Eradicating AWS OOM Kills: 10M LLM Inferences on a $0 Cloud-Cost O(1) Memory Clamped Architecture
High cloud API costs and Out-Of-Memory (OOM) failures in large-scale data pipelines are architectural defects, not hardware constraints. Standard cloud approaches force teams to continually scale up high-memory instances (e.g., AWS r6i/r7i) to survive memory leaks and heap fragmentation. This repor
High cloud API costs and Out-Of-Memory (OOM) failures in large-scale data pipelines are architectural defects, not hardware constraints. Standard cloud approaches force teams to continually scale up high-memory instances (e.g., AWS r6i/r7i) to survive memory leaks and heap fragmentation.
This report documents the performance and execution of a 10,000,000-record batch LLM inference pipeline executed locally on an HP Z4 G4 workstation (Intel Xeon W-2155, 128GB ECC DDR4 RAM, NVIDIA RTX 5060 Ti 16GB VRAM, NVMe PCIe Gen4 SSD).
Technical Architecture & Memory Enforcement