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GPU cloud platform for AI training and inference

RunPod is a GPU cloud platform providing on-demand and serverless GPU compute for AI training and inference workloads. It offers NVIDIA A100, H100, and RTX GPUs with per-second billing, serverless inference endpoints with auto-scaling, persistent storage, and Docker-based deployment. Popular with AI developers for its competitive pricing, fast provisioning, and developer-friendly API for deploying ML models at scale.

Open-source control plane for AI workloads across multi-cloud GPU infrastructure

dstack is an open-source platform that orchestrates AI training and inference workloads across heterogeneous GPU infrastructure spanning multiple clouds, Kubernetes clusters, and bare-metal servers. It abstracts away cloud-specific APIs so teams define GPU requirements declaratively and dstack automatically provisions the cheapest available resources from AWS, GCP, Azure, Lambda, or on-premises hardware.

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.