What this proves
Related systems
Explore more of Amorosi Labs
AI Lab — Distributed Inference R&D
A distributed RAG lab split in two: a local 'brain' orchestrating the pipeline and a RunPod GPU 'muscle' serving 4-bit quantized inference.
Proof
MLOps discipline: total separation of orchestration and inference, VRAM-safe model loading, and embedding parity between the local FAISS knowledge base and the remote /embed endpoint.
'Brain & Muscle' split: local orchestrator + Dockerized RunPod inference server
Read the code
Source on GitHub ↗ModelManager 'VRAM dance' loads 4-bit quantized models without OOM crashes
Built under real friction
Talk to Juan →FAISS knowledge base with embedding parity between local build and remote /embed
Read the code
Source on GitHub ↗Build → verify locally → deploy: Docker images tested before touching the GPU
Built under real friction
Talk to Juan →Stack
Evidence
Founder note
“The RunPod AI Lab shows Juan's MLOps approach to cost-efficient distributed inference.”
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Talk to Juan →Get it
