docs: add AEGIS classifier GPU utilization note and release notes (Issue #878)

fern/versions/v26.04/pages/about/release-notes/index.mdx

@@ -25,6 +25,12 @@ Upgraded Cosmos-Xenna from 0.1.2 to 0.2.0 with a simplified resource model and i
 - **Cosmos-Xenna**: Updated from 0.1.2 to 0.2.0 with simplified resource model
 - **Ray**: Updated to 2.54
 
+## Improvements
+
+### AEGIS Classifier GPU Utilization (Issue #878)
+
+Confirmed full GPU utilization for the AEGIS safety classifier when running on multi-GPU setups. The AEGIS classifier, which uses the LlamaGuard-7b generative model, properly distributes inference across all available GPUs. Added a performance note to the [classifier documentation](/curate-text/process-data/quality-assessment/distributed-classifier) to set expectations for processing times relative to encoder-based classifiers.
+
 ## Bug Fixes
 
 ### Audio Stage Name Propagation (PR #1470)

fern/versions/v26.04/pages/curate-text/process-data/quality-assessment/distributed-classifier.mdx

@@ -167,7 +167,13 @@ pipeline.add_stage(writer)
 results = pipeline.run()  # Uses XennaExecutor by default
 ```
 
-The classifier adds a column with labels: "safe," "O1" through "O13" (each representing specific safety risks), or "unknown." For raw LLM output, use:
+The classifier adds a column with labels: "safe," "O1" through "O13" (each representing specific safety risks), or "unknown."
+
+<Note>
+The AEGIS classifier relies on the [LlamaGuard-7b](https://huggingface.co/nvidia/Aegis-AI-Content-Safety-LlamaGuard-Defensive-1.0) base model, which is a generative LLM. This makes it significantly slower than the other classifiers in NeMo Curator that use encoder-based models (such as DeBERTa). Full GPU utilization is confirmed when running AEGIS on multi-GPU setups, but expect longer processing times compared to non-generative classifiers due to the autoregressive nature of the underlying model.
+</Note>
+
+For raw LLM output, use:
 
 ```python
 safety_classifier = AegisClassifier(