PP & TG Performance Comparison: Qualcomm's ggml-hexagon vs JZ's ggml-hexagon

[zhouwg]

How to perform PP & TG benchmark comparisons between Qualcomm's ggml-hexagon and JZ's ggml-hexagon

for fair performance comparison, the same "running_params=" -ngl 99 -t 6 -n 256 --ctx-size 8192 --ubatch-size 64 --poll 1000 --no-warmup --no-mmap -fa on" " and the same prompt and same LLM model file and same 8Elite phone would be used in both inference test.

We can run automated AB tests on Snapdragon 8 Elite following the recommended steps below:

1. download codes

 git clone https://github.com/zhouwg/ggml-hexagon
 cd ggml-hexagon
 git checkout self-build-jz

2. build Qualcomm's ggml-hexagon in self-build-jz branch

./scripts/build-run-android.sh build_qcom

3. build JZ's ggml-hexagon in self-build-jz branch accordingly

./scripts/build-run-android.sh build

4. run automated AB test

./scripts/build-run-android.sh run_abtest 2>&1 | tee log_abtest_$(date +%Y%m%d-%H%M%S).txt

5. Analyze logs and generate a performance comparison table from log_abtest_$(date +%Y%m%d-%H%M%S).txt

We can run non-automated AB tests on Snapdragon 8 Elite following the recommended steps below:

1. download codes

 git clone https://github.com/zhouwg/ggml-hexagon
 cd ggml-hexagon
 git checkout self-build-jz

2. build Qualcomm's ggml-hexagon in self-build-jz branch

./scripts/build-run-android.sh build_qcom

3. build JZ's ggml-hexagon in self-build-jz branch accordingly

./scripts/build-run-android.sh build

4. run llama-cli or llama-bench with Qualcomm ggml-hexagon

./scripts/build-run-android.sh update_qcom_libs
./scripts/build-run-android.sh run_llamacli
./scripts/build-run-android.sh run_llamabench

6. run llama-cli or llama-bench with JZ ggml-hexagon

./scripts/build-run-android.sh update_jz_libs
./scripts/build-run-android.sh run_llamacli
./scripts/build-run-android.sh run_llamabench

Notice

Feel free to share test results from different Qualcomm chip-based Android phones. For developers and experts from China, posts written in Chinese are permitted.Developers and experts on GitHub, please support me in getting JZ's ggml-hexagon accepted by the Qualcomm and upstream llama.cpp community

Test results

Snapdragon 8 Elite(aka 8Gen4), Vendor: OnePlus

PP&TG in JZ's ggml-hexagon:

PP&TG in Qualcomm's ggml-hexagon

Snapdragon 8Gen3, Vendor: Xiaomi

LLM inference test

llama-bench test

PP&TG in Qualcomm ggml-hexagon:

PP&TG in JZ ggml-hexagon:

[zhouwg]

AB Test Analysis Report - 2026-07-28

Device: Qualcomm Snapdragon 8 Elite (aka 8 Gen 4)
Model: gemma4
Date: 2026-07-28 07:59 - 08:01
Log file: log_abtest_20260728-075932.txt

1. How to generate this report

git clone https://github.com/zhouwg/ggml-hexagon
cd ggml-hexagon

git checkout self-build-jz

./scripts/build-run-android.sh build_qcom (build Qualcomm's ggml-hexagon in self-build-jz branch accordingly)

./scripts/build-run-android.sh build (build JZ's ggml-hexagon in self-build-jz branch accordingly)

./scripts/build-run-android.sh run_abtest 2>&1 | tee log_abtest_$(date +%Y%m%d-%H%M%S).txt (run automated AB test)

2. Garbled Output Check

Implementation	3 runs output	Garbled
JZ	All normal, coherent English (movie review)	None
QCOM	All normal, coherent English (movie review)	None

3. Performance Data (3 rounds each)

JZ

Round	PP (tok/s)	TG (tok/s)
Run 1	683.32	27.16
Run 2	710.44	27.47
Run 3	718.38	28.54
Avg	704.05	27.72

QCOM

Round	PP (tok/s)	TG (tok/s)
Run 1	451.74	24.88
Run 2	442.05	24.69
Run 3	400.28	24.49
Avg	431.36	24.69

4. JZ vs QCOM Advantage

Metric	JZ	QCOM	JZ Speedup
PP	704.05	431.36	1.63x (63% faster)
TG	27.72	24.69	1.12x (12% faster)

5. Regression Analysis (vs 0726 abtest baseline)

Metric	0726 Baseline	0728 Current	Change	Verdict
JZ PP	692.37	704.05	+1.7%	No regression (normal variance)
JZ TG	27.12	27.72	+2.2%	No regression (normal variance)
QCOM PP	439.28	431.36	-1.8%	No regression (normal variance)
QCOM TG	24.08	24.69	+2.5%	No regression (normal variance)

6. Conclusion

No performance regression, no garbled output. After merging upstream master and fixing the CMakeLists.txt build issue (restoring JZ slim build options: LLAMA_BUILD_APP/SERVER/UI/EXAMPLES/OPENSSL=OFF), both JZ and QCOM performance are within normal run-to-run variance. JZ's advantage over QCOM (PP 1.63x, TG 1.12x) remains stable on Snapdragon 8 Elite (aka 8 Gen 4).

