Eagle Mountain Data provides a managed AI‑first edge computing platform that combines GPU clusters, NVMe‑optimized storage, and sub‑millisecond networking to support large‑scale model training and real‑time inference at edge locations. The service offers API‑driven provisioning, automated health monitoring, and integrated ML tooling, reducing latency and operational overhead for enterprise AI teams and research labs.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises and AI research labs face high latency and bandwidth constraints when moving large AI models from centralized data centers to edge locations, limiting real‑time inference and increasing operational costs. Scaling GPU‑intensive training while maintaining consistent performance across distributed sites is also complex and resource‑heavy.
Solution
Eagle Mountain Data delivers an AI‑first edge computing platform that unifies high‑performance GPU compute, purpose‑built storage, and low‑latency networking into a single managed service. The platform supports the full AI lifecycle—from massive distributed training to sub‑millisecond edge inference—by colocating compute resources close to end‑user data. Integrated management tools provide automated cluster health monitoring, observability, and security, reducing operational overhead. Developers can provision resources on demand via APIs, while built‑in analytics and ML tooling accelerate model deployment and scaling. The decentralized architecture ensures consistent performance across global edge sites, eliminating traditional latency bottlenecks.
Target Audience
Primary customers are AI research laboratories, enterprise AI teams, and technology providers that require high‑performance training and real‑time edge inference for applications such as autonomous systems, VFX rendering, and mission‑critical analytics.
Features
- BlinkAI GPU compute clusters with multi‑node NVidia A100/A800 support for large‑scale model training
- Store20 high‑throughput, NVMe‑optimized storage tier designed for AI data pipelines
- LitePulse low‑latency networking fabric delivering sub‑1 ms inter‑node communication
- Swift IQ managed services suite for automated provisioning, patching, and cost optimization
- TriCore cluster health dashboard with real‑time metrics, anomaly detection, and auto‑remediation
- LumaCore platform layer offering observability, role‑based access control, and integrated ML tooling
- Edge AI‑Factories with micro‑qubit quantum‑enhanced nodes for ultra‑fast inference at the edge
- 0.68 ms average inference latency and up to 100× faster inference throughput compared to traditional cloud deployments