Draft:AI architecture models & design patterns

• Comment: We require multiple sources. qcne (talk) 17:10, 10 July 2026 (UTC)

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AI Architecture Models & Design Patters

AI architecture models and design patterns are approaches for organising and orchestrating artificial intelligence inference, application services, workload queues, and distributed computing resources. Such architectures may separate model execution from application logic and distribute inference or background processing across cloud servers, local systems, edge nodes, and other participating computers.

A distributed AI system may combine a shared inference layer with different methods of assigning computational work. One approach uses a replicated model service that is available to multiple applications. Other patterns determine whether background jobs are processed by shared workers, retained on a particular local node, or handled through a combination of shared and local execution.

Centralized Intelligence Model Architecture

The Centralized Intelligence Model Architecture is an architectural model in which multiple applications use a common AI inference layer rather than operating a separate model deployment for every application.

In a clustered deployment, the inference model may run as a replicated service across several computing nodes. Applications communicate with the shared inference service through the cluster network, while the underlying model replicas provide the processing capacity required for inference.

A simplified representation is:

Distributed applications
          ↓
Shared AI inference layer
          ↓
Replicated model services
          ↓
Participating compute nodes

The model separates the application layer from model deployment and execution. Applications are therefore concerned with their own domain logic, while model placement, replication, and inference capacity are managed as infrastructure services.

Architectural principles

The architecture is based on several related principles.

Shared intelligence layer

Multiple applications access a common inference service instead of maintaining independent model deployments.

Distributed execution

Model replicas may execute across more than one computing node while appearing to applications as a single logical service.

Horizontal scaling

Additional processing capacity can be introduced by adding suitable nodes or model-service replicas.

Application–model separation

Application services remain independent of the physical location and lifecycle of the model instances used for inference.

Network-wide inference access

Applications authorised to use the inference network may access the same model service through a common interface.

Infrastructure-driven expansion

Inference capacity can expand together with the underlying cluster rather than requiring each application to maintain its own dedicated AI infrastructure.

Distributed AI job processing

AI inference is only one part of a distributed AI platform. Supporting activities such as web crawling, document processing, indexing, embedding generation, model preparation, training tasks, and other computational workloads may also require substantial processing resources.

A distributed job-processing architecture can separate the ownership and tracking of a job from the machine on which that job is executed.

Two processing patterns are the Shared Worker Pattern and the Delegated Local Proxy Pattern.

Shared Worker Pattern

The Shared Worker Pattern is a distributed job-processing pattern in which job coordination remains centralised while execution is delegated to eligible participating worker nodes.

A typical processing path is:

Jobs
  ↓
Server queue
  ↓
Status monitor
  ↓
Any eligible participating worker

The server queue retains pending workloads while worker nodes participate in processing them. A worker may be located in a data centre, a cloud environment, an office network, an edge location, or a local workstation equipped with suitable processing hardware.

The central server does not necessarily have to execute the workloads itself. It may retain responsibility for job submission, queue management, status tracking, and ownership while execution takes place on other nodes.

This separation allows an infrastructure node responsible for orchestration to operate independently from the nodes supplying computational capacity.

Example configuration

An implementation may use configuration controls similar to:

PROCESS_JOBS = false
SHARE_LOAD_ENABLED = true

In this arrangement, the server does not execute queued jobs locally. A participating worker with shared processing enabled can retrieve eligible jobs from the server queue and execute them using its own computing resources.

For example, a web cluster may coordinate AI-intensive jobs while an office workstation equipped with a GPU performs the actual computation.

                  WEB CLUSTER
             ┌──────────────────┐
Jobs ───────►│   Server Queue   │
             │                  │
             │  Status Monitor  │
             │                  │
             │ PROCESS_JOBS     │
             │      = false     │
             └────────┬─────────┘
                      │
                      │ shared job
                      ▼
             ┌──────────────────┐
             │ OFFICE WORKER    │
             │                  │
             │ SHARED = true    │
             │                  │
             │ GPU / CPU        │
             │ AI processing    │
             └──────────────────┘

This arrangement can make use of existing computational resources located outside the main server cluster. An organisation may, for example, use available office GPU capacity for suitable workloads rather than requiring every computation to run on centrally hosted infrastructure.