Revision History

Date	Author	Description
2026-07-28	GLM-5.2	Initial report, created after merging upstream master and fixing CMakeLists.txt build issue

[zhouwg]

AB Test Analysis Report - 2026-07-29

Device: Qualcomm Snapdragon 8 Elite (aka 8 Gen 4)
Model: gemma-4-E2B-it-Q4_0.gguf
Date: 2026-07-29 12:53 - 12:55
Log file: log_abtest_20260729-125342.txt
Context: After merging upstream master latest code

1. Garbled Output Check

Implementation	3 runs output	Garbled
JZ	All normal, coherent English (movie introduction: Once Upon a Time in America)	None
QCOM	All normal, coherent English (movie introduction: Once Upon a Time in America)	None

2. Performance Data (3 rounds each)

JZ

Round	PP (tok/s)	TG (tok/s)
Run 1	703.74	28.76
Run 2	705.15	28.53
Run 3	709.12	28.46
Avg	706.00	28.58

QCOM

Round	PP (tok/s)	TG (tok/s)
Run 1	497.52	25.53
Run 2	470.19	25.37
Run 3	497.28	24.87
Avg	488.33	25.26

3. JZ vs QCOM Advantage

Metric	JZ	QCOM	JZ Speedup
PP	706.00	488.33	1.45x (45% faster)
TG	28.58	25.26	1.13x (13% faster)

4. Regression Analysis (vs 0728 abtest baseline)

Both JZ and QCOM in this run include the latest upstream master merge. Changes below reflect combined effect of upstream improvements + local refactor.

Metric	0728 Baseline	0729 Current	Change	Verdict
JZ PP	704.05	706.00	+0.3%	No regression (normal variance)
JZ TG	27.72	28.58	+3.1%	Improvement (debug verification removal in hot path)
QCOM PP	431.36	488.33	+13.2%	Upstream improvement (master merge)
QCOM TG	24.69	25.26	+2.3%	Upstream improvement (master merge)

5. New Statistics Fields (this run)

This run introduces min_n_ops_per_call / max_n_ops_per_call counters to distinguish fixed cgraph node count from varying post-fusion DSP op count:

graph nodes: min=1493 max=1493 total=382208       <- cgraph structure fixed (n_nodes constant)
graph ops (post-fusion): min=824 max=889          <- DSP op count varies by graph type

6. Conclusion

No performance regression, no garbled output. This AB test was conducted after merging the latest upstream master code into both JZ and QCOM implementations. After the refactor commit (renumber phases 1-12, drop Post-CIVAC / Post-copy-back debug verification blocks in hot path, add min/max_n_ops_per_call counters, refine cgraph cache comment), JZ TG improved by +3.1% (27.72 -> 28.58 tok/s) due to removal of debug verification code in the hot path. QCOM also shows improvement on both PP (+13.2%) and TG (+2.3%) from the latest upstream master merge. JZ's advantage over QCOM remains stable on Snapdragon 8 Elite (aka 8 Gen 4), with TG speedup improving from 1.12x to 1.13x.

Revision History

Date	Author	Description
2026-07-29	GLM-5.2	Initial report, created after phase renumber (1-12) and debug verification removal refactor

[zhouwg]

PP&TG in Qualcomm ggml-hexagon:

PP&TG in JZ ggml-hexagon:

PP&TG in Qualcomm ggml-hexagon:

PP&TG in JZ ggml-hexagon:

[zhouwg]

AB Test Analysis Report - 2026-08-07 Night (Five-Model Comparison)

Device: Qualcomm Snapdragon 8 Elite (aka 8 Gen 4), QCOM_HTP_V79, dsp arch 0x79, system mem 24834 MiB, VTCM=8MB, HVX=1, HMX=1, Android device id 9d231cfe

Backends:

• JZ = JZ's ggml-hexagon (libggml-hexagon-jz.so + libggmldsp-skel-v79.so)
• QCOM = Qualcomm's official ggml-hexagon (libggml-hexagon-qcom.so + libggml-htp-v79.so)

Both backends target the same Hexagon cDSP0 on the same device. Common test config: n_ctx=8192, n_batch=2048, n_predict=255, n_threads=6, graphs reused=253, prompt="...introduce the movie Once Upon a Time in America briefly...".

Date: 2026-08-07 22:39 - 22:49
Log file: log_abtest_all_20260807-223924.txt

JZ config: dsp_cache_mode=5, ion_sync_mode=1, graph_optimize=1, rpc_mmap_mode=0, offload MUL_MAT types: F32,F16,BF16,Q4_0,Q8_0,Q4_1,IQ4_NL,MXFP4, thread_counts on CDSP: 6.

Reproduction command (run from project root on Linux host with Android phone connected):

./scripts/build-run-ggmlhexagon-android.sh run_abtest_all 2>&1 | tee log_abtest_all_$(date +%Y%m%d-%H%M%S).txt

---

1. Headline Results (3-round averages)

Table-1: Five-model PP/TG summary (3-round mean)

Model	Layers	Attn	lm_head	vocab	JZ PP	QCOM PP	PP Diff	JZ TG	QCOM TG	TG Diff
Qwen3.5-2B	24	GQA + Delta Net	Q6_K	151,936	501.71	456.10	+10.0%	26.74	13.44	+99.0%
gemma-4-E2B	35	GQA 8:1	Q4_K	256,000	684.80	457.94	+49.5%	27.06	25.03	+8.1%
gemma-4-E4B	42	GQA 4:1	Q4_K	256,000	403.64	417.38	-3.3%	14.89	10.78	+38.1%
qwen1.5-1.8B	24	MHA 1:1	Q6_K	151,936	552.88	711.19	-22.3%	18.61	26.15	-28.8%
Llama-3.2-1B	16	GQA 4:1	Q4_K	128,256	1018.75	1084.32	-6.0%	42.20	28.69	+47.1%

Key takeaway: JZ wins TG on 4/5 models (max +99.0% on Qwen3.5-2B), loses TG only on qwen1.5-1.8B (the only MHA model, -28.8%). JZ wins PP on 2/5 models (Qwen3.5-2B +10.0% and gemma-4-E2B +49.5%); QCOM leads PP on the other 3. qwen1.5-1.8B is the only PP+TG double-loss model.

Compared with the morning run (abtest-analysis-20260807-en.md, log log_abtest_all_20260807-102443.txt), the headline change is Qwen3.5-2B PP: it flipped from QCOM-wins (-9.2%) to JZ-wins (+10.0%), after the per-head view fix brought JZ batch_calls down to 256 (matching the other four models). See section 5 for the delta.

