Documentation

Atlas Runtime

The secure execution environment for your AI workloads.

Last updated on February 2, 2026

Overview

Atlas Runtime is the “Sovereign Guard” of the MX4 Atlas platform. It provides a secure, air-gapped execution environment that ensures model inference happens in a strictly controlled environment on your infrastructure.

Key Features

• Zero-trust architecture with end-to-end encryption
• Air-gapped deployment options for maximum security
• Infrastructure activity journaling for operational visibility
• Controlled update workflows and patch management
• Multi-region options within your infrastructure

Architecture

Atlas Runtime consists of several key components working together to provide secure AI inference:

Security Enclave

Hardware-backed secure execution environment that isolates model inference from the host system.

Routing Controls

Real-time routing rules for data residency, access controls, and workload isolation.

Activity Journal

Local infrastructure activity journaling for operational visibility.

Model Isolation

Isolated execution environment for model inference with resource limits and monitoring.

Deployment Options

Cloud Deployment

Deploy Atlas Runtime in your preferred cloud provider with built-in security controls and operational visibility.

yaml

1# atlas-runtime-config.yaml
2deployment:
3  type: cloud
4  provider: aws
5  region: me-south-1
6  security:
7    encryption: kms
8    monitoring: enabled

Air-Gapped Deployment

Complete offline deployment for maximum security. Models and data never leave your infrastructure.

bash

1# Air-gapped installation
2./atlas-runtime install --offline   --model-store /secure/models   --data-store /secure/data   --activity-journal /secure/logs

Hybrid Deployment

Combine cloud scalability with on-premise security. Sensitive workloads run locally while leveraging cloud resources for non-sensitive tasks.

json

1{
2  "deployment": {
3    "mode": "hybrid",
4    "local": {
5      "sensitive_workloads": true,
6      "models": ["mx4-atlas-core", "mx4-arabic-specialist"]
7    },
8    "cloud": {
9      "non_sensitive_workloads": true,
10      "scaling": "auto"
11    }
12  }
13}

Monitoring and Observability

Atlas Runtime provides monitoring capabilities to ensure operational excellence and visibility.

Key Metrics

99.5%

Target SLA (Enterprise)

<100ms

Average Latency

Local

Monitoring Ownership

Health Checks

Implement health checks to ensure runtime availability and performance:

health_monitoring.pypython

1import requests
2import logging
3
4def check_runtime_health():
5    """Monitor Atlas Runtime health status"""
6    health_endpoint = "http://localhost:8080/health"
7    
8    try:
9        response = requests.get(health_endpoint, timeout=5)
10        if response.status_code == 200:
11            health_data = response.json()
12            logging.info(f"Runtime Status: {health_data['status']}")
13            logging.info(f"Active Models: {health_data['models_loaded']}")
14            logging.info(f"Memory Usage: {health_data['memory_percent']}%")
15            return True
16        else:
17            logging.error(f"Health check failed: {response.status_code}")
18            return False
19    except Exception as e:
20        logging.error(f"Health check error: {e}")
21        return False

Performance Tuning

Batch Processing

Group multiple requests to reduce overhead and improve GPU utilization by 40-60%.

Model Quantization

Use quantized models for 50% faster inference with minimal accuracy loss.

Request Pipelining

Enable continuous batching to serve overlapping requests with continuous batching technology.

Resource Allocation

Allocate appropriate CPU/GPU cores based on workload patterns for cost optimization.

Troubleshooting

High Memory Usage

Runtime consuming excessive memory during inference.

Solution: Enable model paging, reduce batch size, or deploy quantized model variants.

Slow Inference

Model responses taking longer than expected.

Solution: Check GPU utilization, enable continuous batching, verify no resource contention.

Routing Rule Violations

Routing rules blocking requests due to residency constraints.

Solution: Review activity journal entries, verify data routing rules, and update configuration if needed.

Note: For detailed security architecture information, see the Sovereign Architecture documentation.