AI Edge Inference with Dual Backend Architecture

Status

Draft
Proposed
Accepted
Deprecated

Date: 2025-09-11 Updated: 2025-10-15 (Production Implementation Complete)

Context

Business Need

Industrial edge computing scenarios require AI inference capabilities that can process visual data in real-time with minimal latency, reduced cloud dependency, and optimized resource utilization. Common use cases include:

Industrial Safety Monitoring: Real-time detection of safety hazards, PPE compliance, unauthorized access
Quality Assurance: Visual inspection for defects, anomaly detection in manufacturing processes
Environmental Monitoring: Leak detection, equipment condition monitoring, facility surveillance
Operational Efficiency: Asset tracking, workflow optimization, predictive maintenance indicators

Technical Requirements

The edge AI inference service must meet the following requirements:

Performance Requirements:

End-to-end latency < 250ms for real-time responsiveness
Support for GPU-accelerated inference (NVIDIA A2000 and similar edge GPUs)
Minimal memory footprint (< 512MB typical operation)
High throughput (4-6 inferences/second per backend)
Container size optimized for edge deployment (< 100MB)

Integration Requirements:

Integration with Azure IoT Operations MQTT broker for message ingestion and publishing
Support for existing edge infrastructure (Ubuntu + K3s Kubernetes)
Compatibility with Media Connector for RTSP camera feeds
Standardized JSON output schema for downstream processing

Operational Requirements:

Hot-swappable models without service restart
Configurable model selection via YAML configuration
Health monitoring and metrics exposure (Prometheus-compatible)
Structured logging with configurable verbosity
Support for both local development and production deployment

Model Requirements:

Support for ONNX and native Candle model formats
Multiple model types: image classification, object detection, custom industrial models
Configurable confidence thresholds and preprocessing parameters
Model validation and versioning capabilities

Existing Architecture Context

The edge AI system operates within the Azure IoT Operations ecosystem:

MQTT Broker: Azure IoT Operations MQTT broker for reliable message delivery
Media Connector: RTSP stream integration for camera feeds with snapshot capabilities
Edge Storage: Azure Container Storage (ACSA) for model storage and caching
Observability: Prometheus/Grafana stack for monitoring and alerting
Security: TLS-encrypted connections, certificate-based authentication

Initial Implementation Challenges

Early prototyping revealed several key insights:

Python-based ML stacks (PyTorch, TensorFlow) have significant overhead:
- Cold start times of 6-8 seconds
- Memory footprint of 1.2GB+ containers
- Complex dependency management for edge deployment
ONNX Runtime provides good model compatibility but:
- Still requires C++ bindings and FFI overhead
- Limited optimization for pure Rust workflows
- Average inference time: 220ms
Pure Rust ML frameworks (Candle) show promise:
- Faster cold start (2-3 seconds)
- Smaller container size (< 50MB)
- Better CPU efficiency on resource-constrained devices

Decision

Implement a dual-backend AI inference service in Rust that supports both ONNX Runtime and Hugging Face Candle inference engines, providing:

Primary Inference Path: Candle backend for optimal edge performance (155ms average inference)
Fallback Compatibility: ONNX Runtime for complex models requiring broader ecosystem support (220ms average inference)
Performance Comparison: Built-in capability to benchmark both backends with identical inputs
Unified Interface: Single service deployment with runtime-selectable backend configuration

Architecture Pattern: Dual Backend with Conditional Compilation

Rather than implementing separate services for each ML framework, we chose a unified service with conditional compilation through Cargo feature flags:

[features]
default = ["candle"]
candle = ["dep:candle-core", "dep:candle-nn", "dep:candle-transformers"]
onnx = ["dep:ort", "dep:ndarray"]
dual = ["candle", "onnx"]  # Both backends for performance comparison
minimal = []  # Ultra-lightweight build without ML dependencies

Rationale:

Operational Simplicity: Single deployment artifact, one service to manage
Resource Efficiency: Shared infrastructure (MQTT client, configuration, monitoring)
Flexible Deployment: Feature flags enable optimization for specific deployment scenarios
Development Efficiency: Shared codebase reduces maintenance burden
Performance Testing: Dual backend mode enables continuous optimization

Decision Drivers

Primary Drivers (High Priority)

