If you've narrowed your search to Apache-2.0 licensed tools, you've eliminated licensing friction—but you now face a more practical question: which one actually fits your hardware and use case? The 12 tools below all share the same permissive license, but they differ sharply in GPU dependency, CPU viability, setup complexity, and what they're built to do. This guide cuts through that variation.
The 12 tools fall into three groups. Pure LLM runtimes (vLLM, text-generation-inference, FastChat, MLC-LLM, llamafile, SGLang, OpenLLM, Xinference) run models and serve them via API or HTTP. RAG frameworks (PrivateGPT, Haystack, h2oGPT) wrap an LLM plus a vector database to let you ask questions about your own documents offline. Embedding servers (Text Embeddings Inference) handle just the vector-generation piece. The category you need narrows the choice immediately.
Seven tools explicitly recommend or require GPU: vLLM, text-generation-inference, FastChat, SGLang, and OpenLLM all target 16GB+ VRAM. If you don't have a GPU, skip those five for production inference. MLC-LLM and llamafile flip that math. MLC-LLM compiles models for edge and mobile with 4GB+ RAM baseline; llamafile runs on CPU alone and ships as a single executable with no installation step. h2oGPT tolerates 16GB RAM without GPU. PrivateGPT and Haystack depend on whatever embedding and LLM backend you plug in, but both can run CPU-only if you choose smaller models. Text Embeddings Inference and Xinference also scale down with model choice. If your hardware maxes out at CPU or modest RAM, llamafile or MLC-LLM are your only pure-play runtimes; for RAG, PrivateGPT or Haystack with small models are safer bets.
vLLM and text-generation-inference are the speed leaders for GPU-heavy serving—they optimize throughput and latency for production load. FastChat and SGLang are close seconds, with SGLang adding structured output. If you need the absolute fastest inference on good hardware, vLLM wins. If you want HuggingFace's battle-tested inference tooling, text-generation-inference is standard. For a gentler on-ramp, FastChat includes chat fine-tuning and was the origin of Vicuna models. llamafile requires zero setup and no dependencies; h2oGPT gives you a web UI and document upload out of the box. MLC-LLM and Xinference trade ease for flexibility—both support multiple backends and mobile/edge targets. For a weekend project or proof-of-concept, start with llamafile or h2oGPT. For production, vLLM or text-generation-inference.
If your goal is to let users ask questions about their own PDFs, Word docs, or web pages offline, PrivateGPT and Haystack are the two Apache-2.0 picks. PrivateGPT is simpler: it embeds documents, stores them locally, and queries them with a local LLM—no external APIs, no configuration needed beyond pointing it at your files. Haystack is a pipeline framework; it's more powerful if you need chaining, filtering, or multi-step logic, but steeper to learn. h2oGPT bridges the gap: it's a runtime with RAG bundled in, a web UI, and document upload. Choose PrivateGPT if you want minimal, auditable code. Choose Haystack if you're building a complex retrieval pipeline. Choose h2oGPT if you want one tool that does both chat and document Q&A without tinkering.
vLLM and text-generation-inference are both mature with very active development. FastChat, h2oGPT, PrivateGPT, Haystack, and Xinference are mature and actively maintained. SGLang, MLC-LLM, llamafile, OpenLLM, and Text Embeddings Inference are active but younger or smaller in team size. All 12 are production-viable; none are stalled. If you need a safe default, vLLM or text-generation-inference reduce regret risk. If you're comfortable with a smaller ecosystem, SGLang or MLC-LLM are solid.