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AICR

AICR is a multi-institution AI-focused computing cluster at MGHPCC designed to support large-scale AI and AI-enabled research.

AICR is a partnership between the Commonwealth of Massachusetts and MGHPCC's six member universities: Boston University, Harvard University, MIT, Northeastern University, the University of Massachusetts, and Yale University. The system is shared across the participating institutions and provides large, tightly connected GPU resources that are particularly well suited to distributed, multi-node GPU workloads.

Is AICR Right for My Research?

AICR is a specialized resource intended for experienced researchers with established, large-scale AI/ML workloads that cannot be effectively accommodated by Yale's existing HPC resources.

Yale already provides substantial GPU capacity on its existing HPC systems. Bouchet includes H200, B200, and other GPU resources for low-risk research data, while Hopper provides substantial H200 and B200 GPU capacity for research involving regulated or other high-risk data. Most GPU workloads should continue to run on Bouchet or Hopper, as appropriate for the data involved. This includes single-GPU workloads and many workloads using multiple GPUs within a single node.

AICR is intended primarily for established workloads that have demonstrated the ability to effectively use large GPU allocations and need to scale beyond the resources that can reasonably be provided on Bouchet, particularly for large multi-node GPU jobs. Examples include distributed model training and other AI or AI-enabled workflows that require many GPUs simultaneously.

A Shared Institutional Resource

AICR is shared among multiple universities. Yale receives an institutional share of the system, and usage by Yale researchers collectively affects Yale's scheduling priority.

As a result, heavy AICR usage by one Yale project can affect the scheduling priority of other Yale projects. Yale researchers may be able to use additional resources when capacity is otherwise idle, but this additional capacity is not guaranteed.

For this reason, AICR access is intentionally limited to projects whose computational requirements particularly benefit from the resource. Researchers whose workloads can be effectively supported on Bouchet, Hopper, or other YCRC systems should continue to use those resources.

Data Restrictions

AICR is only approved for low-risk data. Research involving moderate, regulated or other high-risk data should use an approved Yale secure computing environment such as Hopper. If you are unsure whether your data is appropriate for AICR, contact YCRC Research Support before transferring data to the system.

Apply for Access

AICR access for Yale researchers is granted through a biannual internal Yale proposal process, with applications accepted in the Fall and Spring. Applications are screened by the YCRC for technical suitability and viability and then reviewed by a Yale faculty review panel. For approval, a submitted project must meet above stated guidelines, such as established workflows with demonstrated ability to effectively use large GPU allocations, have needs that exceed the resources readily available on other YCRC clusters and involving only low-risk data.

Applications for AICR will open in Fall 2026

An announcement with details on the application requirements will be sent to all active YCRC users when applications are open.

Approved projects are granted access for 12 months, at which point they must reapply to retain access. If a project is not renewed (either not accepted or not resubmitted), the team must remove their data within 30 days, at which point all remaining data will be deleted from access and project team members AICR access will be deactivated to release resources for incoming projects.

AICR User Documentation

This page contains Yale-specific information about using AICR. General documentation for the system, including job submission and software information, is maintained by AICR.

AICR User Documentation

Get Help

Support for Yale researchers on AICR is provided by the YCRC. Users can contact us for assistance with AICR at research.computing@yale.edu and other ways of engaging with YCRC Research Support.

Access the Cluster

Once your project has been approved for AICR access, you can connect using Open OnDemand or SSH.

Your AICR username is your netid followed by _yale, for example: abc123_yale.

AICR Open OnDemand

You can access AICR's Open OnDemand Web Portal at http://ood.aicr.ai. You will authenticate with your Yale netid and password (not your AICR username).

Certificate-signed SSH Keys

AICR uses certificate-signed SSH keys that are generated for you and put into your account. To get these keys you:

  • Login into AICR OOD (see above)
  • Open the File Browser and download the aicr_keys.zip file

To install these keys follow these steps:

# copy keys to local $HOME/.ssh directory
cp aicr_keys/id_ed25519_aicr* $HOME/.ssh/

# fix permissions on private key
cd $HOME/.ssh/
chmod 600 id_ed25519_aicr

# To change passphrase (initial passphrase is in aicr_keys/.passphrase):
ssh-keygen -p -f $HOME/.ssh/id_ed25519_aicr

# Then (needs passphrase):
ssh-add id_ed25519_aicr

#To log in:
ssh aicr_username@login.aicr.ai

Installed Applications

A large number of software and applications are installed on our clusters, including AICR. AICR software are made available to researchers via software modules.

If you need a specific application that is not present, let us know and we can install it for you.

Partitions and Hardware

AICR initially contains 248 B200 GPUs and 152 RTX Pro 6000 Blackwell GPUs, grouped into Slurm partitions meant to serve different purposes. AICR provides several partitions for different workload types, including:

  • cpu: a small CPU-only partition for jobs which do not require GPU cards
  • rtx-devel and b200-devel: interactive access to GPUs, comprised of ~10% of the available cards
  • rtx-batch and b200-batch: standard batch-job partitions for RTX Pro 6000 Blackwell and B200 GPUs
  • b200-fullnode: a full-node B200 partition for jobs requesting 8 or more GPUs in multiples of 8. It is intended for large-memory, multi-GPU, and fabric-bound workloads such as distributed training across multiple GPUs or nodes. This partition is currently being evaluated as part of a four-week trial, and its size and policies may change. Smaller GPU jobs should continue to use the standard B200 or RTX partitions.
  • preemptable: a preemptable partition available to all AICR users. Jobs submitted here may be preempted to make resources available for higher-priority workloads. The default time limit is 15 minutes and the maximum time limit is 24 hours. This partition is most appropriate for workloads that can tolerate interruption and restart.

All nodes feature the same AMD 9575F CPUs with 128 cores (across two sockets). The CPU nodes offer 1TB of RAM, while the GPU nodes all have 2.2TB.

Note, the default job parameters are slightly different than YCRC clusters. The most notable differences are the default time-limit of 15 minutes and the default memory allocation of 1GB.

More information on AICR partitions and associated limits can be found in the AICR Documentation.

Storage & Data

AICR mounts an all-flash, NFS filesystem similar to the Roberts filesystem on Bouchet and the Palmer filesystem on Grace and McCleary.

Visit the official AICR Documentation for transfer instructions.

Allocations

Note: Scratch is a per user directory and quota, not a shared directory like other YCRC clusters.

No storage on AICR is backed up. Self-service snapshots are available for home and work on the main AICR filesystem but do not provide protection against many types of storage failures. We encourage you to back up all critical data to a secondary storage location.

Partition Root Directory Storage File Count Backups Snapshots Notes
home /home/<username> 100GiB/user 500,000 No 7 days
work /work/yale/<project> 1TiB/project* 5,000,000 No 7 days
scratch /scratch/<username> 10TiB/user 15,000,000 No No 30-day purge

* Additional storage quota may be available upon request to the YCRC with appropriate justification.

Last update: October 21, 2026