Edge Performance Optimization
- Description: Minimize latency, memory usage, and startup time for edge deployment
- Impact: Candle delivers 29.5% faster inference (155ms vs 220ms) and 74MB less memory
- Weight: Critical - directly affects user experience and resource costs
Operational Simplicity
- Description: Single service deployment reduces operational complexity
- Impact: Easier monitoring, updates, and troubleshooting in production
- Weight: High - reduces operational overhead and error surface
Model Ecosystem Compatibility
- Description: Support for both emerging (Candle) and established (ONNX) ML frameworks
- Impact: ONNX Runtime provides fallback for complex models not yet supported in Candle
- Weight: High - ensures broad model compatibility

Secondary Drivers (Medium Priority)

Container Size and Deployment Speed
- Description: Minimize container image size for faster deployment and updates
- Impact: 24.8MB final container vs 1.2GB for Python-based alternatives
- Weight: Medium - positions solution for future ML advancements

Considered Options

Option 1: ONNX Runtime Only (Original Proposal)

Description: Implement inference service using only ONNX Runtime as the ML backend.

Advantages:

Container Size and Deployment Speed
- Description: Minimize container image size for faster deployment and updates
- Impact: 24.8MB final container vs 1.2GB for Python-based alternatives
- Weight: Medium - improves deployment reliability on bandwidth-constrained edge sites
Development Velocity
- Description: Pure Rust stack aligns with existing Azure IoT Operations SDK integration
- Impact: Faster development cycles, better IDE support, stronger type safety
- Weight: Medium - accelerates feature development and reduces bugs
Future-Proofing
- Description: Growing Rust ML ecosystem provides long-term sustainability
- Impact: Candle ecosystem expanding rapidly with Hugging Face backing
- Weight: Medium - positions solution for future ML advancements

Technical Details:

Single backend simplifies initial implementation
Broad model compatibility with PyTorch, TensorFlow, scikit-learn exports
Proven production stability in enterprise environments
Hardware acceleration via TensorRT, DirectML, CoreML

Pros:

Widest model ecosystem support (virtually any ML framework can export to ONNX)
Battle-tested in production at scale (Microsoft, NVIDIA, cloud providers)
Excellent documentation and tooling
Hardware vendor support for accelerators

Cons:

Slower inference performance (220ms vs 155ms for Candle)
Larger memory footprint (74MB more than Candle)
C++ FFI overhead in Rust integration
Less optimized for pure Rust workflows

Risks:

Performance Risk: May not meet < 200ms latency target for all models
- Probability: Medium
- Impact: Medium
- Mitigation: Optimize preprocessing, use GPU acceleration, profile hotspots
Resource Constraints: Higher memory usage may limit concurrent model loading
- Probability: Low
- Impact: Medium
- Mitigation: Implement model unloading policies, memory monitoring

Dependencies:

ort crate (ONNX Runtime Rust bindings)
System ONNX Runtime libraries
CUDA libraries for GPU support

Costs:

Initial: 2-3 weeks implementation
Ongoing: Standard maintenance, ONNX Runtime version updates
Effort: Low complexity, well-documented integration

Option 2: Candle Only (Pure Rust Approach)

Description: Implement inference service using only Hugging Face Candle as the ML backend.

Technical Details:

Pure Rust implementation, no FFI overhead
Native integration with Rust async runtime (Tokio)
Growing model ecosystem with Hugging Face support
CUDA and Metal GPU acceleration

Pros:

Best performance: 155ms average inference (29.5% faster than ONNX)
Minimal memory footprint (74MB less than ONNX Runtime)
Fastest cold start time (2-3 seconds vs 6-8 seconds)
Smallest container size (24.8MB)
Pure Rust benefits: memory safety, better error handling, type safety

Cons:

Smaller model ecosystem compared to ONNX (growing rapidly)
Newer framework with evolving API stability
Some complex models may not have Candle equivalents yet
Smaller community and fewer production deployments

Risks:

Model Compatibility Risk: Critical models may not be available in Candle format
- Probability: Medium
- Impact: High
- Mitigation: Validate all required models before full commitment
Framework Maturity: API changes may require code updates
- Probability: Medium
- Impact: Low
- Mitigation: Pin versions, monitor release notes, maintain test coverage

Dependencies:

candle-core, candle-nn, candle-transformers crates
CUDA toolkit for GPU support
SafeTensors format for model weights

Costs:

Initial: 3-4 weeks implementation (newer framework, less documentation)
Ongoing: API updates as framework matures
Effort: Medium complexity, learning curve for Candle API

Option 3: Dual Backend Architecture (Selected)

Description: Implement both ONNX Runtime and Candle backends with conditional compilation, allowing runtime selection and performance comparison.

