides secure execution boundaries across enterprise AI workloads.

Tenant IsolationProcess IsolationAccelerator Isolation

Elastic Runtime

Expands and reclaims execution resources dynamically as workload demand changes.

Dynamic AllocationElastic ScalingResource Reclamation

Runtime Telemetry

Captures execution metrics, profiling, tracing, logs, and runtime state.

MetricsTracingGPU Profiling

High-performance Serving

Provides low-latency, high-throughput execution for enterprise AI applications.

Low LatencyHigh ThroughputContinuous Serving

RUNTIME ENGINEERING PRINCIPLES

ENGINEERED FORAI EXECUTION.

A runtime designed around accelerators, distributed systems, measurable execution, and enterprise-grade isolation.

  1. 01

    GPU Native

    Designed around accelerators, memory locality, topology, and high-bandwidth execution.

    The accelerator is the starting point.

  2. 02

    Distributed First

    Engineered for multi-node execution across enterprise GPU systems from the beginning.

    Scale is part of the runtime.

  3. 03

    Observable by Design

    Every execution produces measurable state across processes, resources, and accelerators.

    Execution is never a black box.

  4. 04

    Enterprise Secure

    Isolation, resilience, and execution boundaries are built directly into the runtime.

    Security begins at execution.

ONE EXECUTION LAYER.EVERY AI WORKLOAD.

Purpose-built for enterprise AI execution.