# What is vLLM? Features, integrations and alternatives > Source: https://netray.co/software/vllm > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Manufacturing Software Encyclopedia ## Short answer vLLM is an open-source inference and serving engine for large language models, built to get high throughput from GPUs through techniques such as PagedAttention and continuous batching. It exposes an OpenAI-compatible HTTP server, so applications written for hosted APIs can point at a model running on your own hardware. Manufacturers use it to serve open-weight models privately, on-prem or in a sovereign cloud. ## Vendor vLLM project ## Category ai-infrastructure ## Aliases - vLLM inference engine - vllm serve ## Overview vLLM started as a research project at UC Berkeley and is now a community open-source project under an Apache-style license. Its core idea is to manage the key-value cache of a running model in paged blocks, which reduces wasted GPU memory and lets the server batch many concurrent requests. In practice this means one GPU node can serve several users or agents at once instead of one prompt at a time. It loads open-weight models from common model hubs, including popular families such as Llama, Qwen, Mistral, Gemma and DeepSeek class models, subject to each model's own license. It runs as a server process (typically in a container) and speaks an OpenAI-compatible API for chat and completions, plus embeddings for supported models. Tensor and pipeline parallelism let a large model span several GPUs, and quantized checkpoints reduce memory needs. In a manufacturing stack, vLLM is infrastructure, not an application. It sits behind a retrieval layer, an agent framework or an ERP assistant and answers the model calls. It matters most where data cannot leave the plant or the company network, such as controlled technical data in aerospace and defense, or customer drawings under NDA. It needs GPU capacity planning and someone who can operate it. ## Best For - Manufacturers that need private LLM inference on their own GPUs or in a sovereign cloud - Teams serving many concurrent users or agents against one model, where throughput matters - Aerospace, defense and electronics firms that keep controlled data inside their boundary - IT groups with Linux, container and GPU skills who want an OpenAI-compatible endpoint - Integrators building ERP assistants, document search and agents on open-weight models ## Modules ### OpenAI-compatible server **Description:** An HTTP server that exposes chat completions, completions and, for supported models, embeddings, so existing client libraries work with a changed base URL. ### PagedAttention and KV cache management **Description:** Manages attention cache in paged blocks to cut memory waste and allow larger effective batch sizes on a given GPU. ### Continuous batching **Description:** Adds and removes requests from a running batch as they arrive and finish, improving GPU utilization under mixed load. ### Parallelism **Description:** Tensor and pipeline parallelism to split larger models across several GPUs, and across nodes in supported setups. ### Quantization support **Description:** Runs a range of quantized model formats to fit larger models on less memory; supported formats vary by version and hardware. ### LoRA adapter serving **Description:** Can serve fine-tuned LoRA adapters on top of a base model, useful for per-department or per-customer tuning. ### Structured output **Description:** Guided decoding options to constrain output to JSON schemas or grammars, which helps agent tool calls and ERP queries. ### Metrics and observability **Description:** Exposes server metrics that can be scraped into standard monitoring stacks. ## Deployment Self-hosted: run as a container or Python process on Linux GPU servers, on-prem, in air-gapped networks or in a private cloud. Most deployments target NVIDIA GPUs; support for other accelerators exists and varies by release. ## Integrations ### OpenAI-compatible API **Detail:** Any client or framework that can target an OpenAI-style endpoint, including LangChain, agent frameworks and custom code, can use vLLM by changing the base URL. ### Model sources **Detail:** Loads weights from a model hub or a local directory, which is how air-gapped sites stage approved models behind the firewall. ### ERP and RAG layers **Detail:** An ERP assistant or retrieval service sends prompts and retrieved context to vLLM; the ERP itself is accessed through its own APIs or a read-only database layer. ### Containers and orchestration **Detail:** Commonly deployed as a container image under Docker or Kubernetes, with GPU scheduling handled by the cluster. ### Monitoring **Detail:** Prometheus-style metrics endpoints feed existing dashboards and alerting. ### Gateways and auth **Detail:** Authentication, rate limits and audit logging are usually added by a reverse proxy or API gateway in front of the server. ## Strengths - High throughput under concurrent load compared with simple single-request runners - OpenAI-compatible API keeps application code portable between hosted and private models - Active open-source community and fast support for new open-weight model families - Scales from one GPU to multi-GPU and multi-node serving - No per-token fee for inference you run yourself, beyond hardware and operations ## Limitations - It is an engine, not a product: you still need retrieval, security, UI and monitoring around it - GPU sizing, driver versions and model-specific settings need real operational skill - Release pace is fast, so pinning versions and testing upgrades matters in regulated environments - Hardware support is strongest on NVIDIA GPUs; other platforms vary - Out-of-the-box access control and audit logging are minimal and must be added ## Alternatives - Ollama - NVIDIA NIM - Hugging Face Text Generation Inference - llama.cpp server - TensorRT-LLM - SGLang ## Ai Opportunities - Serving a private model that answers natural-language questions over ERP data without sending records to a public API - Powering agents that read work orders, BOMs and quality records and draft actions for human approval - Running embeddings and rerankers for private search across drawings, SOPs and supplier documents - Serving LoRA adapters tuned on a company's own terminology, part numbering and report formats - Batch jobs such as classifying nonconformance text or summarizing maintenance logs overnight on idle GPUs ## Selection Tips - Size GPU memory for the model plus KV cache at your expected concurrency, not just the weights - Test with your real prompts and context lengths; ERP and document prompts are often long - Confirm each model's license permits your commercial and export-controlled use - Put authentication, logging and rate limits in a gateway before exposing the endpoint - Plan how approved model files reach an air-gapped network and how you will verify them - Compare against NIM or TGI if you want vendor support or a different operating model ## Faq ### What is vLLM used for? **Answer:** vLLM is used to serve large language models efficiently on GPUs. Teams run it so applications such as chat assistants, agents and document search can call a model through an OpenAI-compatible API, while the model and the data stay on infrastructure the company controls. ### Is vLLM better than Ollama? **Answer:** They solve different problems. vLLM targets multi-user, high-throughput serving on server GPUs. Ollama targets easy local use on a workstation or small server. For a pilot or a single user Ollama is often simpler; for shared production serving vLLM is usually the stronger fit. ### Can vLLM run in an air-gapped network? **Answer:** Yes. Once the software and approved model files are staged inside the network, vLLM does not need internet access to serve requests. You must plan for transferring and verifying weights, container images and updates through your own controlled process. ### Does vLLM connect directly to an ERP? **Answer:** No. vLLM only runs the model. A separate layer, such as a retrieval service or agent, queries the ERP through its APIs or a read-only database view and sends the results to vLLM as context. That layer also enforces roles and approvals. ### What hardware does vLLM need? **Answer:** Most production use is on Linux servers with NVIDIA GPUs. Required memory depends on the model size, quantization and concurrent users. Small models can run on a single GPU, while larger models need several GPUs. Check current documentation for supported accelerators. ## Search terms what is vllm, vllm alternatives, vllm vs ollama, vllm api, vllm openai compatible server, vllm on prem, vllm air gapped, vllm vs tgi, vllm for manufacturing, private llm inference server --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json