Technical Details:

Unified inference interface abstracts backend differences
Cargo feature flags enable selective compilation
Runtime configuration selects active backend
Comparison mode runs both backends for benchmarking

Architecture:

trait InferenceBackend {
    fn load_model(&mut self, config: &ModelConfig) -> Result<()>;
    fn infer(&self, input: &ImageTensor) -> Result<InferenceResult>;
    fn backend_name(&self) -> &str;
}

struct CandleBackend { /* ... */ }
struct OnnxBackend { /* ... */ }

enum Backend {
    Candle(CandleBackend),
    Onnx(OnnxBackend),
    Dual(CandleBackend, OnnxBackend),
}

Pros:

Best of both worlds: Candle performance with ONNX compatibility fallback
Performance optimization: Continuous benchmarking identifies best backend per model
Risk mitigation: ONNX fallback ensures broad model support
Flexible deployment: Feature flags optimize for specific scenarios
Future-ready: Easily adopt new backends as ecosystem evolves

Cons:

Increased codebase complexity with two backend implementations
Dual mode increases binary size (mitigated by feature flags)
Backend abstraction adds minor overhead (< 1ms)
More testing surface area

Risks:

Maintenance Burden: Two backends require ongoing updates
- Probability: Medium
- Impact: Low
- Mitigation: Shared interface minimizes duplication, automated testing
Configuration Complexity: Users must choose backend
- Probability: Low
- Impact: Low
- Mitigation: Sensible defaults (Candle primary), clear documentation

Dependencies:

Both Candle and ONNX Runtime dependencies
Conditional compilation managed via Cargo features
Unified configuration schema for both backends

Costs:

Initial: 4-5 weeks implementation (both backends + abstraction layer)
Ongoing: Maintain both backends, but shared infrastructure reduces duplication
Effort: High initial complexity, medium ongoing maintenance

Option 4: Python-Based ML Stack

Description: Use traditional Python ML frameworks (PyTorch, TensorFlow) with Rust wrapper service.

Pros:

Largest ML ecosystem and community
Most mature tooling and pre-trained models
Familiar to data scientists

Cons:

Significantly slower (6-8 second cold start)
Much larger containers (1.2GB+)
Complex dependency management
Higher memory usage
Not considered viable for edge deployment

Rejected: Does not meet edge deployment requirements.

Comparison Matrix

Criteria	Weight	ONNX Only	Candle Only	Dual Backend	Python Stack
Inference Performance	High	7/10	10/10	10/10	5/10
Model Compatibility	High	10/10	6/10	10/10	10/10
Container Size	Medium	6/10	10/10	8/10	3/10
Operational Simplicity	High	9/10	9/10	7/10	5/10
Development Velocity	Medium	8/10	6/10	6/10	9/10
Edge Optimization	High	7/10	10/10	10/10	4/10
Community/Ecosystem	Medium	9/10	6/10	9/10	10/10
Risk (lower is better)	High	3/10	6/10	4/10	7/10
Total Score		59	57	64	53

Scoring: 1-10 scale (10 = best) Result: Dual Backend Architecture provides the best balance of performance, compatibility, and risk mitigation.

Consequences

Positive Outcomes

Superior Edge Performance
- Candle backend delivers 155ms inference (29.5% faster than ONNX)
- 24.8MB container size enables rapid deployment and updates
- Sub-3-second cold start improves service availability
- 74MB less memory usage per instance enables higher density
Production Reliability
- ONNX Runtime fallback ensures critical model compatibility
- Dual backend validation enables continuous optimization
- Proven Azure IoT Operations MQTT integration
- Comprehensive monitoring and health checks
Operational Benefits
- Single service deployment simplifies operations
- Hot-swappable models without downtime
- Feature flags optimize deployment for specific scenarios
- Unified configuration and observability
Developer Experience
- Pure Rust stack improves type safety and error handling
- Faster development cycles with better tooling
- Shared codebase reduces maintenance burden
- Clear backend abstraction simplifies testing
Future-Proofing
- Growing Candle ecosystem provides long-term optimization path
- ONNX compatibility ensures backward compatibility
- Easy addition of new backends as ecosystem evolves
- Aligns with Azure IoT Operations Rust-first architecture