Delegated Local Proxy Pattern

The Delegated Local Proxy Pattern is a node-centred processing model in which a proxy node retains responsibility for its own jobs, local queue, job status, and execution.

The processing path can be represented as:

Jobs
  ↓
Local queue
  ↓
Status monitor
  ↓
Local processor

Unlike a shared worker, which can obtain work from a central queue, a local proxy can retain the processing responsibility for jobs accepted by that node.

This approach may be useful when execution depends on resources that exist only within the local environment. Examples include:

• locally hosted AI models;
• private databases;
• internal network services;
• specialised hardware;
• local file systems;
• restricted datasets; and
• workloads subject to location or privacy requirements.

Example configuration

A node configured for local execution may use a control such as:

LOCAL_LOAD_ENABLED = true

With local processing enabled, accepted jobs remain on the node and are processed through its local job queue. When local execution is disabled, the proxy can instead submit suitable jobs to the central server queue.

Comparison of processing patterns

Characteristic	Shared Worker Pattern	Delegated Local Proxy Pattern
Queue location	Central or server queue	Node-specific local queue
Job ownership	Centrally coordinated	Retained by the local node
Execution location	Any eligible participating worker	Specific accepting node
Primary scaling model	Shared distributed capacity	Node-specific execution
Typical infrastructure	Cloud nodes, GPU workers, office servers, edge workers	Local AI systems, internal services, private data environments
Main objective	Distributed capacity	Locality and dependency isolation

Hybrid processing

The Shared Worker Pattern and Delegated Local Proxy Pattern can operate together on the same node.

A hybrid node may use a configuration such as:

SHARE_LOAD_ENABLED = true
LOCAL_LOAD_ENABLED = true

Such a node can process locally owned workloads while also accepting suitable jobs from the shared server queue.

For example, an office AI server may:

1. process internal jobs against a locally hosted model or private database;
2. retain workloads that must remain within the office environment; and
3. use spare GPU or CPU capacity to process jobs retrieved from the central queue.

The two workload paths can coexist:

LOCAL WORKLOAD

Job
 ↓
Local queue
 ↓
Local processor

SHARED WORKLOAD

Server job
 ↓
Server queue
 ↓
Participating worker
 ↓
Same node's processor

This creates a hybrid processing environment in which the location of execution can be selected according to workload requirements while idle computing capacity may still participate in distributed processing.

Relationship between inference architecture and job-processing patterns

The architecture model and job-processing patterns address different parts of an AI platform.

The Centralized Intelligence Model Architecture concerns access to shared AI inference services.

The Shared Worker Pattern concerns the distribution of centrally coordinated jobs to participating compute nodes.

The Delegated Local Proxy Pattern concerns workloads that remain under the responsibility of a specific node.

Together, these approaches form a layered system:

APPLICATION LAYER
Distributed applications and services
                ↓

AI INFERENCE LAYER
Shared model interface
                ↓

MODEL EXECUTION LAYER
Replicated AI model services
                ↓

JOB PROCESSING LAYER
Shared workers ↔ Local proxy processors
                ↓

INFRASTRUCTURE LAYER
Cloud | Office | Edge | Local | GPU nodes

The resulting architecture separates application functionality, inference access, workload coordination, and physical compute execution.

Applications

Distributed AI architectures of this type can be applied where computational workloads are spread across different infrastructure environments. Possible applications include:

• document crawling and indexing;
• retrieval system preparation;
• embedding generation;
• batch inference;
• data transformation;
• model evaluation;
• local inference;
• private AI processing;
• GPU-assisted background tasks; and
• distributed application services.

The suitability of a shared, local, or hybrid execution path depends on factors such as resource availability, network connectivity, data location, hardware requirements, and workload characteristics.

See also

• Artificial intelligence
• Distributed computing
• Distributed computing architecture
• Cluster computing
• Edge computing
• Cloud computing
• Message queue
• Job scheduler
• Service-oriented architecture
• Microservices
• Inference
• Graphics processing unit

External links

• Architecture Models and Design Patterns — technical architecture reference