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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.

Python 63.2% JavaScript 23.5% CSS 11.8% Shell 0.9% Makefile 0.5%

expA+expB: NEGATIVE results — no pageable/cacheable dynamic sparsity on dense SwiGLU

expA kill: 77% of 64-neuron blocks for 95% energy; expB kill: 99.9%
union at 128 tokens, Jaccard ~ random null. Fine-grained escalation
routes all closed; evidence selects C1->C2 merge (batch-amortized
margin-gated refinement).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Simon-Pierre Boucher committed 5 h ago (Aug 12, 2026) parent ca815b5

Showing 6 changed files with +273 and −14

modified experiments/micro/expA_weight_concentration/analysis.md +45 −3
@@ -3,10 +3,52 @@ project: localvm-research
3 3 document: expA_weight_concentration/analysis
4 4 author: Simon-Pierre Boucher
5 5 contact: contact@spboucher.ai
6 −created: 2026-08-11
7 −status: draft
6 +created: 2026-08-12
7 +status: reviewed
8 8 ---
9 9
10 10 # Analysis — expA_weight_concentration
11 11
12 −*To be written after results exist. Must include the seven-field block and the evidence standard of CLAUDE.md §10.*
12 +Run: `results/expA_weight_concentration/20260812T044829Z/` · Qwen3-1.7B bf16,
13 +48 trajectories × 128 tokens (6 144 positions), SwiGLU intermediate energy
14 +per 16-neuron block (384 blocks × 28 layers), 64-neuron granularity derived.
15 +
16 +```text
17 +Hypothesis / Falsification
18 + Hoped: top 20% of 64-neuron blocks ≥60% energy; ≤50% of blocks for 95%.
19 + Kill: >70% of 64-neuron blocks needed for 95% energy.
20 +
21 +Result — KILL CRITERION TRIGGERED at 64-neuron granularity
22 + Granularity top10% top20% needed for 90% / 95% / 99%
23 + neuron 0.88 0.94 0.13 / 0.20 / 0.38
24 + block-16 0.58 0.71 0.48 / 0.61 / 0.81
25 + block-64 0.44 0.57 0.65 / 0.77 / 0.92 ← 0.77 > 0.70 kill line
26 + Domain-independent (95% needs 61–62% of block-16 across all six domains).
27 + Strong depth gradient: late layers concentrate (L27 needs 11% of blocks
28 + for 95%; L20–26: 30–47%) while early/mid layers are diffuse (64–81%).
29 +
30 +Interpretation
31 + 1. NEGATIVE at pageable granularity: real per-token concentration exists
32 + at neuron level (20% for 95% — consistent with TEAL-class ~40–50%
33 + approximate sparsity claims), but bundling to SSD-friendly blocks
34 + destroys it: at 64 neurons (≈ 256 KiB rows bundle at 1.7B dims) the
35 + important set is 77% of the layer — no meaningful byte savings.
36 + Important neurons are SCATTERED, not clustered: block energy ≈
37 + uniform mixing. This is the quantitative reason LLM-in-a-flash used
38 + ReLU models — SwiGLU energy has no exploitable block structure.
39 + 2. The expH fetch contract (≥256 KiB) and neuron-level concentration
40 + (4 KiB-scale rows) are mutually exclusive on this architecture:
41 + the SSD wants big blocks, the sparsity lives in small ones. A
42 + permutation/clustering pass (grouping co-active neurons) is the one
43 + remaining idea for this route — but expB (below) must first show the
44 + sets are stable enough to be worth clustering.
45 + 3. The depth gradient is scientifically interesting: late layers are
46 + energy-concentrated but (expF) decision-insensitive; early layers are
47 + decision-relevant but energy-diffuse. Energy is not the right
48 + importance signal for escalation — margins are (expG).
49 +
50 +Next experiment
51 + expB on the same trace (temporal stability) — run before drawing final
52 + conclusions on route (c); if sets churn too, the sparsity-paging family
53 + (G06/G07/G12/G13) dies for dense SwiGLU models at this scale.
54 +```
modified experiments/micro/expA_weight_concentration/hypothesis.md +5 −4
@@ -4,7 +4,8 @@ document: expA_weight_concentration/hypothesis
4 4 author: Simon-Pierre Boucher
5 5 contact: contact@spboucher.ai
6 6 created: 2026-08-12
7 −status: draft
7 +modified: 2026-08-12
8 +status: reviewed
8 9 ---
9 10
10 11 # Hypothesis — expA_weight_concentration
@@ -48,11 +49,11 @@ Baseline
48 49 achievable concentration.
49 50
50 51 Result
51 − <filled after the run>
52 + KILL TRIGGERED at 64-neuron granularity: 95% energy needs 77% of blocks (>70% line). Neuron-level real (20% for 95%) but scattered — bundling destroys it. Depth gradient: late layers concentrated, early diffuse. Domain-independent.
52 53
53 54 Interpretation
