Podman

Open-source background jobs and AI workflows for TypeScript

Trigger.dev is an open-source platform for building and deploying background jobs, AI agents, and long-running workflows in TypeScript. It eliminates serverless timeouts with durable task execution, automatic retries, queue-based concurrency control, and elastic scaling. Used by 30,000+ developers at companies like MagicSchool and Icon.com, it processes hundreds of millions of agent runs monthly. Backed by a $16M Series A led by Dalton Caldwell's Standard Capital fund.

Run LLMs as a single portable executable file

Llamafile by Mozilla packages a complete LLM — model weights, inference engine, and OpenAI-compatible API server — into a single executable file that runs on Mac, Windows, Linux, FreeBSD, and OpenBSD with no installation. Built on llama.cpp and Cosmopolitan Libc for cross-platform portability, it delivers GPU-accelerated inference when available and falls back to optimized CPU execution. Supports GGUF models with a built-in web chat UI and REST API for integration.

All-in-one self-hosted AI app with RAG, agents, and multi-user support

AnythingLLM is an open-source, privacy-first AI application that turns any document into an interactive knowledge base. It bundles document ingestion, vector storage (built-in LanceDB), RAG pipelines, AI agents, and multi-user access into a single deployable package. Supports 30+ LLM providers including OpenAI, Anthropic, Ollama, and local models. With 62K+ GitHub stars and MIT license, it runs as a desktop app or Docker container with zero configuration required out of the box.

Container-native local AI model serving with Podman

RamaLama is an open-source tool that containerizes AI model inference using Podman or Docker, eliminating host system configuration complexity. It auto-detects GPUs (NVIDIA, AMD, Intel, Apple Silicon), pulls models from HuggingFace, Ollama, and OCI registries, and runs them in isolated rootless containers with read-only mounts and network isolation. Developed under the Containers project (Red Hat ecosystem), it brings familiar container workflows to local LLM serving.

Heterogeneous CPU-GPU inference and SFT for large MoE models

Open-source framework for running and fine-tuning large Mixture-of-Experts models with heterogeneous CPU-GPU execution, optimized kernels, limited VRAM and SGLang or LLaMA-Factory integrations.

Official Kubernetes and Helm reference stack built on the vLLM inference engine

Official vLLM reference implementation for scaling the existing inference engine on Kubernetes with Helm, request routing, KV-cache offload, autoscaling and Prometheus/Grafana observability.

Distributed inference orchestration above vLLM, SGLang and TensorRT-LLM

Open-source, datacenter-scale orchestration layer that coordinates vLLM, SGLang and TensorRT-LLM across nodes with disaggregated serving, KV-aware routing, multi-tier cache management and automatic scaling.

Open-source GPU control plane for scalable AI model serving

Open-source GPU cluster manager that configures vLLM, SGLang, TensorRT-LLM or custom engines, serves models through compatible APIs, and provisions SSH-accessible GPU instances across on-premises, Kubernetes and cloud environments.

Disaggregated KV cache storage and transfer for LLM serving

Open-source infrastructure for disaggregated LLM serving that pools KV caches across prefill and decode workers, with high-performance transfer, distributed storage and integrations for vLLM and SGLang.

Reusable KV cache infrastructure for scalable LLM inference

Open-source KV cache management layer that persists, offloads and reuses model key-value caches across requests and serving engines to reduce repeated prefill work and improve inference throughput.