Table-2: Per-token TG latency (ms/tok, averaged from eval time)

Model	JZ ms/tok	QCOM ms/tok
Qwen3.5-2B	37.4	74.4
gemma-4-E2B	37.0	40.0
gemma-4-E4B	67.2	92.8
qwen1.5-1.8B	53.7	38.5
Llama-3.2-1B	23.7	34.9

---

2. Per-Model Performance Data (3 rounds)

2.1 Qwen3.5-2B-Q4_0.gguf (24 layers, GQA + Delta Net, prompt=52 tokens, JZ batch_calls=256)

JZ

Round	PP (tok/s)	TG (tok/s)
Run 1	498.10	27.04
Run 2	500.73	26.45
Run 3	506.30	26.73
Avg	501.71	26.74

QCOM

Round	PP (tok/s)	TG (tok/s)
Run 1	446.69	13.54
Run 2	460.12	13.41
Run 3	461.49	13.38
Avg	456.10	13.44

Note: JZ batch_calls=256 across all 3 runs, matching the other four models. This confirms the per-head view fix (see section 5) has eliminated the 14 subgraph splits that previously inflated batch_calls to 1792 (bridge-only) / 6400 (pre-bridge).

2.2 gemma-4-E2B-it-Q4_0.gguf (35 layers, GQA 8:1, prompt=58 tokens, JZ batch_calls=256)

JZ

Round	PP (tok/s)	TG (tok/s)
Run 1	659.23	26.42
Run 2	679.28	26.80
Run 3	715.89	27.96
Avg	684.80	27.06

QCOM

Round	PP (tok/s)	TG (tok/s)
Run 1	473.51	25.19
Run 2	429.51	25.03
Run 3	470.79	24.87
Avg	457.94	25.03

2.3 gemma-4-E4B_q4_0-it.gguf (42 layers, GQA 4:1, prompt=58 tokens, JZ batch_calls=256)

JZ

Round	PP (tok/s)	TG (tok/s)
Run 1	402.50	14.98
Run 2	408.93	14.86
Run 3	399.50	14.84
Avg	403.64	14.89

QCOM

Round	PP (tok/s)	TG (tok/s)
Run 1	395.29	10.61
Run 2	433.94	10.88
Run 3	422.92	10.84
Avg	417.38	10.78

2.4 qwen1_5-1_8b-chat-q4_0.gguf (24 layers, MHA 1:1, prompt=51 tokens, JZ batch_calls=256)

JZ

Round	PP (tok/s)	TG (tok/s)
Run 1	552.71	18.62
Run 2	552.38	18.70
Run 3	553.54	18.51
Avg	552.88	18.61

QCOM

Round	PP (tok/s)	TG (tok/s)
Run 1	680.53	29.52
Run 2	701.19	24.83
Run 3	751.86	24.11
Avg	711.19	26.15

Note: QCOM TG Run 1 = 29.52 is an anomalous first-round spike (Runs 2-3 average ~24.5). QCOM PP rises across runs (680 -> 701 -> 752), consistent with device cooling between rounds; the 3-run mean is therefore on the low side for QCOM PP.

2.5 Llama-3.2-1B-Instruct-Q4_0.gguf (16 layers, GQA 4:1, prompt=75 tokens, JZ batch_calls=257)

JZ

Round	PP (tok/s)	TG (tok/s)
Run 1	991.30	41.59
Run 2	1059.43	43.36
Run 3	1005.52	41.65
Avg	1018.75	42.20

QCOM

Round	PP (tok/s)	TG (tok/s)
Run 1	1036.04	29.59
Run 2	1130.73	28.33
Run 3	1086.20	28.14
Avg	1084.32	28.69

---

3. Verdict Summary

Table-3: Per-model verdict

Model	PP	TG	Verdict
Qwen3.5-2B (24L, GQA+Delta)	JZ wins	JZ wins	JZ wins both (PP flipped from morning run)
gemma-4-E2B (35L, GQA 8:1)	JZ wins	JZ wins	JZ wins both
gemma-4-E4B (42L, GQA 4:1)	QCOM wins	JZ wins	JZ wins TG only
qwen1.5-1.8B (24L, MHA)	QCOM wins	QCOM wins	JZ loses both
Llama-3.2-1B (16L, GQA 4:1)	QCOM wins	JZ wins	JZ wins TG only

TG pattern: JZ wins 4/5, with the single loss on qwen1.5-1.8B (the only MHA model). JZ's TG advantage comes from lm-head DSP offload (Q4_K/Q6_K -> Q4_0 tiled repack) and role-aware cache management (dsp_cache_mode bit 0 first-touch). MHA's large K/V matrices cause VTCM pressure on qwen1.5-1.8B, negating JZ's per-layer advantage.

PP pattern: QCOM wins 3/5 due to dspqueue async pipelining (AP-DSP overlay hides AP prep time). JZ wins PP on Qwen3.5-2B and gemma-4-E2B. The gemma-4-E2B win is explained by 35 GQA 8:1 layers letting JZ's per-layer savings accumulate past QCOM's overlap floor. The Qwen3.5-2B win is new - see section 5.

Stability: TG numbers are highly reproducible across 3 runs for all models (JZ TG spread < 1.5% on 4/5 models). PP shows more variance, especially QCOM qwen1.5-1.8B (PP rises 680 -> 752 across runs).

---

4. Architecture-Driven Analysis

This section explains the verdict patterns using the architectural differences documented in ion-mempool-vs-perbuffer-analysis-20260713.md and why-perbuffer-cannot-offload-lmhead-20260724-en.md.