54 − <filled after the run>
55 + SwiGLU energy has no exploitable block structure; SSD fetch contract (≥256 KiB) and neuron-scale sparsity are mutually exclusive. Energy ≠ decision importance (cf. expF).
55 56
56 57 Next experiment
57 − <filled after the run>
58 + expB on the same trace; then pivot decision.
58 59 ```
modified experiments/micro/expB_token_stability/analysis.md +56 −3
@@ -3,10 +3,63 @@ project: localvm-research
3 3 document: expB_token_stability/analysis
4 4 author: Simon-Pierre Boucher
5 5 contact: contact@spboucher.ai
6 −created: 2026-08-11
7 −status: draft
6 +created: 2026-08-12
7 +status: reviewed
8 8 ---
9 9
10 10 # Analysis — expB_token_stability
11 11
12 −*To be written after results exist. Must include the seven-field block and the evidence standard of CLAUDE.md §10.*
12 +Run: `results/expB_token_stability/20260812T045224Z/` · consumes expA's trace
13 +(Qwen3-1.7B, 6 144 positions × 28 layers), 64-neuron sets covering 95% of
14 +per-token FFN energy.
15 +
16 +```text
17 +Hypothesis / Falsification
18 + Hoped: Jaccard(t,t+1) ≥ 0.5 with slow decay; 128-token union ≤80%.
19 + Kill: Jaccard(t,t+1) < 0.3 OR 128-token union ≥95%.
20 +
21 +Result — KILL CRITERION TRIGGERED (union), stickiness ILLUSORY
22 + Jaccard(t,t+δ): 0.690 (δ=1) → 0.688 (δ=16) → 0.686 (δ=32) — flat.
23 + Random-set null at matched sizes: 0.640. Real stickiness beyond
24 + set-size artifact: +0.05 — negligible. (The registered ≥0.5 threshold
25 + is technically met at 0.69, but the null exposes it as a size effect:
26 + sets covering ~77% of blocks overlap ~64% by chance alone.)
27 + Union working set: 98.0% of blocks after 8 tokens, 99.3% after 32,
28 + 99.9% after 128 — the entire FFN is touched within a few tokens.
29 + Domain locality (expC-lite): within-domain = across-domain = 1.0 —
30 + every trajectory's union covers everything; no domain-specific caches.
31 +
32 +Interpretation
33 + 1. NEGATIVE, decisively: for dense SwiGLU models, per-token FFN energy
34 + working sets are neither sparse at pageable granularity (expA) nor
35 + temporally reusable (here) nor domain-clustered (here). The
36 + DejaVu / LLM-in-a-flash paradigm does NOT transfer to modern
37 + non-ReLU architectures without ReLUfication retraining — now
38 + measured, not just suspected from the literature. G06 (TealPager),
39 + G12 (HiddenPrefetch for dense FFNs) and G13 (DomainCache) are dead
40 + on this model family; G07 (WandaTiers) survives only for STATIC
41 + (input-independent) importance tiering.
42 + 2. Scope note (honesty): we measured activation-ENERGY sets on a 1.7B
43 + dense model. Residual-relevance sets (which bytes fix a decision)
44 + could differ, and MoE models have architectural, not emergent,
45 + sparsity — C3 is untouched by this result. But the prior for any
46 + per-token dynamic weight selection on dense SwiGLU is now strongly
47 + unfavorable.
48 + 3. PIVOT (with expF, expA): all three fine-grained routes for cutting
49 + C1's per-token escalation bytes are closed — layers (expF), blocks
50 + (expA), temporal caching (expB). What remains is BATCH AMORTIZATION,
51 + which expH makes attractive: the full 1 GB residual streams
52 + sequentially in ~80 ms at 13 GB/s; a margin-gated queue that defers
53 + low-margin tokens and refines them in periodic sweeps shares one
54 + sequential residual pass across many tokens. C1 thereby converges
55 + with C2 (Amortized Verification Sweeps) — the evidence has selected
56 + the candidate.
57 +
58 +Next experiment
59 + Prototype candidate: margin-gated deferred-refinement runtime
60 + (C1→C2 merge): 4-bit resident base generates optimistically; low-margin
61 + tokens queue; a periodic sequential residual sweep verifies/corrects
62 + (speculative-decoding-style rollback on flips). Measure end-to-end
63 + bytes/token, tok/s, and agreement on this Mac. Also rerun expG at 8B to
64 + check margin-signal scaling before committing the prototype design.
65 +```
modified experiments/micro/expB_token_stability/hypothesis.md +5 −4
@@ -4,7 +4,8 @@ document: expB_token_stability/hypothesis
4 4 author: Simon-Pierre Boucher
5 5 contact: contact@spboucher.ai
6 6 created: 2026-08-12
7 −status: draft
7 +modified: 2026-08-12
8 +status: reviewed
8 9 ---
9 10
10 11 # Hypothesis — expB_token_stability
@@ -44,11 +45,11 @@ Baseline
44 45 tokens) as the floor stickiness must beat.
