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  "path": "/t/aios-first-ground-truth-baseline-cpu-dram-measurement/174769#post_2",
  "publishedAt": "2026-04-09T04:28:29.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "TurboQuant: Redefining AI efficiency with extreme compression"
  ],
  "textContent": "Update: TurboQuant (Google, ICLR 2026) is directly relevant to AIOS\nGoogle released TurboQuant this week — a KV cache quantization algorithm that compresses attention key-value pairs from 16 bits to 3 bits with near-zero quality loss and no retraining required(TurboQuant: Redefining AI efficiency with extreme compression).\nThis is complementary to AIOS, not competing. They address the same bottleneck from different directions:\n∙ TurboQuant reduces KV cache size — fewer bits per KV entry\n∙ AIOS reduces KV cache DRAM reads — fewer times those bits are fetched per token\nBoth optimizations apply simultaneously. A model running TurboQuant under AIOS memory management addresses the KV bottleneck from two directions at once.\nOur first baseline (Intel Ultra 7 265K) measured 2,340 MB/token on stock llama.cpp. At 4K context, KV cache reads are a significant fraction of that. TurboQuant’s 5x KV compression would reduce that fraction further before AIOS residency management applies on top.\nThe broader pattern: BitNet (weight arithmetic), CALM (forward passes), TurboQuant (KV size), AIOS (DRAM access patterns) — four independent groups addressing four non-overlapping bottlenecks in the same inference stack. None of them are sufficient alone. All of them stack.",
  "title": "AIOS — First Ground Truth Baseline (CPU DRAM Measurement)"
}