Enterprise AI on your machine: Local inference and benchmarking with Red Hat AI Inference

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...

Benchmarking enterprise LLMs on AMD GPUs with Red Hat AI Inference and Podman

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...

Benchmarking AI inference on CPUs: A transparent blueprint for the enterprise

by Maryam Tahhan, John Harrigan, Anton Ivanov, Paul Power, Luigi Mario Zuccarelli | May 28, 2026 | AI

As enterprises look to optimize the total cost of ownership (TCO) of Large Language Model deployment, utilizing existing enterprise CPU infrastructure alongside GPU resources for specific inference workloads has become a strategic initiative. However, infrastructure...

Protecting Triton kernel deployments with cryptographic signatures

by Anton Ivanov, Maryam Tahhan | Feb 5, 2026 | AI

Triton is a domain-specific language and compiler for writing high-performance GPU kernels (snippets of compiled GPU code) using a Python-like syntax. It offers fine-grained control over memory and parallelism, making it ideal for custom, architecture-optimized...

Skip the JITters: Fast, trusted model kernels with OCI caching

by Maryam Tahhan | Jan 29, 2026 | AI

Triton is a domain-specific language and compiler for writing high-performance GPU kernels in Python. It offers fine-grained control over memory and parallelism, making it ideal for custom, architecture-optimized compute in machine language and  high-performance...