45 46
46 47 Result
47 − <filled after the run>
48 + KILL TRIGGERED: 128-token union = 99.9% of blocks (≥95% line); Jaccard(t,t+1)=0.69 vs random null 0.64 — stickiness is a set-size artifact. Domain locality nil (within=across=1.0).
48 49
49 50 Interpretation
50 − <filled after the run>
51 + No cacheable working set for dense SwiGLU energy sets: DejaVu/LLM-in-a-flash paradigm does not transfer without ReLUfication. G06/G12/G13 dead on this family; pivot to batch amortization (C1 merges into C2).
51 52
52 53 Next experiment
53 − <filled after the run>
54 + Prototype margin-gated deferred-refinement runtime; expG rerun at 8B.
54 55 ```
modified research/LOG.md +26 −0
@@ -219,3 +219,29 @@ Format per entry: date/time (local, with timezone) · question · experiment ·
219 219 — the last converging with C2 (amortized verification). Negative result recorded per §10/§17.
220 220 - **Decision:** Next: expA (block-granularity concentration) and expB (temporal stability) —
221 221 the trace campaign that also feeds G06/G07/G12/G13 per the Phase 3 cluster analysis.
222 +
223 +---
224 +
225 +## 2026-08-12 13:10 EDT — expA + expB complete: NEGATIVE — dynamic sparsity paging dead on dense SwiGLU; evidence selects the C1→C2 merge
226 +
227 +- **Question:** Is per-token FFN importance concentrated at pageable block granularity (expA),
228 + and is the important set temporally/domain reusable (expB)?
229 +- **Experiment:** One instrumented trace (SwiGLU intermediate energy, 16-neuron blocks × 28
230 + layers × 6 144 positions) feeding both analyses.
231 + Results: `results/expA_weight_concentration/20260812T044829Z/`,
232 + `results/expB_token_stability/20260812T045224Z/`.
233 +- **Result:** expA: 95% energy needs 77% of 64-neuron blocks (kill >70%); neuron-level
234 + concentration real (20%) but scattered — bundling destroys it; strong depth gradient
235 + (L27: 11%, early layers: ~80%). expB: Jaccard(t,t+1)=0.69 vs matched random null 0.64
236 + (stickiness = set-size artifact); union working set 98% after 8 tokens, 99.9% after 128
237 + (kill ≥95%); domain locality nil.
238 +- **Interpretation:** All three fine-grained routes for per-token escalation bytes are now
239 + closed by measurement: layers (expF), blocks (expA), temporal/domain caching (expB). The
240 + DejaVu/LLM-in-a-flash paradigm measurably does not transfer to dense SwiGLU models.
241 + G06/G12/G13 dead on this family; G07 survives only as static tiering. Remaining route is
242 + batch amortization, which expH's sequential numbers make attractive (full 1 GB residual
243 + streams in ~80 ms at 13 GB/s, shared across a queue of deferred low-margin tokens).
244 + **C1 converges into C2: margin-gated deferred refinement with periodic sequential sweeps.**
245 +- **Decision:** (1) Rerun expG at 8B to confirm margin-signal scaling; (2) then prototype
246 + candidate_01: 4-bit resident base + margin-gated deferral queue + periodic residual sweep
247 + with rollback, measured in bytes/token, tok/s, agreement, on this Mac.
added results/expB_token_stability/20260812T045224Z/results.json +136 −0
@@ -0,0 +1,136 @@
1 +{
2 + "experiment": "expB_token_stability",
3 + "author": "Simon-Pierre Boucher",
4 + "contact": "contact@spboucher.ai",
5 + "manifest": {
6 + "author": "Simon-Pierre Boucher",
7 + "contact": "contact@spboucher.ai",
8 + "project": "localvm-research",
9 + "collected_utc": "2026-08-12T04:52:24.381905+00:00",
10 + "chip": {
11 + "brand": "Apple M5 Max",
12 + "arch": "arm64",
13 + "cores_total": 18,
14 + "cores_performance": 6,
15 + "cores_efficiency": 12,
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17 + },
18 + "memory": {
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20 + "unified_gb": 48.0,
21 + "pagesize": 16384
22 + },
23 + "ssd": {
24 + "model": "APPLE SSD AP2048Z",
25 + "size": "2 TB",
26 + "smart_status": "Verified"
27 + },
28 + "os": {
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31 + "build": "26A5388g",
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45 + "thermal_level_at_collect": null
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49 + "target": 0.95
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133 + "across_domain_union_jaccard": 1.0
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135 + "mean_topset_frac": 0.6474677191840279
136 +}
\ No newline at end of file
137