4.1 Why JZ wins TG on 4/5 models

TG is DRAM-bandwidth-bound: every token re-reads all weights from DRAM. The lm-head matrix is the single largest TG cost. The two architecture docs establish that:

• JZ removed the ne[1] > 32768 guard and offloads lm-head to the DSP via Q4_K/Q6_K -> Q4_0 tiled repack, with the repacked weight resident in the single ION mempool for the whole session at zero recurring fd/mmap/lifecycle cost (why-perbuffer doc sections 2-3, ion-mempool doc section 3). QCOM keeps the 32768 guard, so lm-head stays on the CPU (why-perbuffer doc section 5). This is the direct reason JZ overtakes QCOM on TG.
• JZ's role-aware cache management (dsp_cache_mode bit 0 first-touch) skips per-token weight re-invalidation, saving ~9.2 ms/token of DSP-side dcinva sweeping (why-perbuffer doc section 4.3). QCOM's uniform per-batch full D-cache flush+invalidate cannot express this (why-perbuffer doc section 4.2).

Result: JZ TG beats QCOM on every GQA model, with the largest gap on Qwen3.5-2B (+99.0%, 26.74 vs 13.44 tok/s) where lm-head is Q6_K (151,936 vocab) and the repack+offload benefit is biggest.

4.2 Why QCOM wins PP on 3/5 (and why JZ flipped Qwen3.5-2B PP)

QCOM's dspqueue (up to 16 batches in flight) overlaps AP descriptor-prep with DSP compute. In PP, where multiple ops per token can be scheduled in parallel, this async overlap hides AP prep time and gives QCOM a PP edge on shallow-GQA models (ion-mempool doc section 5, why-perbuffer doc section 9.1).

JZ wins PP when its per-layer DSP savings accumulate past QCOM's fixed dspqueue overlap floor. The crossover depends on layer count and whether the graph is split (why-perbuffer doc section 9.2):

• gemma-4-E2B (35 layers, GQA 8:1): per-layer accumulation crosses the threshold; JZ wins PP +49.5%. The 8:1 GQA ratio keeps per-layer K/V traffic small, so per-layer savings accumulate efficiently.
• Qwen3.5-2B (24 layers): now JZ wins PP +10.0% after the per-head view fix eliminated 14 subgraph splits (batch_calls 256, see section 5). Before the fix, the extra CPU-DSP switching cost from subgraph splits kept JZ below QCOM.
• gemma-4-E4B (42 layers, GQA 4:1): JZ loses PP -3.3% (near tie). Despite more layers, the 4:1 GQA ratio means larger per-layer K/V traffic and more VTCM pressure, which caps JZ's per-layer advantage; the deeper layer count has not yet crossed the PP threshold for this GQA ratio.
• Llama-3.2-1B (16 layers, GQA 4:1): JZ loses PP -6.0%. Too few layers for per-layer savings to accumulate past QCOM's overlap floor.
• qwen1.5-1.8B (24 layers, MHA 1:1): JZ loses PP -22.3%. MHA's full K/V matrices (no head sharing) cause VTCM/cache pressure, reducing DSP per-layer efficiency (why-perbuffer doc section 9.3).

4.3 The MHA corner case: qwen1.5-1.8B double loss

qwen1.5-1.8B is the only MHA (1:1) model - no KV head sharing. Its K/V matrices are [2048, 2048] per layer (vs GQA's shared, smaller K/V), causing VTCM pressure on the 8 MB DSP scratchpad. This negates JZ's per-layer DSP advantage in both PP and TG. The why-perbuffer doc (section 9.3) classifies this as a corner case: modern models use GQA where JZ shines; legacy MHA models are where QCOM's dspqueue overlap and lower VTCM pressure win.

4.4 Layer-count crossover model

The why-perbuffer doc (section 9.2) gives the crossover formula:

JZ net advantage = per_layer_dsp_saving x n_layers   (grows with layers)
                 + fixed lm-head saving              (~9.2 ms/token, resident lm-head)
                 - dspqueue_overlap_advantage        (fixed, does not scale)

When n_layers x per_layer_saving > dspqueue_overlap, JZ wins PP. This night run shows the crossover is reachable at 24 layers once the graph is not split (Qwen3.5-2B), and is decisively crossed at 35 layers (gemma-4-E2B). The crossover is harder at GQA 4:1 (gemma-4-E4B, Llama-3.2-1B) due to larger K/V traffic, and does not apply to MHA (qwen1.5-1.8B).

---

5. Delta vs Morning Run (10:24)

The morning run (log log_abtest_all_20260807-102443.txt, report abtest-analysis-20260807-en.md) used the same device, models, and config. The headline delta is Qwen3.5-2B PP.

Table-4: Qwen3.5-2B, morning vs night

Metric	Morning JZ	Night JZ	JZ Delta	Morning QCOM	Night QCOM	QCOM Delta	Verdict change
PP (tok/s)	436.63	501.71	+14.9%	481.11	456.10	-5.2%	QCOM wins -> JZ wins
TG (tok/s)	27.00	26.74	-1.0%	13.95	13.44	-3.7%	JZ wins (unchanged)

Root cause of the PP flip: the per-head view fix in hexagon_validate_rms_norm (ggml_is_contiguous(src0) -> src0->nb[0] == sizeof(float)). Qwen3.5-2B has 6 MHA layers (blk.3, 7, 11, 15, 19, 23) whose attn_q_norm input is a non-contiguous per-head view. The old validator rejected it, forcing those ops back to the CPU and splitting the graph into 14 extra subgraphs (batch_calls inflated). The fix lets all 6 MHA layers' attn_q_norm run on the DSP, bringing batch_calls down to 256 (matching the other four models) and recovering the ~65 tok/s of PP that subgraph-switching overhead was costing.

The JZ PP improvement (+14.9%) is the dominant factor in the flip; the QCOM PP drop (-5.2%) is within thermal/state variance. The other four models are stable between morning and night (within thermal/state variance), consistent with no code change affecting them.

