EDGE and Data Center Solutions By Axcelia
Axcelia is a global leader in Edge Compute Solutions. Our patented Multi-Tenant Edge Compute (MTEC) platform is purpose-built for enterprise AI applications and services, delivering scalability, security, and performance at the edge.
The Axcelia AI Stack for Edge delivers flexibility, scalability, security, intelligent data management, and dynamic cluster allocation — all optimized for real-world edge AI deployments.
Visit the Axcelia AI Platform to explore the full capabilities.
With fully touch-free operation, our solutions drive peak performance and maximize ROI, making every deployment seamless and cost-effective for our customers.
Full-Stack Intelligence from Hardware to AI
Axcelia AI is an intelligent and user-friendly platform engineered to manage the complete AI deployment pipeline — from physical hardware resources to live model training and orchestration. Designed for scale, Axcelia seamlessly integrates infrastructure management with AI operations, enabling users to deploy, train, and monitor models across complex environments in real time.
1. Physical Layer & Hardware Abstraction
- Full control and visibility over CPUs, GPUs, Memory, Interfaces, and other compute resources.
- Real-time monitoring and health checks for hardware components.
- Hardware-aware scheduling to optimize performance and resource utilization.
2. AI Cluster Management & Workload Orchestration
- Dynamic cluster allocation for distributed training and inference workloads.
- Intelligent orchestration of workloads across cloud, edge, or on-prem vironments.
- Support for containerized environments using Kubernetes, Docker, and custom runtimes.
3. Live Training & Inference
- Train AI models live on connected clusters — from data ingestion to real-time feedback loops.
- Support for major AI frameworks: TensorFlow, PyTorch, ONNX, etc.
- Continuous training and fine-tuning using edge-generated data or streaming sources.
4. Resource & Performance Optimization
- Automatic resource allocation based on model size, priority, and target deployment.
- Fine-grained control over memory, compute, and network interfaces for optimal throughput.
- Built-in profiling and diagnostics tools for identifying bottlenecks.
5. Developer & Ops Tools
- Visual dashboard for end-to-end system and model observability.
- API and SDK support for integration with CI/CD pipelines and DevOps workflows.
- Role-based access control, audit logging, and policy enforcement for enterprise governance.