vllm

VLLM is a high‑throughput, memory‑efficient inference engine that targets large language models (LLMs). The web pages claim that the system can deploy AI models faster, with a focus on speed, cost efficiency, and broad hardware support. It presents itself as a drop‑in replacement that provides an OpenAI‑compatible API, making it easy for developers to add LLM inference to existing services.

The engine ships with a paged attention implementation that stores the key–value cache in a low‑precision format. In practice, this means that when a model generates text, vLLM keeps the KV cache in a smaller data type, freeing GPU memory and allowing more tokens to be processed per second. Paging also allows a single GPU to hold multiple model instances simultaneously; each instance can produce multiple prompts in parallel. To use the feature you install vLLM, pick a model from the supported list, and launch a server. The server exposes a standard REST endpoint; you send a JSON payload with a `"prompt"` string and receive a `"completion"` string in response. Pagination states that the system can also handle larger contexts, such as GLM‑5.3 with 1‑million‑token contexts, by using a Hybrid HiSparse decode that streams tokens as they are produced.

VLLM also incorporates an advanced scheduler and continuous batching mechanism. When multiple incoming requests arrive, the engine groups them into a single batch that runs on the GPU. This reduces idle time and maximizes GPU utilization, which is useful for workloads that need many short requests. The document describes using the engine on a variety of hardware: NVIDIA CUDA GPUs (e.g., Tesla V100, A100, H100), AMD ROCm GPUs, Intel XPU, Huawei Ascend NPUs, Google Cloud TPUs, and even CPU and Apple Silicon. Users who already run Hugging Face models on these devices can replace their inference code with vLLM without changing the model weights.

VLLM is framed as “easy, fast, and cost‑efficient” for everyone, but the material emphasizes that the benefits are most pronounced when using high‑performance GPUs and deploying models that require large contexts. The listing does not provide any pricing information or commercial plans, and it gives no official benchmarks for CPU‑only deployments.