AI4RA Tip of The Week #15

What is the MindRouter Cluster Configurator? 
One of the most common questions we hear from institutions exploring Vandalizer deployment is “What hardware do we actually need?” and the Cluster Configurator addresses exactly that. The MindRouter Cluster Configurator is a free, interactive planning tool developed by Luke Sheneman, AI4RA Co-PI and Director of Research, Data, and Computing Services at the University of Idaho. It helps institutions size an on-premise, AI inference cluster based on their actual workload, ranging from a single NVIDIA DGX Spark all the way up to a cluster of HGX B300s. 

The MindRouter Cluster Configurator works like this: begin by describing the workload by adjusting a set of parameters––number of total users, concurrent chat users, API request volume, token throughput, data retention needs, and the level of AI model intelligence required relative to today’s open-weight frontier. The tool then recommends a hardware build and provides estimated cost ranges and power consumption estimates. 

The configurator is a planning tool only. The AI4RA team sells nothing and endorses no specific vendor. Hardware and model references (NVIDIA, Supermicro, and others) are examples used to anchor cost and power estimates. Comparable equipment from other vendors works equally well. 

Why This Matters for Research Administrators 
As we discussed in a previous tip, Vandalizer is only as good as the AI and OCR infrastructure powering it. Institutions that deploy it on underpowered hardware with inadequate models will face disappointing results, and may conclude the tool underperforms, when the real issue is the infrastructure underneath it. Having a concrete, defensible hardware estimate is essential when making the case to institutional leadership, IT departments, or budget committees for an AI deployment investment. 

How to Use This Resource 

1. Open the configurator at https://mindrouter.ai/configurator.html.
2. Drag the radar handles to describe your workload. The tool includes four preset profiles (Starter Lab, Department, Campus, and Frontier) as a starting point. Parameters interact with each other: raising user demand lifts
3. the budget floor, while pulling budget down trades away model intelligence first, then throughput and concurrency.
4. Fine-tune exact values using the detailed controls below the radar chart if you need more precision.
5. Review your recommended build. The output includes a hardware specification, estimated cost range, and power consumption estimate. You can copy the spec sheet directly for use in planning documents or budget requests. 

If your institution configures down to a single DGX Spark, the companion DGX Stack repository (https://github.com/ui-insight/dgx-stack) provides a ready-to-run vLLM and OCR deployment for that machine, with optional MindRouter integration. 

How can I learn more about this topic?   
To use the Cluster Configurator visit https://mindrouter.ai/configurator.html
To learn more about Mindrouter visit https://mindrouter.ai/
If you have any questions, contact us using the Mindrouter contact page at https://mindrouter.ai/#contact or attend the AI4RA Technical Office Hours occurring on the second Tuesday of every month with the next one occurring on September 8th 11:00AM PDT (visit https://ai4ra.uidaho.edu/events/ for the full schedule). 

Share your questions or success stories  
Post them to the AI4RA community forum (https://groups.ai4ra.uidaho.edu/g/main/topics). Your tips help the whole research‑admin community thrive.