Developer Advocate
Cedric Clyburn
Cedric Clyburn (@cedricclyburn), Senior Developer Advocate at Red Hat, is an enthusiastic software technologist with a background in Kubernetes, DevOps, and container tools. He has experience speaking and organizing conferences including DevNexus, WeAreDevelopers, The Linux Foundation, KCD NYC, and more. Cedric loves all things open-source, and works to make developer's lives easier! Based out of New York.
Cedric Clyburn's contributions
Article
Implement GPU-as-a-Service with Kueue and NVIDIA MIG
Learn how to implement GPU-as-a-Service on Red Hat OpenShift using Kueue, NVIDIA MIG, and a custom dashboard plug-in for self-service GPU resource booking.
Article
llama.cpp vs. vLLM: Choosing the right local LLM inference engine
Learn when to use llama.cpp and vLLM for local inference of large language models (LLMs). Discover the key differences, benchmarks, and use cases for each engine.
Article
Model-as-a-Service: How to run your own private AI API
Learn how Model-as-a-Service (MaaS) solves the problem of managing AI costs, security, and models for every developer in an organization.
Blog
Learn to optimize, deploy, and benchmark LLMs with vLLM: A New Free Course
Red Hat and DeepLearning.AI have released a free hands-on course on the full LLM
Article
MCP servers vs. skills: Choosing the right context for your AI
Learn how to extend your large language model's capabilities with Model Context Protocol (MCP) servers and skills.
Article
Build resilient guardrails for OpenClaw AI agents on Kubernetes
Learn how to build security hygiene into OpenClaw by using containers for isolation, role-based access control (RBAC) for user access permissions, and secrets for sensitive information. This article explores how to use infrastructure powered by open source technology to help protect these workflows.
Article
Building effective AI agents with Model Context Protocol (MCP)
Learn how Model Context Protocol (MCP) enhances agentic AI in OpenShift AI, enabling models to call tools, services, and more from an AI application.
Article
The state of open source AI models in 2025
Discover 2025's leading open models, including Kimi K2 and DeepSeek. Learn how these models are transforming AI applications and how you can start using them.
Implement GPU-as-a-Service with Kueue and NVIDIA MIG
Learn how to implement GPU-as-a-Service on Red Hat OpenShift using Kueue, NVIDIA MIG, and a custom dashboard plug-in for self-service GPU resource booking.
llama.cpp vs. vLLM: Choosing the right local LLM inference engine
Learn when to use llama.cpp and vLLM for local inference of large language models (LLMs). Discover the key differences, benchmarks, and use cases for each engine.
Model-as-a-Service: How to run your own private AI API
Learn how Model-as-a-Service (MaaS) solves the problem of managing AI costs, security, and models for every developer in an organization.
Learn to optimize, deploy, and benchmark LLMs with vLLM: A New Free Course
Red Hat and DeepLearning.AI have released a free hands-on course on the full LLM
MCP servers vs. skills: Choosing the right context for your AI
Learn how to extend your large language model's capabilities with Model Context Protocol (MCP) servers and skills.
Build resilient guardrails for OpenClaw AI agents on Kubernetes
Learn how to build security hygiene into OpenClaw by using containers for isolation, role-based access control (RBAC) for user access permissions, and secrets for sensitive information. This article explores how to use infrastructure powered by open source technology to help protect these workflows.
Building effective AI agents with Model Context Protocol (MCP)
Learn how Model Context Protocol (MCP) enhances agentic AI in OpenShift AI, enabling models to call tools, services, and more from an AI application.
The state of open source AI models in 2025
Discover 2025's leading open models, including Kimi K2 and DeepSeek. Learn how these models are transforming AI applications and how you can start using them.
