openEuler Wants Linux to Treat an Entire AI Cluster Like One Single Machine
For most of its life, Linux has been built around one simple assumption.
Executive Summary
For most of its life, Linux has been built around one simple assumption. There's a box; it has CPUs, memory, and devices, and the kernel manages them. Clusters exist, of course, but they've mostly been a problem for the layers above the OS, i.e., schedulers, orchestrators, distributed frameworks. AI infrastructure wants more. Today's training and inference setups bind dozens of CPUs, NPUs, GPUs, and memory pools so tightly together that they behave less like a group of servers and more like a single but very large computer.
Artificial Intelligence Architecture & Model Evaluation
From an artificial intelligence architecture, model weights governance, and inference efficiency perspective: - **Weights Accessibility & Sovereignty:** Evaluates whether weights are open for private self-hosting or locked behind centralized cloud APIs. - **Quantization & Edge Performance:** Kernel optimizations (4-bit/8-bit GGUF, AWQ, EXL2) allow high tokens-per-second on consumer GPUs and Apple Silicon. - **Reasoning & Architectural Scaling:** Scrutinizes mixture-of-experts (MoE), attention mechanisms, and fine-tuning datasets against open community benchmarks.
Impact on the Open Ecosystem
Protects developers and enterprises from proprietary black-box entrapment, fostering auditable, sovereign AI infrastructure.