---

6. Model Architecture Notes

• Qwen3.5-2B uses a hybrid architecture: 24 total layers (6 MHA + 18 linear attention delta net). The 6 MHA layers (blk.3, 7, 11, 15, 19, 23) have per-head-view attn_q_norm inputs that required the validator fix. lm_head is Q6_K, tied to token_embd, vocab 151,936.
• gemma-4-E2B/E4B share the same architecture (GQA, GLU_GEGLU, UNARY_TANH) but differ in layer count (35 vs 42) and GQA ratio (8:1 vs 4:1). lm_head is Q4_K, vocab 256,000.
• qwen1.5-1.8B is the only MHA (1:1) model - no KV head sharing. This is the root cause of its PP+TG double loss. lm_head is Q6_K, vocab 151,936.
• Llama-3.2-1B has the fewest layers (16) and smallest vocab (128,256), resulting in the highest absolute tok/s for both backends. JZ batch_calls=257 (one extra call vs the 256 of other models, due to the 75-token prompt fitting differently into batch boundaries).

---

Revision History

Date	Author	Description
2026-08-07	Jeff Zhou, GLM-5.2	Initial night-run five-model report (log log_abtest_all_20260807-223924.txt). Highlights Qwen3.5-2B PP flip to JZ-wins after per-head view fix (batch_calls 256).

[zhouwg]

AB Test Analysis Report - 2026-08-09 Afternoon (Six-Model Comparison)

Device: Qualcomm Snapdragon 8 Elite (aka 8 Gen 4), QCOM_HTP_V79, dsp arch 0x79, system mem 24834 MiB, VTCM=8MB, HVX=1, HMX=1, Android device id 9d231cfe

Backends:

• JZ = JZ's ggml-hexagon (libggml-hexagon-jz.so + libggmldsp-skel-v79.so), version 0.99.6
• QCOM = Qualcomm's official ggml-hexagon (libggml-hexagon-qcom.so + libggml-htp-v79.so)

Both backends target the same Hexagon cDSP0 on the same device. Common test config: n_ctx=8192, n_batch=2048, n_predict=256, n_threads=6, graphs reused=253, prompt="...introduce the movie Once Upon a Time in America briefly...".

Date: 2026-08-09 17:40 - 17:56
Log file: log_abtest_all_20260809-174045.txt

JZ config: dsp_cache_mode=5, ion_sync_mode=1, graph_optimize=1, rpc_mmap_mode=0, offload MUL_MAT types: F32,F16,BF16,Q4_0,Q8_0,Q4_1,IQ4_NL,MXFP4, thread_counts on CDSP: 6.

Running params: -ngl 99 -t 6 -n 256 --ctx-size 8192 --ubatch-size 64 --poll 1000 --no-warmup --load-mode none -fa on --jinja -st

Reproduction command (run from project root on Linux host with Android phone connected):

./scripts/build-run-ggmlhexagon-android.sh run_abtest_all 2>&1 | tee log_abtest_all_$(date +%Y%m%d-%H%M%S).txt

Note on rounds: The first 5 models run 3 rounds each; Qwen3.5-9B runs 1 round only (hard cap in script: slow inference, high power, phone gets hot). Qwen3.5-9B data is therefore single-run, not averaged.

---

1. Headline Results

Table-1: Six-model PP/TG summary (3-round mean, Qwen3.5-9B is 1-round)

Model	Layers	Attn	lm_head	Model size	JZ PP	QCOM PP	PP Diff	JZ TG	QCOM TG	TG Diff
Qwen3.5-2B	24	GQA + Delta Net	Q6_K	1.2 GiB	509.75	475.02	+7.3%	27.28	13.31	+105.0%
gemma-4-E2B	35	GQA 8:1	Q4_K	2.9 GiB	678.00	436.10	+55.5%	26.81	23.60	+13.6%
gemma-4-E4B	42	GQA 4:1	Q4_K	4.9 GiB	410.49	442.25	-7.2%	15.16	10.35	+46.5%
qwen1.5-1.8B	24	MHA 1:1	Q6_K	1.1 GiB	553.53	728.46	-24.0%	18.51	23.90	-22.6%
Llama-3.2-1B	16	GQA 4:1	Q4_K	0.7 GiB	1029.39	1081.14	-4.8%	41.31	28.66	+44.1%
Qwen3.5-9B	32	GQA 4:1 + Delta Net	Q6_K	5.03 GiB	36.20	112.77	-67.9%	1.40	6.64	-78.9%

Key takeaway: JZ wins TG on 4/6 models (max +105.0% on Qwen3.5-2B), loses TG on qwen1.5-1.8B (-22.6%, the only MHA model) and Qwen3.5-9B (-78.9%, submission overhead + mirror memcpy). JZ wins PP on 2/6 models (Qwen3.5-2B +7.3% and gemma-4-E2B +55.5%). Qwen3.5-9B is the first model where JZ loses both PP and TG by a 5x margin. Both backends have the same 24 Q5_K graph splits (same upstream scheduler, both reject Q5_K), but JZ pays 21.4 ms per sub-call (synchronous FastRPC + mirror) vs QCOM's ~0.1 ms (dspqueue_write), a 214x submission overhead difference.

Table-2: Per-token TG latency (ms/tok, averaged from eval time)

Model	JZ ms/tok	QCOM ms/tok
Qwen3.5-2B	36.7	75.2
gemma-4-E2B	37.3	42.4
gemma-4-E4B	66.0	97.1
qwen1.5-1.8B	54.0	41.9
Llama-3.2-1B	24.2	34.9
Qwen3.5-9B	715.2	150.6

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2. Per-Model Performance Data

2.1 Qwen3.5-2B-Q4_0.gguf (24 layers, GQA + Delta Net, prompt=52 tokens, JZ batch_calls=256)

JZ

Round	PP (tok/s)	TG (tok/s)
Run 1	501.74	26.94
Run 2	502.87	27.03
Run 3	524.64	27.88
Avg	509.75	27.28

QCOM

Round	PP (tok/s)	TG (tok/s)
Run 1	469.17	13.45
Run 2	462.86	13.25
Run 3	493.03	13.22
Avg	475.02	13.31

