August 31, 2026
We are currently investigating an outage of our Slurm controller on the Palmetto 2 cluster.
Job submission, Open OnDemand, and other tools that integrate with Slurm are impacted.
We will provide an update as soon as a fix is in place.
August 24, 2026
We have a few updates for our LLM Service.
As we prepare for the Fall semester, we’re looking at changes to our largest models. As of now, GLM 5.1 (our previous LTS model) is marked as deprecated, and we will retire it in early September. We’re currently marking GLM 5.2 as our active model, but will likely replace it with GLM 5.3 when the weights are released (depending on the final license).
We’ve also deployed Qwen 3.8 27b, a surprisingly capable smaller model with image support. If there is a particular model that would be useful to your research, please let us know!
A feature we have just added is the concept of model aliases. These aliases can represent a particular capability or use-case, and will track the current best model for the task. Currently, we have the following aliases:
tiger: This alias points to the most powerful model we are currently hosting, useful in coding agents.cub: This model alias points to a smaller agentic capable model.We hope this alias system will alleviate some of the friction caused by the model changes that are inevitable with limited hardware. If there are other use cases you would like to track with an alias, please let us know.
We’ve also introduced a Batch API support over this summer. It is designed to be compatible with the OpenAP Batch API and you can either use the APIs directly (e.g. through OpenAI python SDK as we show in the Quick Start) or through the Batch Web UI. Using the Batch API provides the following benefits:
For more details, please read our documentation.
As part of our enterprise agreement with OpenAI, we have credits available for API access to OpenAI models. We now make a portion of these credits available to projects using the RCD LLM Service. Each allocation is given a default allocation of credits, and then can also consume credits from a shared pool.
Most OpenAI models are available. To see the list, go to our models page, and make sure external models are not hidden. The OpenAI models should appear at the bottom of the list.
Requests can be made to the same endpoint as the open weight models, and you can use the RCD LLM Service Batch API to submit requests to OpenAI models. Since the batching happens at our RCD LLM Service proxy level, you do not directly get the OpenAI batch pricing by submitting to our Batch API, however you can receive the equivalent 50% discount by submitting using the flex service tier in each of your batch requests.
There are currently restrictions on certain API features (e.g. web_search, image_generation). If these pose significant limitations to your research, please let us know. We may be able to lift the restrictions.
For more details, please read our documentation.
There have also been updates to the scheduler, which should keep the models feeling more performant even has load increases by dynamically adjusting the number of requests allowed to the backend based on real time backend metrics. The scheduler still uses a fair-share policy, deciding which requests to send to the backend when it is saturated based on recent usage.
We’ve also introduce a “Nice” configuration option when you create API keys. You can now configure an API key to have a high “Nice” value, which voluntarily deprioritizes and requests using the key. This is useful so that you can use a large Nice value on your batch requests and default low Nice value on your interactive (e.g. coding harness) requests, which would allow your interactive use to have priority over batch requests.
May 12, 2026
We’d like to announce the pilot of our new RCD LLM Service. This service provides no-cost API access to leading open-weight models to all Clemson users.
The service provides access through common API formats including:
These APIs are widely supported by many AI tools and libraries, including coding assistants such as OpenAI Codex, Claude Code, and OpenCode; chat and RAG interfaces such as OpenWebUI and AnythingLLM; and software libraries such as Pydantic AI, LangChain, and OpenAI SDKs.
There are no set rate limits or token quotas. Instead the API has a fair-share scheduling mechanism built in so that if the underlying hardware becomes saturated, requests are held before sending to the underlying engine with priority based on recent usage.
Check out our documentation for details. To get started, you will need to request an allocation in a process similar to Palmetto, then you can register API keys.
As documented in our Acceptable Use Guidelines, any use of the service must remain consistent with Clemson University IT policies, Clemson AI guidance, and any sponsor, contract, or regulatory requirements that apply to your work. Non-academic personal and commercial use is prohibited.
March 14, 2025
The Research Computing and Data (RCD) group in Clemson Computing and Information Technology (CCIT) is seeking interns to support Clemson’s goal to increase research capacity. Interns in this position will learn the basics of high-performance computing and will support researchers making use of advanced cyber infrastructure including the Palmetto 2 Cluster and Indigo Data Lake.
As an RCD intern, you will be responsible for supporting the RCD staff in several areas, including:
As interns gain more knowledge, in addition to the main support role, they will have the opportunity to work on advanced projects. The RCD staff will help match you with project topics areas such as AI/ML, software engineering, HPC software management, bioinformatics, or computational material science.
Ideal candidates should:
For more details on the position, please review our Position Description.
To apply, please use the CCIT Student Employment Application and select “RCD Intern” as the preferred position.
January 9, 2025
The RCDE team is excited to announce our Spring 2025 workshop series, available free to any Clemson University student, faculty, or staff member!
Our series will cover various high-performance computing, machine learning, and software development topics. Here’s a summary of what we’ll cover:
You can learn more about the details of each workshop, the schedule, and registration links on the upcoming live training sessions page of our documentation site.
Use our online registration system to secure your spot!
We look forward to the workshops and hope to see you there. Please submit a support ticket if you have any questions or suggestions.