Negative Outcomes

Increased Complexity
- Two backend implementations require parallel maintenance
- Backend abstraction layer adds minor overhead
- More comprehensive testing matrix required
- Configuration complexity for backend selection
Framework Maturity
- Candle API still evolving (though Hugging Face backing provides stability)
- Some advanced models may not have Candle equivalents
- Smaller community for Candle-specific issues
- Documentation less comprehensive than ONNX Runtime
Binary Size
- Dual backend mode increases deployment size (mitigated by feature flags)
- Larger attack surface with two ML frameworks
- More dependencies to track for security updates

Risks and Mitigations

Model Compatibility Issues
- Risk: Required models may not be available in Candle format
- Mitigation: ONNX Runtime fallback provides compatibility safety net
- Mitigation: Pre-validate all critical models before deployment
- Mitigation: Maintain conversion tools for ONNX to Candle migration
Performance Degradation
- Risk: Model updates may reduce inference speed
- Mitigation: Continuous benchmarking with dual backend mode
- Mitigation: Performance regression tests in CI/CD pipeline
- Mitigation: Configurable quality vs. speed trade-offs
MQTT Connection Reliability
- Risk: Network disruptions may cause message loss
- Mitigation: Enhanced connection monitoring and automatic reconnection
- Mitigation: Message queuing during disconnection periods
- Mitigation: Circuit breaker pattern for degraded broker states
Resource Exhaustion
- Risk: Multiple concurrent inferences may exhaust GPU memory
- Mitigation: Configurable inference queue depth
- Mitigation: Model size validation and memory monitoring
- Mitigation: Automatic model unloading based on usage patterns
Dependency Vulnerabilities
- Risk: Security vulnerabilities in ML framework dependencies
- Mitigation: Automated dependency scanning in CI/CD
- Mitigation: Regular security updates and patch management
- Mitigation: Container scanning with configurable severity thresholds

Implementation Details

Component Architecture

graph TB
    subgraph "AI Edge Inference Service (507-ai-inference)"
        MQTT[MQTT Client<br/>Azure IoT Operations SDK]
        Router[Topic Router]
        Queue[Inference Queue]
        Config[Configuration Manager]

        subgraph "Dual Backend Engine"
            Interface[Inference Interface]
            Candle[Candle Backend<br/>155ms avg]
            ONNX[ONNX Runtime Backend<br/>220ms avg]
            Comparator[Performance Comparator]
        end

        Models[Model Manager<br/>Hot-swappable]
        Metrics[Metrics Exporter<br/>Prometheus]
        Health[Health Monitor]
    end

    Camera[Media Connector<br/>Camera Snapshots] --> MQTT
    Sensors[Sensor Data] --> MQTT
    MQTT --> Router
    Router --> Queue
    Queue --> Interface
    Interface --> Candle
    Interface --> ONNX
    Candle --> Comparator
    ONNX --> Comparator
    Comparator --> Results[Result Formatter]
    Results --> MQTT
    Config --> Models
    Config --> Interface
    Models --> Candle
    Models --> ONNX
    Metrics --> Prometheus[Prometheus/Grafana]
    Health --> K8s[Kubernetes Probes]

    style Candle fill:#90EE90
    style ONNX fill:#FFB6C1
    style MQTT fill:#87CEEB
    style Interface fill:#DDA0DD

Directory Structure

src/500-application/507-ai-inference/
├── docker-compose.yaml              # Local development orchestration
├── services/
│   ├── ai-edge-inference/           # Main inference service (Rust)
│   │   ├── Cargo.toml               # Dependencies with feature flags
│   │   ├── Dockerfile               # Multi-stage container build
│   │   └── src/
│   │       ├── main.rs              # Service entry point
│   │       ├── mqtt_client.rs       # Enhanced MQTT integration
│   │       ├── inference.rs         # Backend abstraction
│   │       ├── config.rs            # Configuration management
│   │       └── backends/
│   │           ├── candle.rs        # Candle implementation
│   │           └── onnx.rs          # ONNX implementation
│   └── ai-edge-inference-crate/     # Shared library crate
├── charts/                          # Kubernetes manifests
│   ├── base/
│   │   ├── deployment.yaml
│   │   ├── service.yaml
│   │   └── configmap.yaml
│   └── model-downloader-job.yaml   # Init job for model download
└── resources/
    ├── model_configs/               # Model YAML configurations
    │   ├── industrial-safety.yaml
    │   └── tiny-yolo.yaml
    └── models/                      # Model files (gitignored, downloaded)

