OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Infrastructure & Serving

Vllm

Vllm is an open-source, Python-based inference and serving engine for large language models, focused on high throughput and memory efficiency.

Independent OpenViST project profile, based on linked sources.

Vllm — OpenViST project profile in AI Infrastructure & Serving.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Vllm when an LLM deployment needs a dedicated serving layer and throughput or memory efficiency are important considerations.

Who it is for

  • Teams deploying LLM-backed applications
  • Engineers building model-serving and inference infrastructure
  • Organizations operating self-hosted or cloud-based language-model services
  • Developers working with server and GPU inference environments

Documented uses

  • Run inference for large language models
  • Expose models through a high-throughput serving endpoint
  • Build a self-hosted LLM inference service
  • Provide an internal platform or application backend for served language models
Before you choose

Requirements & limits

The project is publicly available as open-source software under the Apache-2.0 license. The evidence does not specify hosted pricing or account requirements.

  • Whether the deployment requires self-hosted or server-side model serving
  • The importance of throughput and memory efficiency for inference workloads
  • Compatibility with the intended Linux, cloud or GPU environment
  • Whether a Python-based infrastructure component fits the application stack

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
VllmRun inference for large language modelsCloud, Linux, Self-hosted, ServerThe project is publicly available as open-source software under the Apache-2.0 license. The evidence does not specify hosted pricing or account requirements.
MorphingdbImport a libtorch model and create predictions from values in a PostgreSQL table.Docker, Linux, Self-hosted, ServerThe README documents a Docker image and a build-from-repository option, followed by connecting to PostgreSQL and enabling the pgdl extension.
Gateway — pyadavSend chat-completion requests to a provider through a common server endpoint.ServerThe repository is public and identifies the project as Apache-2.0 licensed. README installation instructions include binaries, platform package managers and Docker.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
30 September 2026
Original website title
vllm-project/vllm · Official homepage title · retained in its source language
How we review sourcesSuggest a correction