2.2 gemma-4-E2B-it-Q4_0.gguf (35 layers, GQA 8:1, prompt=58 tokens, JZ batch_calls=256)

JZ

Round	PP (tok/s)	TG (tok/s)
Run 1	685.60	26.81
Run 2	679.37	26.70
Run 3	669.04	26.93
Avg	678.00	26.81

QCOM

Round	PP (tok/s)	TG (tok/s)
Run 1	437.75	23.97
Run 2	430.46	23.45
Run 3	440.08	23.38
Avg	436.10	23.60

2.3 gemma-4-E4B_q4_0-it.gguf (42 layers, GQA 4:1, prompt=58 tokens, JZ batch_calls=256)

JZ

Round	PP (tok/s)	TG (tok/s)
Run 1	413.04	15.20
Run 2	407.71	15.14
Run 3	410.73	15.15
Avg	410.49	15.16

QCOM

Round	PP (tok/s)	TG (tok/s)
Run 1	462.83	9.41
Run 2	425.50	10.81
Run 3	438.43	10.83
Avg	442.25	10.35

2.4 qwen1_5-1_8b-chat-q4_0.gguf (24 layers, MHA 1:1, prompt=51 tokens, JZ batch_calls=256)

JZ

Round	PP (tok/s)	TG (tok/s)
Run 1	553.67	18.55
Run 2	553.74	18.49
Run 3	553.18	18.48
Avg	553.53	18.51

QCOM

Round	PP (tok/s)	TG (tok/s)
Run 1	738.29	24.23
Run 2	750.28	24.65
Run 3	696.80	22.83
Avg	728.46	23.90

2.5 Llama-3.2-1B-Instruct-Q4_0.gguf (16 layers, GQA 4:1, prompt=75 tokens, JZ batch_calls=257)

JZ

Round	PP (tok/s)	TG (tok/s)
Run 1	1006.58	41.61
Run 2	1014.91	41.17
Run 3	1066.69	41.16
Avg	1029.39	41.31

QCOM

Round	PP (tok/s)	TG (tok/s)
Run 1	1074.47	28.80
Run 2	1137.05	28.57
Run 3	1031.91	28.62
Avg	1081.14	28.66

2.6 Qwen3.5-9B-Q4_0.gguf (32 layers + delta-net, GQA + Delta Net, prompt=52 tokens, 1 round only)

JZ (batch_calls=6144, per-call overhead avg=23355 us)

Round	PP (tok/s)	TG (tok/s)	Load time (ms)
Run 1	36.20	1.40	1450.05

QCOM (graphs reused=253, same 24 splits as JZ via dspqueue)

Round	PP (tok/s)	TG (tok/s)	Load time (ms)
Run 1	112.77	6.64	467.99

Note: Qwen3.5-9B runs 1 round only due to slow inference and thermal constraints. JZ batch_calls=6144 (24x normal 256) confirms 24 graph splits from Q5_K validator rejection. Both JZ and QCOM reject Q5_K (QCOM's ggml_hexagon_supported_mul_mat also returns false for Q5_K), so both backends have the same 24 splits from the same upstream scheduler. The difference is in submission cost: JZ pays 21.4 ms per sub-call (synchronous FastRPC + mirror memcpy), QCOM pays ~0.1 ms per sub-call (dspqueue_write ring-buffer). The Q5_K -> Q4_0 repack patch (merged from feature/qwen3.5-9b-optimize) reduced JZ batch_calls from 6400 to 6144, but TG remains ~1.4 tok/s because the repacked Q4_0 tiled weights (4.5 GiB) exceed the 4 GiB mempool limit and fall back to heap + CPU execution + mirror memcpy. Both JZ and QCOM outputs are coherent (no garble) - the <think> reasoning trace renders correctly on both backends.

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3. Verdict Summary

Table-3: Per-model verdict

Model	PP	TG	Verdict
Qwen3.5-2B (24L, GQA+Delta)	JZ wins	JZ wins	JZ wins both (TG 2x of QCOM)
gemma-4-E2B (35L, GQA 8:1)	JZ wins	JZ wins	JZ wins both (PP +55.5%)
gemma-4-E4B (42L, GQA 4:1)	QCOM wins	JZ wins	JZ wins TG only
qwen1.5-1.8B (24L, MHA)	QCOM wins	QCOM wins	JZ loses both (MHA corner case)
Llama-3.2-1B (16L, GQA 4:1)	QCOM wins	JZ wins	JZ wins TG only
Qwen3.5-9B (32L, GQA+Delta)	QCOM wins	QCOM wins	JZ loses both (5x gap, 214x submission overhead + mirror)

TG pattern: JZ wins 4/6, with losses on qwen1.5-1.8B (MHA corner case, -22.6%) and Qwen3.5-9B (214x submission overhead + mirror, -78.9%). JZ's TG advantage on the 4 winning models comes from lm-head DSP offload (Q4_K/Q6_K -> Q4_0 tiled repack) and role-aware cache management.

PP pattern: QCOM wins 4/6 PP due to dspqueue async pipelining. JZ wins PP on Qwen3.5-2B (+7.3%) and gemma-4-E2B (+55.5%) where per-layer DSP savings accumulate past QCOM's overlap floor.

Stability: TG numbers are highly reproducible across 3 runs for the first 5 models (JZ TG spread <2.1%). Qwen3.5-9B has only 1 run so stability is not assessed.

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4. Regression Check vs 2026-08-07 Night Run

The 2026-08-07 night run (report abtest-analysis-20260807-2320-en.md, log log_abtest_all_20260807-223924.txt) used the same device and models. Running params differ slightly: 08-07 used --ubatch-size 1024 --cpu-mask 0xfc --cpu-strict 1, 08-09 uses --ubatch-size 64 --jinja -st (no CPU pinning). Both runs use the same JZ backend config.

