Emerging Technologies Focus: AI
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Red Hat enterprise-specific benchmarks for coding agents: Why we built RH SWE-bench
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 the...
Enterprise AI on your machine: Local inference and benchmarking with Red Hat AI Inference
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 on...
From zero to benchmark: Deploying LLM inference on CPUs with Red Hat AI Inference
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...
Benchmarking enterprise LLMs on AMD GPUs with Red Hat AI Inference and Podman
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...
Beyond guardrails: mitigating prompt injection attacks
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...