vLLM

vLLMSince 2023-06

vLLM is the industry-standard open-source LLM serving and inference engine designed for ultra-high throughput and minimal memory waste. Developed by UC Berkeley researchers, vLLM introduced PagedAttention—a revolutionary memory management algorithm that manages attention key-value (KV) cache like virtual memory in operating systems, virtually eliminating memory fragmentation. Capable of delivering 2x to 4x higher throughput than Hugging Face TGI and standard PyTorch runtimes, vLLM powers production AI inference infrastructure across enterprise cloud clusters and high-volume API providers worldwide.

vLLM features state-of-the-art inference optimizations including continuous request batching, Chunked Prefill, speculative decoding, prefix caching, and native quantization support (AWQ, GPTQ, FP8, INT4, SqueezeLLM). It provides drop-in OpenAI-compatible REST API endpoints, supports multi-GPU distributed tensor parallelism with Ray/NCCL, and serves all major model architectures including DeepSeek-V3, Llama 3.3, Mistral, Qwen 2.5, and Command R+.

RunPodSince 2022

RunPod is a leading globally distributed GPU cloud and serverless computing platform engineered specifically for artificial intelligence workloads. It provides developers, AI researchers, and enterprises with on-demand access to top-tier NVIDIA GPUs (including H100, A100, L40S, and RTX 4090) at up to 80% lower cost than traditional legacy hyperscalers.

With RunPod Serverless, developers can deploy production-ready AI endpoints with zero idle server costs, sub-second cold starts, and automated scaling. RunPod also offers pre-configured one-click templates for DeepSeek-R1, vLLM, ComfyUI, Stable Diffusion, Ollama, and PyTorch, making it the premier infrastructure choice for deploying modern open-source models.