Configuration Schema

# Example: resources/model_configs/industrial-safety.yaml
name: "industrial-safety-detector"
backend: "candle"  # candle | onnx | dual
model_path: "/models/industrial-safety.safetensors"
model_type: "image-classification"

preprocessing:
  resize: [224, 224]
  normalize: true
  mean: [0.485, 0.456, 0.406]
  std: [0.229, 0.224, 0.225]

inference:
  confidence_threshold: 0.7
  batch_size: 1
  gpu_enabled: true

mqtt:
  input_topics:
    - "edge-ai/+/+/camera/snapshots"
  output_topic_template: "edge-ai/{business_unit}/{facility}/ai/inference/vision"

Deployment Configuration

Environment Variables:

Variable	Description	Default
`DEFAULT_BACKEND`	Primary backend	`candle`
`ENABLE_DUAL_BACKEND`	Compare both backends	`false`
`MODEL_CONFIG_PATH`	Model configuration	`/app/resources/model_configs/industrial-safety.yaml`
`AIO_BROKER_HOSTNAME`	MQTT broker host	`aio-mq-dmqtt-frontend`
`AIO_BROKER_TCP_PORT`	MQTT broker port	`1883`
`MQTT_INPUT_TOPICS`	Input topic patterns	`edge-ai/+/+/camera/snapshots`
`RUST_LOG`	Logging verbosity	`info,ai_edge_inference=debug`
`GPU_ENABLED`	Enable GPU acceleration	`true`

MQTT Topic Structure

# Input Topics (subscribe)
edge-ai/{business_unit}/{facility}/{gateway_id}/camera/snapshots
edge-ai/{business_unit}/{facility}/{gateway_id}/sensors/temperature

# Output Topics (publish)
edge-ai/{business_unit}/{facility}/{gateway_id}/ai/inference/vision
edge-ai/{business_unit}/{facility}/{gateway_id}/ai/inference/sensor
edge-ai/{business_unit}/{facility}/{gateway_id}/ai/status
edge-ai/{business_unit}/{facility}/{gateway_id}/ai/metrics

Message Schemas

Input Message (Camera Snapshot):

{
  "device_id": "camera-01",
  "timestamp": 1697385600,
  "image_data": "base64_encoded_jpeg_data",
  "metadata": {
    "location": "assembly-line-3",
    "resolution": "1920x1080"
  }
}

Output Message (Inference Result):

{
  "message_type": "ai_inference_result",
  "timestamp": 1697385600,
  "device_id": "camera-01",
  "model_name": "industrial-safety-detector",
  "backend_used": "candle",
  "inference_time_ms": 155,
  "predictions": [
    {
      "class": "person_without_helmet",
      "confidence": 0.94,
      "bbox": [120, 340, 280, 520]
    },
    {
      "class": "forklift",
      "confidence": 0.87,
      "bbox": [450, 200, 780, 600]
    }
  ],
  "metadata": {
    "preprocessing_time_ms": 12,
    "total_objects_detected": 2,
    "alert_triggered": true
  }
}

Dual Backend Comparison (when enabled):

{
  "message_type": "dual_backend_comparison",
  "timestamp": 1697385600,
  "device_id": "camera-01",
  "model_name": "industrial-safety-detector",
  "backends": {
    "candle": {
      "inference_time_ms": 155,
      "predictions": [...],
      "memory_usage_mb": 180
    },
    "onnx": {
      "inference_time_ms": 220,
      "predictions": [...],
      "memory_usage_mb": 254
    }
  },
  "performance_delta": {
    "time_difference_ms": 65,
    "candle_faster_by_percent": 29.5,
    "predictions_match": true
  }
}

Performance Metrics

Production Benchmarks (Edge Device - NVIDIA A2000)

Metric	Candle Backend	ONNX Runtime	Python Stack
Average Inference Time	155ms	220ms	450ms
Cold Start Time	2.1s	3.8s	6.8s
Container Size	24.8MB	98MB	1.2GB
Memory Usage (Runtime)	180MB	254MB	780MB
Throughput (inf/sec)	6.4	4.5	2.2
GPU Memory Usage	340MB	410MB	890MB

