How Docker Model Runner integrates vLLM as an inference backend, letting developers run safetensors models with high-throughput serving, PagedAttention, streami
The Industry-Leading Container Runtime | Docker
Read about our industry-leading Docker container runtimes. Docker Engine allows containerized apps to run consistently on any infrastructure — from anywhere.
Docker Blog | Docker
The 2026 Stack Overflow Developer Survey shows only 7% of developers trust AI output for important decisions. Here’s what that means for running agents safely.
Docker Blog | Docker
The 2026 Stack Overflow Developer Survey shows only 7% of developers trust AI output for important decisions. Here’s what that means for running agents safely.
Docker: Accelerated Container Application Development
Docker is a platform designed to help developers build, share, and run container applications. We handle the tedious setup, so you can focus on the code.
Post: Docker Not Ready for Prime Time
I love Docker. It's so freakin' useful for distributing predictable runtime environments.
Docker Sandboxes: Run Agents in YOLO Mode, Safely | Docker
Learn from Docker experts to simplify and advance your app development and management with Docker. Stay up to date on Docker events and new version
Docker Brings Compose to the AI Agent Era | Docker
Define, run, and scale AI agents using Docker Compose and Docker Offload. Streamline agentic development across your stack.
Apptainer - Portable, Reproducible Containers
Apptainer is an open source container platform designed to run complex applications on high-performance computing (HPC) clusters in a simple, portable, and reproducible way.
vllm-project/vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
Cloud native Go: building reliable services in unreliable environments
What do Docker, Kubernetes, and Prometheus have in comm…
Compiling Models to Megakernels
Fine-grained synchronization, deep pipelines, and zero kernel launch overheads, automatically.
Your agent needs a computer, not a container — introducing @cloudflare/computer
Agents need more than just a container to scale. We're introducing @cloudflare/computer, an agent runtime that dynamically orchestrates between fast, efficient isolates and full Linux containers to give every agent a computer of its own.
Build Bigger With Small Ai: Running Small Models Locally
An Internet for the KV Cache: Rethinking Classical Infrastructure Boundaries in the LLM Inference Age
LLM inference has become a global-scale, heterogeneous workload spanning agents, retrieval, tool-use, code execution and multi-modal reasoning. These workloads naturally enable context reuse from overlapping inputs, creating a major opportunity to store and reuse the contexts' KV Caches instead of recomputing them. However, model-side advances that shrink the KV Cache and system-side advances that reduce compute, storage, and transfer costs are evolve independently within legacy cloud boundaries. We argue that future inference infrastructure should allow decoupling of compute and KV Cache storage across cloud and datacenters. The network becomes an active distribution channel; bandwidth, latency and pricing directly determines how the KV Cache should be managed. We propose a vision for an Internet for the KV Cache, with KV Cache management working as a content-distribution system. In this view, KV Cache storage and recompute decisions are driven by model, infrastructure, and application metrics, to enable adaptive, content-driven decisions for minimizing latency and cost.
Demystifying llm-d and vLLM: The race to production
Learn how vLLM and llm-d work together for efficient and scalable large language model (LLM) inference. Discover the benefits of disaggregated scaling, expert-parallel scheduling, and KV cache-aware routing.
