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  "path": "/t/aios-cpu-native-llm-inference-architecture-seeking-validation-contributors/174633#post_1",
  "publishedAt": "2026-03-26T02:50:48.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "aios-framework/aios-paper · Hugging Face",
    "Falcon 7B + AIOS: measure baseline MB/token (primary validation) · Issue #2 · acasavaraju/AIOS · GitHub"
  ],
  "textContent": "I’ve published a framework paper proposing a CPU-native inference\narchitecture for large language models.\n\n**Core argument:** LLMs are slow on CPU not because CPUs are unsuited\nto inference, but because models and runtimes were designed for GPU\nmemory architecture and never redesigned for CPU cache hierarchy.\nAIOS proposes a memory residency controller and Model Contract to\nclose that gap.\n\n**What AIOS is:**\n\n  * A runtime (memory residency controller) between inference engines\nand hardware — reducing DRAM data movement per generated token\n  * A Model Contract — five architectural requirements models can\nsatisfy to expose the full optimization surface\n\n\n\n**Current state:** Paper published, spec complete, validation tooling\nrunnable. Runtime not yet implemented. All performance projections\nare analytical — no empirical results exist yet.\n\n**What I need most:**\nSomeone with bare metal Linux (Intel Haswell+ or AMD Zen+, 16GB RAM)\nto run the Phase 1 baseline measurement on Falcon 7B Q4_K_M using\nstock llama.cpp. Full protocol in Issue #2. Takes ~2 hours including\nsetup.\n\n**Links:**\n\n  * HuggingFace: aios-framework/aios-paper · Hugging Face\n  * Issue #2 (start here): Falcon 7B + AIOS: measure baseline MB/token (primary validation) · Issue #2 · acasavaraju/AIOS · GitHub\n\n",
  "title": "AIOS: CPU-Native LLM Inference Architecture — Seeking Validation Contributors"
}