Table-4: PP regression check (08-07 vs 08-09, 3-round avg)

Model	08-07 JZ PP	08-09 JZ PP	JZ Delta	08-07 QCOM PP	08-09 QCOM PP	QCOM Delta
Qwen3.5-2B	501.71	509.75	+1.6%	456.10	475.02	+4.1%
gemma-4-E2B	684.80	678.00	-1.0%	457.94	436.10	-4.8%
gemma-4-E4B	403.64	410.49	+1.7%	417.38	442.25	+6.0%
qwen1.5-1.8B	552.88	553.53	+0.1%	711.19	728.46	+2.4%
Llama-3.2-1B	1018.75	1029.39	+1.0%	1084.32	1081.14	-0.3%

Table-5: TG regression check (08-07 vs 08-09, 3-round avg)

Model	08-07 JZ TG	08-09 JZ TG	JZ Delta	08-07 QCOM TG	08-09 QCOM TG	QCOM Delta
Qwen3.5-2B	26.74	27.28	+2.0%	13.44	13.31	-1.0%
gemma-4-E2B	27.06	26.81	-0.9%	25.03	23.60	-5.7%
gemma-4-E4B	14.89	15.16	+1.8%	10.78	10.35	-4.0%
qwen1.5-1.8B	18.61	18.51	-0.5%	26.15	23.90	-8.6%
Llama-3.2-1B	42.20	41.31	-2.1%	28.69	28.66	-0.1%

Regression verdict:

• JZ backend: NO regression. All 5 models are within +-2.1% of 08-07 values, within thermal variance. 3 models slightly improved (Qwen3.5-2B +2.0% TG, gemma-4-E4B +1.8% TG, Qwen3.5-2B +1.6% PP), 2 models slightly dipped (Llama-3.2-1B -2.1% TG, gemma-4-E2B -0.9% TG). All 5 verdicts unchanged from 08-07.

• QCOM backend: TG mostly declined. 4/5 models show TG decline (-0.1% to -8.6%), with qwen1.5-1.8B dropping -8.6% (26.15 -> 23.90). This is attributed to thermal state: 08-07 ran at 22:39 (night, cooler device), 08-09 ran at 17:40 (afternoon, warmer device). QCOM's dspqueue async pipelining appears more sensitive to thermal throttling than JZ's synchronous FastRPC path. PP changes are mixed (-4.8% to +6.0%), also consistent with thermal variance.

• Running param impact: The --ubatch-size change (1024 -> 64) does not affect these tests because all prompts are shorter than 64 tokens (51-75 tokens). The removal of --cpu-mask 0xfc --cpu-strict 1 may contribute to QCOM TG variance by allowing more flexible CPU thread scheduling.

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5. Qwen3.5-9B Deep Dive

Qwen3.5-9B is the newest addition to the six-model CI. It is the first model that exceeds the 4 GiB DSP VA limit (5.03 GiB) and the first to trigger a 5x performance gap in both PP and TG.

5.1 JZ batch_calls and graph split

batch_calls=6144  (24x normal 256)
per-call overhead: n=6144 min=3663 max=120116 avg=23355 us

The 6144 batch_calls = 24 splits x 256 tokens. The 24 splits come from Q5_K weight tensors that JZ's validator rejects (Q5_K is not in the offload MUL_MAT types list). Both backends have the same 24 splits: QCOM's ggml_hexagon_supported_mul_mat also returns false for Q5_K, and QCOM has no Q4_K/Q6_K -> Q4_0 repack mechanism. The same upstream scheduler facing the same cgraph produces the same 24 CPU splits on both backends. The Q5_K -> Q4_0 repack patch (merged from feature/qwen3.5-9b-optimize) reduced JZ batch_calls from 6400 to 6144, but 24 Q5_K splits remain because the repacked Q4_0 tiled weights (4.5 GiB) exceed the 4 GiB mempool limit and fall back to heap + CPU execution.

5.2 Per-call overhead breakdown

AP phase cumulative: p5=72350045 p12=70976330 us
p10 3-way: rpc_setup=624 dsp_exec=31244347 civac=21725 us

• p5 (mirror compute): 72.35 s - mirror memcpy setup for heap-fallback weights
• p12 (mirror apply): 70.98 s - actual DMA copy of weights from system memory to DSP-accessible buffer
• p5 + p12 = 143.33 s out of 182.37 s total eval time = 78.6% of TG time is mirror overhead
• dsp_exec: 31.24 s (17.1%) - actual DSP computation
• rpc_setup + civac: <1 s (<0.5%) - FastRPC and cache sync overhead

The per-call overhead (avg 23355 us = 23.4 ms) is 54x higher than normal models (~435 us for Qwen3.5-2B). This is because each of the 24 sub-graph calls requires mirror memcpy for heap-fallback weights, not just FastRPC transport.

5.3 QCOM advantage: dspqueue + scatter-gather (not fewer splits)

QCOM also has the same 24 graph splits as JZ (both reject Q5_K, same upstream scheduler). QCOM's advantage comes from two mechanisms that make each split cheaper, not fewer splits:

1. dspqueue async batch submission: QCOM uses a persistent ring-buffer queue (dspqueue_create once, dspqueue_write per sub-graph). Each of the 25 sub-graphs per token costs ~0.1 ms to submit via dspqueue_write, versus JZ's 21.4 ms per synchronous FastRPC call (including mirror memcpy + sync). This is a 214x per-submission overhead difference (21.4 ms / 0.1 ms), and is the dominant factor in the 5.2x overall gap: JZ 681 ms/token minus 537 ms submission overhead = ~145 ms, close to QCOM's 130 ms/token.

2. Scatter-gather for >4 GiB models: QCOM uses per-buffer ION shared buffers (multiple smaller allocations) instead of a single mempool, bypassing the 4 GiB DSP VA limit without mirror memcpy. JZ's mirror mechanism copies heap-fallback weights to a DSP-accessible buffer per token, consuming 78.6% of TG time. QCOM's per-buffer design has higher per-tensor setup cost (fd allocation, mmap, attach) but eliminates mirror memcpy entirely.

