The ACCESS network (Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support) is the NSF-funded successor to XSEDE. Rather than being a single computing system, ACCESS federates HPC, cloud, GPU, storage, and AI resources from many institutions and allows researchers to request allocations through a common process. Texas A&M University (TAMU) is one of ACCESS's major resource providers, contributing several advanced computing systems through its High Performance Research Computing (HPRC) center. 

Compute and GPU Access at Texas A&M Through ACCESS

Texas A&M contributes multiple systems, but the two most important ACCESS resources for AI and accelerated computing are:

• ACES (Accelerating Computing for Emerging Sciences)
• FASTER (Fostering Accelerated Scientific Transformations, Education, and Research)

Researchers obtain access through the ACCESS allocation process rather than a local TAMU account. Allocations are awarded in Service Units (SUs) and are available to U.S.-based academic researchers. 

ACES

ACES is a next-generation accelerator testbed specifically designed for emerging AI and scientific computing workloads. It combines traditional CPUs with a diverse collection of cutting-edge accelerators. 

Key hardware includes:

• NVIDIA H100 GPUs
• NVIDIA A30 GPUs
• Intel Data Center Max (Ponte Vecchio) GPUs
• Intel FPGAs
• Graphcore IPUs
• NEC Vector Engines
• NextSilicon accelerators
• Intel Sapphire Rapids and AMD EPYC processors 

Notable characteristics:

• ~110 nodes
• NVIDIA NDR200 interconnect
• SLURM scheduler
• Composable hardware infrastructure via Liqid fabric
• Designed specifically for GPU and accelerator experimentation
• Classified by ACCESS as a Category II advanced architecture resource 

For AI researchers, ACES is one of the most interesting systems in ACCESS because it provides access to accelerators that are often unavailable on traditional university clusters. 

FASTER

FASTER is an NSF MRI-funded composable supercomputer designed to support GPU-intensive data analysis and AI workflows. Unlike conventional HPC clusters where GPUs are physically attached to specific nodes, FASTER uses Liqid composable infrastructure to dynamically connect GPUs to computational workloads. 

Key features include:

• Approximately 184 Intel Ice Lake compute nodes
• NVIDIA A100 GPUs
• NVIDIA A10 GPUs
• NVIDIA A30 GPUs
• NVIDIA A40 GPUs
• NVIDIA T4 GPUs
• HDR InfiniBand networking
• NVMe-based storage architecture 

A major advantage is the ability to request large numbers of GPUs for a single workflow without being restricted by a node's fixed local GPU count. Researchers can configure CPU and GPU ratios more flexibly than on many traditional clusters. 

Additional TAMU Resources

Texas A&M also operates:

Grace

• TAMU's flagship supercomputer.
• Roughly 6 PFLOPS peak performance.
• Hundreds of CPU nodes plus approximately 100 NVIDIA A100 GPU nodes.
• More than 5 PB of Lustre storage. 

Launch

• Another ACCESS-connected cluster used for production HPC workloads and workforce development activities. 

AI and LLM Capability

ACCESS differs from NRP in an important way.

ACCESS Focus: Compute Allocation

The ACCESS program's primary model is:

• Allocate compute resources.
• Researchers run their own training and inference jobs.
• Users manage software stacks and model deployments.
• Systems are primarily accessed through Linux login nodes and SLURM batch scheduling. 

This resembles traditional HPC more than a cloud-hosted AI service.

AI-Oriented Services on ACES

The newest ACCESS documentation for ACES explicitly identifies:

• AI tools and support
• Model inference services
• Specialized AI hardware
• NAIRR Pilot participation
• GPU-centric research workloads

This indicates that ACES is evolving beyond traditional HPC toward support for AI workflows and inference services. However, ACCESS does not currently provide the broad, centrally managed, OpenAI-compatible hosted-LLM catalog that NRP advertises. Researchers generally deploy or run models themselves on allocated resources. 

Why Researchers Use ACCESS at TAMU

For AI and computational science, TAMU's ACCESS systems are particularly attractive when researchers need:

• Large-scale GPU training
• Access to H100s and cutting-edge accelerators
• Experimental hardware architectures
• High-performance interconnects
• Large-memory scientific computing
• Custom model development and fine-tuning
• Benchmarking across different accelerator technologies 

In contrast, NRP's biggest advantage is that it offers a ready-to-use hosted LLM platform. ACCESS, especially through ACES and FASTER at TAMU, is stronger when researchers need direct control of the hardware and software stack for training, inference, benchmarking, or systems research.