Coding · 8 tools · updated Oct 15, 2026
How do I run an open model on my own machine?
vLLM leads with an evidence score of 76/100, ahead of Hugging Face (66) and Ollama (66). 8 of 8 can be used free, 6 are open source. Best for self-hosting models for production traffic: vLLM.
Ranked by evidence — GitHub stars, Hacker News mentions, release activity and how much you can use for free. Recomputed nightly. How the score works.
+ Serves many concurrent requests on GPUs far faster than llama.cpp-based tools.
− Linux and NVIDIA-centric; painful on Mac or consumer hardware.
+ Every open model ends up here, with Inference Providers routing to multiple backends.
− Serverless inference rate limits and cold starts make it unreliable for production.
+ `ollama run llama3` is the simplest path to a local model, and most tools integrate with it.
− Hides llama.cpp tuning options, so throughput trails hand-tuned setups.
+ The engine underneath Ollama, LM Studio, and most local tooling, with the widest hardware support.
− Building and tuning flags yourself is a chore compared with wrappers.
+ Friendly GUI for browsing Hugging Face GGUF and MLX models with hardware fit estimates.
− The app itself is closed source, which matters to some privacy-minded users.
+ Multi-user accounts, RAG, and tool plugins make it a full private ChatGPT replacement.
− Docker-only deployment and a licence change in 2025 that restricts branding removal.
+ LocalDocs lets you chat with folders of files with no extra setup.
− Development has slowed and model support lags newer releases.
+ Clean open-source app with a built-in OpenAI-compatible server and MCP support.
− Fewer model-management and tuning features than LM Studio.
Head to head: vLLM vs Hugging Face vLLM vs Ollama Hugging Face vs Ollama
Missing a tool? It gets added when it shows up in the release feed with real traction, not by request.