Resource Requirements

Minimum Requirements:

CPU: 2 cores
Memory: 512MB
GPU: Optional (CPU fallback available)
Storage: 500MB (includes models)

Recommended Production:

CPU: 4 cores
Memory: 1GB
GPU: NVIDIA A2000 or similar (4GB VRAM)
Storage: 2GB (multiple models)

Success Metrics

Functional Requirements ✅

End-to-end latency < 250ms (Achieved: 155ms with Candle)
Model hot-swapping without service restart
MQTT integration with Azure IoT Operations
Dual backend performance comparison
GPU acceleration support
Configurable confidence thresholds

Performance Requirements ✅

Container size < 100MB (Achieved: 24.8MB)
Memory usage < 512MB (Achieved: 180MB typical)
Cold start < 5 seconds (Achieved: 2.1s)
Throughput > 4 inferences/second (Achieved: 6.4 inf/s)

Integration Requirements ✅

Compatible with Media Connector snapshots
Standardized JSON output schema
Prometheus metrics exposure
Kubernetes health probes
Structured logging integration

Operational Requirements ✅

Reproducible deployment via docker-compose and Kubernetes
Clear configuration documentation
Troubleshooting guides and runbooks
99.9% uptime during testing period

Edge Data Transform Separation - Preprocessing responsibilities between components
MQTT QoS - Message delivery guarantees for AI results
Observability for Disconnected Environments - Monitoring strategies for edge AI

Technical Documentation

Application Instructions - Component structure standards
507-ai-inference README - Service documentation
Docker Compose Configuration - Local development setup

External References

Hugging Face Candle - Primary ML framework
ONNX Runtime - Fallback inference engine
Azure IoT Operations - Edge platform
Azure IoT Operations MQTT Broker - Message infrastructure

AI and automation capabilities described in this architecture should be implemented following responsible AI principles, including fairness, reliability, safety, privacy, inclusiveness, transparency, and accountability. Organizations should ensure appropriate governance, monitoring, and human oversight are in place for all AI-powered solutions.

Status​

Context​

Business Need​

Technical Requirements​

Existing Architecture Context​

Initial Implementation Challenges​

Decision​

Architecture Pattern: Dual Backend with Conditional Compilation​

Decision Drivers​

Primary Drivers (High Priority)​

Secondary Drivers (Medium Priority)​

Considered Options​

Option 1: ONNX Runtime Only (Original Proposal)​

Option 2: Candle Only (Pure Rust Approach)​

Option 3: Dual Backend Architecture (Selected)​

Option 4: Python-Based ML Stack​

Comparison Matrix​

Consequences​

Positive Outcomes​

Negative Outcomes​

Risks and Mitigations​

Implementation Details​

Component Architecture​

Directory Structure​

Configuration Schema​

Deployment Configuration​

MQTT Topic Structure​

Message Schemas​

Performance Metrics​

Production Benchmarks (Edge Device - NVIDIA A2000)​

Resource Requirements​

Success Metrics​

Functional Requirements ✅​

Performance Requirements ✅​

Integration Requirements ✅​

Operational Requirements ✅​

Related ADRs and Documentation​

Related ADRs​

Technical Documentation​

External References​

Status

Context

Business Need

Technical Requirements

Existing Architecture Context

Initial Implementation Challenges

Decision

Architecture Pattern: Dual Backend with Conditional Compilation

Decision Drivers

Primary Drivers (High Priority)

Secondary Drivers (Medium Priority)

Considered Options

Option 1: ONNX Runtime Only (Original Proposal)

Option 2: Candle Only (Pure Rust Approach)

Option 3: Dual Backend Architecture (Selected)

Option 4: Python-Based ML Stack

Comparison Matrix

Consequences

Positive Outcomes

Negative Outcomes

Risks and Mitigations

Implementation Details

Component Architecture

Directory Structure

Configuration Schema

Deployment Configuration

MQTT Topic Structure

Message Schemas

Performance Metrics

Production Benchmarks (Edge Device - NVIDIA A2000)

Resource Requirements

Success Metrics

Functional Requirements ✅

Performance Requirements ✅

Integration Requirements ✅

Operational Requirements ✅

Related ADRs and Documentation

Related ADRs

Technical Documentation

External References