Docker Model Runner uses llama.cpp's KV cache for automatic token caching, eliminating redundant prompt processing in local LLM deployments. Discover how this built-in optimization works.
Designed to reduce vulnerabilities and simplify compliance, DHIs integrate easily into your existing Docker-based workflows as well as Kubernetes deployments with little to no retooling required.
Frustrated by tiny context windows when you know your model can handle so much more? If you're running llama.cpp through Docker Model Runner and hitting that annoying 4096 token wall, there's a simple fix you need to know about. Your model isn't the problem—your configuration is.
Post-GITEX, 27 innovators gathered at Coders HQ for the Agentic AI and Security Meetup. The topic? "Agentic AI and Docker." But it was the questions that revealed the real story—engineers already deploying custom models, architects rethinking infrastructure for multi-LLM systems.
Master multi-agent AI workflows with simple YAML configurations—no programming required
Join Docker at GITEX Global 2025—the world's largest tech event—to discover how Docker Hardened Images are revolutionizing container security. With 95% less attack surface, enterprise SLA-backed patching, and built-in compliance, DHI proves security doesn't have to slow you down.
As AI systems evolve from simple chatbots to autonomous agents with memory, tools, and the ability to collaborate, they're creating security vulnerabilities we've never seen before. This deep dive into cutting-edge arXiv research explains why agentic AI is your biggest security challenge.
Your winning strategy for $15,000 in prizes and career opportunities
Vibe coding is the new rhythm of software: start with a fuzzy idea, throw a prompt at an AI, and—boom—a demo runs. The catch? Creation is instant; correctness isn’t. This post unpacks that paradox.
The future of AI isn't about finding the perfect model—it's about orchestrating the right models for the right tasks.
Docker Compose now supports AI models as first-class citizens with the new models top-level element. Adding machine learning capabilities to your applications is now as simple as defining a model and binding it to your services.
The MCP Gateway is Docker’s answer to the growing complexity and security risks of connecting AI agents to MCP servers. By aggregating multiple MCP servers behind a single, secure interface, it gives developers and teams a consistent way to build, scale, and govern agent-based workloads.
