Amazon SageMaker
Fully managed machine learning service to build, train, and deploy ML models at scale
OpenLLM
Run any LLM locally behind an OpenAI-compatible API
OpenLLM lets developers serve any open‑source LLM (Llama, Qwen, Phi, etc.) as an OpenAI‑compatible API with a single command, plus a chat UI and Docker/K8s deployment tools.
Overview
Overview
OpenLLM is designed for developers, data scientists, and enterprises that want to self‑host large language models without building custom inference stacks. With a single openllm serve command you can launch a model such as Llama 3.3, Qwen 2.5, or Phi‑4 and instantly expose OpenAI‑compatible endpoints for chat, completions, and embeddings.
Capabilities & Deployment
The framework includes a built‑in web chat UI, supports a growing catalog of state‑of‑the‑art models, and integrates with Docker, Kubernetes, and BentoCloud for production‑grade deployments. Model weights are fetched from Hugging Face at runtime, requiring only an HF token for gated models. Once running, the server is reachable at http://localhost:3000 (or any configured host) and can be consumed by any client library that speaks the OpenAI API.
OpenLLM streamlines the workflow from local experimentation to scalable cloud services, letting teams iterate quickly while retaining full control over data and infrastructure.
Highlights
OpenAI‑compatible API for any supported open‑source LLM
Single‑command server launch with built‑in chat UI
Extensive model catalog (Llama, Qwen, Phi, Mistral, etc.)
Docker, Kubernetes, and BentoCloud deployment options
Pros
- Fast setup – one command starts a fully functional API
- Broad model support reduces the need for multiple tools
- Self‑hosted endpoints give full data privacy control
- Flexible deployment paths from local to cloud
Considerations
- Requires compatible GPU hardware for larger models
- Gated models need a Hugging Face token and access approval
- Performance depends on underlying infrastructure and drivers
- Not a managed SaaS solution; operational responsibility remains with the user
Managed products teams compare with
When teams consider OpenLLM, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- Rapid prototyping of LLM‑powered applications
- Enterprises seeking on‑premise inference to avoid vendor lock‑in
- Researchers comparing multiple open‑source models under a uniform API
- Teams that already use Docker or Kubernetes for deployment
Not ideal when
- Environments without GPU acceleration or limited memory
- Large‑scale production requiring auto‑scaling beyond basic K8s setups
- Users who prefer a fully managed hosted service
- Projects that cannot obtain required Hugging Face tokens for gated models
How teams use it
Chatbot prototype
Launch a functional chat API in minutes for internal testing or demos.
Internal microservice
Expose a secure, self‑hosted LLM endpoint that integrates with existing backend services.
Model benchmarking
Run multiple models behind the same API to compare latency, cost, and quality.
Educational labs
Provide students with hands‑on experience deploying and querying LLMs without external cloud costs.
Tech snapshot
Python96%
Shell4%
Frequently asked questions
Which models are supported?
OpenLLM ships with dozens of models including Llama 3.1/3.2/3.3, Qwen 2.5, Phi‑4, Mistral, Gemma, and more. Custom model repositories can also be added.
Do I need to download model weights beforehand?
No. Weights are fetched from Hugging Face at runtime. A valid HF_TOKEN is required for gated models.
Is an API key required for client requests?
The API key is optional; you can use a dummy value (`na`) for local testing.
Can I deploy OpenLLM on Kubernetes?
Yes. The project provides Docker images and Helm charts that integrate with standard K8s workflows.
Project at a glance
Active- Stars
- 12,058
- Watchers
- 12,058
- Forks
- 797
LicenseApache-2.0
Repo age2 years old
Last commit2 days ago
Primary languagePython
Last synced 3 hours ago