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AI Grid

Linker AI Resource Fabric™ (Core Platform)

Most research institutions and governments have already invested heavily in GPUs, but those GPUs are not managed as one unified resource — each unit builds its own AI Lab, Digital Twin Lab, HPC cluster, AI research center, or simulation center, creating 'GPU islands' that sit idle, get duplicated, and leave applications waiting. Linker AI Resource Fabric consolidates these scattered resources into one unified AI infrastructure so compute flows to where applications need it — like a power grid dispatching electricity, the AI Grid dispatches compute.

Customer challenges

1

Low GPU utilization — typically 20–35%; peaks fall short while off-peak sits idle, stranding capital

2

Continuous expansion but persistent shortage — more GPUs and data centers every year, yet demand still queues; the problem is not a lack of GPUs but a lack of unified scheduling

3

Applications disconnected from compute — today resources follow the organization (Organization → GPU); they should follow the application (Application → GPU)

4

Token economics out of control — in the GenAI era inference cost overtakes training cost, driving up token cost, GPU cost, and unpredictable opex

Customer benefits

GPU utilization from ~20% to 70%+

Token cost reduced 30–60% (workload-dependent)

CAPEX deferred 1–3 years

More AI output from the same hardware investment

Key features

Unified AI Resource Pool

integrates Data Center, HPC Cluster, DGX Pod, and Edge AI Cluster into a single pool

Application-Aware Scheduling

recognizes workload type (inference, training, digital twin, simulation, agentic AI, robotics) and allocates accordingly

Dynamic GPU Dispatch

real-time GPU dispatch across departments, campuses, cities, and countries

OpenClaw Agent Engine

autonomous AI-agent scheduler that continuously analyzes utilization, queue, SLA, and token consumption, forecasts demand, and pre-allocates compute

Multi-Tenant AI Fabric

multiple units share GPU, CPU, storage, and models with permission isolation

Before & after

Before GPU islands: Institute A DGX cluster 25%, Institute B DGX cluster 35%, Institute C DGX cluster 40% — average utilization about 33% After — with Linker AI Nexa AI Resource Fabric: all resources unified under one scheduling platform, average utilization 75%+; no new GPU purchases needed, lower queue time, faster model output