BentoML

BentoML is a local inference server - The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more.

Overview

🍱 Build model inference APIs and multi-model serving systems with any open-source or custom AI models. 👉 Join our forum!

BentoML is a Python library for building online serving systems optimized for AI apps and model inference.

• 🍱 Easily build APIs for Any AI/ML Model. Turn any model inference script into a REST API server with just a few lines of code and standard Python type hints. - 🐳 Docker Containers made simple. No more dependency hell! Manage your environments, dependencies and model versions with a simple config file. BentoML automatically generates Docker images, ensures reproducibility, and simplifies how you deploy to different environments. - 🧭 Maximize CPU/GPU utilization. Build high performance inference APIs leveraging built-in serving optimization features like dynamic batching, model parallelism, multi-stage pipeline and multi-model inference-graph orchestration. - 👩‍💻 Fully customizable. Easily implement your own APIs or task queues, with custom business logic, model inference and multi-model composition. Supports any ML framework, modality, and inference runtime. - 🚀 Ready for Production. Develop, run and debug locally. Seamlessly deploy to production with Docker containers or BentoCloud.

Install PyTorch and Transformers packages to your Python virtual environment.

Ensure Docker is running. Generate a Docker container image for deployment:

BentoML is open-source, written primarily in Python, with 8,670 GitHub stars under the Apache 2.0 license. The latest release is v1.4.39 (2026-05-07).

Key capabilities

From the project's documentation:

• Computer Vision: YOLO and ResNet
• Workers and model parallelization
• Model loading and Model Store
• Report bugs and "Thumbs up" on issues that are relevant to you.
• Share your feedback and discuss roadmap plans in our forum.

Install

A quick way to get started (always check the official docs for the latest):

pip install -U bentoml

How it fits a local-AI stack

BentoML runs on your own hardware, so pair it with a model and a GPU sized to your needs. Use the VRAM calculator to pick a model that fits your card, and see what you can run for hardware guidance. Related local inference servers in the directory:

• ExLlamaV2
• llama.cpp
• LocalAI
• Ollama

Sources

• Source code & docs: bentoml/BentoML
• Official website: https://bentoml.com

Stats from GitHub, 2026-06-08.