#140
C+
Rank #224
PrivateGPT
PrivateGPT lets you run document Q&A and RAG entirely offline against local LLMs, keeping data off third-party APIs. Its standout trait is a privacy-first design built around fully local inference.
56.0
/ 100
⚙ Full Stack
A FastAPI Python service wrapping local LLM inference (via Ollama or llama.cpp bindings) and a local vector store (Qdrant/Chroma) for document embeddings, with GPU acceleration strongly recommended.
📈 Scaling Analysis
Inference throughput is bound to a single GPU/CPU node per instance with no built-in load balancing across model replicas, so scaling beyond a handful of concurrent users requires manually running multiple stacks behind an external router — it's built for single-tenant privacy, not multi-tenant scale.
🚀 Running on Nexlayer
Needs a GPU-enabled pod for inference plus a vector-store pod reached via <podName>.pod:6333; model weights are large so plan a persistent volume for the model cache. GPU scheduling constraints make this one of the harder apps in this batch to place on a shared cluster.