by Rounak Bende | Oct 21, 2026 | AI, Developer Productivity
Coding agents promise to revolutionize software development, but how do we know if they’re any good at the kind of work enterprise teams do every day? SWE-bench, a benchmark for evaluating large language models (LLMs) on real-world software issues, established...
by Ryan Malone, Maryam Tahhan, Evgenii Dushkin | Oct 15, 2026 | AI
Evaluating local large language model (LLM) performance before scaling to enterprise production doesn’t require complex infrastructure. With Red Hat Enterprise Linux (RHEL) and Red Hat AI Inference, you can launch, benchmark, and test an OpenAI-compatible server...
by Maryam Tahhan, John Harrigan, Anton Ivanov, Evgenii Dushkin, Ryan Malone | Oct 6, 2026 | AI
What if your standard-issue CPU infrastructure could serve as a hub for enterprise AI? With the right tools, it can. You can deploy a large language model on CPU inside a container on Red Hat Enterprise Linux (RHEL), query it via an OpenAI compatible REST API, and...
by Evgenii Dushkin, Ryan Malone, Maryam Tahhan | Sep 28, 2026 | AI
Deploying large language models (LLMs) on enterprise hardware requires a balance of reproducibility, security, and performance. Accurately benchmarking model performance under production-like workloads is both challenging and crucial for optimizing infrastructure...
by Michael Peters | Aug 26, 2026 | AI, Trust
A primary reason many AI pilot projects fail to reach production is lack of confidence in the security of the systems. One of the most common and pernicious security issues affecting generative AI (GenAI) systems is the prompt injection attack—now listed by OWSAP as...
