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Running LLMs larger than memory on a consumer Mac — falsification-driven research: margin-gated deferred refinement, out-of-core verification on Apple Silicon. TR-01 published.
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1---2project: localvm-research3document: expF_error_accumulation/analysis4author: Simon-Pierre Boucher5contact: contact@spboucher.ai6created: 2026-08-127status: reviewed8---910# Analysis — expF_error_accumulation1112Run: `results/expF_error_accumulation/20260812T043945Z/` · code committed before13run. Qwen3-1.7B, 28 layers in 7 depth-groups of 4, affine g64 4-bit, 4814trajectories × 128 tokens teacher-forced, 17 configurations.1516```text17Hypothesis / Falsification18 Hypothesized ≥3× sensitivity spread across depth groups and ≥40% of lost19 agreement recovered by repairing the top ~25% of layers.20 Kill criteria: <2× spread, or top-25% repair recovering <20%.2122Result — HYPOTHESIS REFUTED (spread criterion killed; repair borderline-negative)23 All-4-bit floor: 87.53% agreement (loss = 12.47 points vs bf16).24 DEGRADE-ONE (one group 4-bit, rest bf16): agreement drops span only25 3.7–5.6 points across the 7 groups — a 1.5× spread (< the 2× kill26 line). Mid-depth groups (L12–19) are marginally most sensitive; the27 LAST group is among the LEAST sensitive (3.96) despite expD showing28 the largest hidden-state error there — late-layer error is large in29 norm but decision-benign.30 REPAIR-ONE (one group bf16, rest 4-bit): best single group (L12–15)31 recovers +1.5 points = 12% of the loss; worst 5%.32 REPAIR-TOP-K: top-2 groups (29% of layers) recover 24% of the loss;33 top-3 (43% of layers) recover 33% — consistently SUB-proportional.34 Cross-check of additivity: individual degrade-one drops sum to ~3335 points, yet degrading everything at once costs only 12.5 — errors36 partially mask each other; symmetrically, repair values sum to 7.437 of the 12.5 lost — repair requires cooperation across depth.3839Interpretation40 1. NEGATIVE RESULT (recorded per charter §10/§17): quantization damage41 to token decisions is DIFFUSE and cooperative across depth, not42 concentrated. Layer-restricted escalation cannot materially cut43 bytes-per-escalation: paying 29% of the residual bytes buys only44 24% of the repair — worse than linear, no leverage.45 2. expD's depth-concentrated hidden-state error was a red herring for46 decision repair: large late-layer representation drift coexists with47 benign decisions (norms ≠ decisions — a caution for any design that48 gates on hidden-state error instead of decision margin).49 3. Consequently C1's bytes-per-escalation must come from the remaining50 mechanisms: (b) temporal locality — the residual working set of51 escalated tokens staying hot in RAM (expB); (c) sub-layer/block-level52 selection — repair only the weight blocks that matter for THIS token53 (expA/expE, finer grain than layers); or (d) batch-amortized54 escalation — G17/G18-style verification sweeps sharing one residual55 read across many queued low-margin tokens.56 4. Design implication: if (b) also fails, C1 degrades into "resident57 4-bit + rare whole-model refinement passes" — which is exactly58 candidate C2 (amortized verification). The two candidates are59 converging on the same mechanism from opposite ends; this is60 useful, not disappointing.6162Next experiment63 expA (weight contribution concentration at BLOCK granularity within64 layers) — the finer-grained version of the question expF just answered65 negatively at layer granularity; feeds expE (partial GEMM) directly.66 In parallel, expB (temporal stability of important blocks) decides the67 page-cache path (b).68```69