OpenLLM
FreeAn open platform for operating large language models (LLMs) in production. Fine-tune, serve, deploy, and monitor any LLMs with ease using OpenLLM. 
FreeFree tier
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About OpenLLM
OpenLLM is an open-source platform from BentoML that enables developers to run any open-source large language models (LLMs) as OpenAI-compatible API endpoints with a single command. It supports state-of-the-art models like Llama 3.3, Qwen2.5, Gemma, DeepSeek, and many more, offering a built-in chat UI and optimized inference backends. OpenLLM simplifies deployment to production environments using Docker, Kubernetes, or BentoCloud, making it ideal for self-hosting and integrating LLMs into applications.
Key Features
Run any open-source LLMs as OpenAI-compatible APIs with a single command
Supports a wide range of models: Llama 3.3, Qwen2.5, Gemma, Phi, DeepSeek, Mistral, and more
Built-in chat UI for interactive testing
State-of-the-art inference backends for high performance
Simplified workflow for cloud deployment via Docker, Kubernetes, and BentoCloud
Custom model support through model repository
Free and open source under the BentoML ecosystem
Pros & Cons
Pros
- Extremely easy to get started: one command to serve a model
- Open source and free to use with no licensing costs
- Compatible with OpenAI API format, enabling drop-in replacement in existing workflows
- Supports a large and growing list of open-source LLMs
- Designed for both development and production deployment scenarios
Cons
- Requires significant GPU memory for larger models (e.g., 80GB for Llama3.3 70B)
- Hugging Face token needed for gated models like Llama
- Not a full managed service; users must handle infrastructure and scaling themselves
- Documentation and community support primarily through GitHub and not dedicated docs site
Best For
Self-hosting LLMs for privacy and controlDeploying LLMs as OpenAI-compatible API endpointsIntegrating LLMs into existing applications using standard OpenAI client librariesEnterprise-grade LLM deployment with Docker and KubernetesBuilding internal chatbots or AI assistants with a custom chat UI
FAQ
How do I start an LLM server with OpenLLM?
Use the command `openllm serve <model>:<version>`, for example `openllm serve llama3.2:1b`. The server will be accessible at http://localhost:3000 with OpenAI-compatible endpoints.
What models are supported in OpenLLM?
OpenLLM supports many open-source models including DeepSeek R1, Gemma 2/3, Jamba, Llama 3.1/3.2/3.3, Llama4, Mistral, Phi4, Pixtral, Qwen2.5, QwQ, and more. You can also run custom models via a model repository.
Do I need a GPU to run OpenLLM?
Yes, GPU is recommended and often required for reasonable performance. Minimum GPU memory varies by model; for example, Gemma2 2B needs about 12GB, while Llama3.3 70B requires 80GB x 2 GPUs. Check the model table for requirements.
Is OpenLLM free to use?
Yes, OpenLLM is free and open source. You can install it via pip and run it locally or deploy on your own infrastructure without any licensing fees.
How do I use OpenLLM with the OpenAI Python client?
Set the base URL to your OpenLLM server (e.g., http://localhost:3000) and optionally an API key. Example: `client = OpenAI(base_url='http://localhost:3000/v1', api_key='na')`.