AI InfrastructureVendor: vLLM project Agent view

What is vLLM? Features, integrations and alternatives

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.

Also known as: vLLM inference engine, vllm serve

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.

Modules and capabilities

OpenAI-compatible server

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

Manages attention cache in paged blocks to cut memory waste and allow larger effective batch sizes on a given GPU.

Continuous batching

Adds and removes requests from a running batch as they arrive and finish, improving GPU utilization under mixed load.

Parallelism

Tensor and pipeline parallelism to split larger models across several GPUs, and across nodes in supported setups.

Quantization support

Runs a range of quantized model formats to fit larger models on less memory; supported formats vary by version and hardware.

LoRA adapter serving

Can serve fine-tuned LoRA adapters on top of a base model, useful for per-department or per-customer tuning.

Structured output

Guided decoding options to constrain output to JSON schemas or grammars, which helps agent tool calls and ERP queries.

Metrics and observability

Exposes server metrics that can be scraped into standard monitoring stacks.

Integrations and data access

OpenAI-compatible API

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

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

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

Commonly deployed as a container image under Docker or Kubernetes, with GPU scheduling handled by the cluster.

Monitoring

Prometheus-style metrics endpoints feed existing dashboards and alerting.

Gateways and auth

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

Where AI helps vLLM users

- 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

Evaluating vLLM? Check these

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

Common alternatives: Ollama, NVIDIA NIM, Hugging Face Text Generation Inference, llama.cpp server, TensorRT-LLM, SGLang.

Frequently asked questions

What is vLLM used for?

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?

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?

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?

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?

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.

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