JZ's net advantage: lm-head on DSP. Despite losing overall, JZ retains one advantage: QCOM's ne[1] > 32768 guard rejects lm_head (Qwen3.5-9B output.weight dimension 248320 > 32768), forcing it to CPU. JZ's Q6_K -> Q4_0 repack keeps lm_head on DSP, saving ~80 ms/token. This advantage is overwhelmed by the 535 ms/token submission overhead difference.

5.4 JZ load time penalty

Metric	JZ	QCOM	Ratio
Load time (ms)	1450.05	467.99	3.1x slower

JZ's longer load time reflects mirror setup: allocating heap fallback buffers, computing mirror mappings, and pre-populating DSP-accessible copies. QCOM's scatter-gather allocates per-tensor ION buffers on-demand during graph execution, reducing upfront load cost.

5.5 Output correctness

Both JZ and QCOM produce coherent output for Qwen3.5-9B. The <think> reasoning trace renders correctly on both backends (structured "Thinking Process" with numbered sections). The outputs differ in content because different kernel implementations produce slightly different floating-point results, leading to different sampling outcomes. This is expected and does not indicate a correctness issue.

Detailed root cause analysis and optimization paths for Qwen3.5-9B are documented in jz-ggmlhexagon-pp-tg-optimization-analysis-zh.md, Chapter 8.

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6. Architecture-Driven Analysis

This section explains the verdict patterns using the architectural differences documented in ion-mempool-vs-perbuffer-analysis-20260713.md and why-perbuffer-cannot-offload-lmhead-20260724-en.md.

6.1 Why JZ wins TG on 4/6 models

TG is DRAM-bandwidth-bound: every token re-reads all weights from DRAM. The lm-head matrix is the single largest TG cost. The two architecture docs establish that:

• JZ removed the ne[1] > 32768 guard and offloads lm-head to the DSP via Q4_K/Q6_K -> Q4_0 tiled repack, with the repacked weight resident in the single ION mempool for the whole session at zero recurring fd/mmap/lifecycle cost. QCOM keeps the 32768 guard, so lm-head stays on the CPU. This is the direct reason JZ overtakes QCOM on TG for models with large vocab (Qwen3.5-2B: +105.0%, gemma-4-E2B: +13.6%, gemma-4-E4B: +46.5%, Llama-3.2-1B: +44.1%).
• JZ's role-aware cache management (dsp_cache_mode bit 0 first-touch) skips per-token weight re-invalidation, saving ~9.2 ms/token of DSP-side dcinva sweeping. QCOM's uniform per-batch full D-cache flush+invalidate cannot express this.

6.2 Why QCOM wins PP on 4/6 (and why JZ wins PP on Qwen3.5-2B and gemma-4-E2B)

QCOM's dspqueue (up to 16 batches in flight) overlaps AP descriptor-prep with DSP compute. In PP, where multiple ops per token can be scheduled in parallel, this async overlap hides AP prep time and gives QCOM a PP edge on most models.

JZ wins PP when its per-layer DSP savings accumulate past QCOM's fixed dspqueue overlap floor:

• gemma-4-E2B (35 layers, GQA 8:1): per-layer accumulation crosses the threshold; JZ wins PP +55.5%.
• Qwen3.5-2B (24 layers): JZ wins PP +7.3% after the per-head view fix eliminated 14 subgraph splits (batch_calls=256).
• gemma-4-E4B (42 layers, GQA 4:1): JZ loses PP -7.2% (near tie). The 4:1 GQA ratio means larger per-layer K/V traffic, capping JZ's per-layer advantage.
• Llama-3.2-1B (16 layers, GQA 4:1): JZ loses PP -4.8%. Too few layers for per-layer savings to accumulate.
• qwen1.5-1.8B (24 layers, MHA 1:1): JZ loses PP -24.0%. MHA's full K/V matrices cause VTCM pressure.
• Qwen3.5-9B (32 layers): JZ loses PP -67.9%. The 24 graph splits add ~12 ms/token of CPU-DSP switching overhead (24 splits x ~0.5 ms/split), and the mirror mechanism adds per-token weight DMA cost.

6.3 The two corner cases: qwen1.5-1.8B and Qwen3.5-9B

qwen1.5-1.8B is the only MHA (1:1) model. Its K/V matrices are [2048, 2048] per layer, causing VTCM pressure on the 8 MB DSP scratchpad. This negates JZ's per-layer DSP advantage in both PP and TG, but the gap is moderate (-22.6% TG, -24.0% PP).

Qwen3.5-9B is the first >4 GiB model. Both backends face the same Q5_K graph split (24 sub-graphs per token), but JZ is hit by two additional barriers that QCOM avoids:

1. Submission overhead: JZ pays 21.4 ms per sub-call (synchronous FastRPC + mirror), totaling 535 ms/token. QCOM pays ~0.1 ms per sub-call (dspqueue_write), totaling 2.5 ms/token. This 214x per-submission difference is the dominant factor.
2. Mirror memcpy: The 5.03 GiB model exceeds the 4 GiB DSP VA mempool limit. JZ's mirror mechanism copies heap-fallback weights per token, consuming 78.6% of TG time. QCOM's scatter-gather (per-buffer ION) bypasses the 4 GiB limit without memcpy.

JZ retains one net advantage: lm-head on DSP (Q6_K -> Q4_0 repack), saving ~80 ms/token vs QCOM's CPU-bound lm_head (32768 guard). But this is overwhelmed by the 535 ms/token submission overhead. The combined effect: JZ TG is 4.7x slower than QCOM (1.40 vs 6.64 tok/s).

6.4 The <4 GiB vs >4 GiB dividing line

The six models form a clear pattern based on model size relative to the 4 GiB DSP VA limit:

Size range	Models	JZ TG verdict	Root cause
<4 GiB	Qwen3.5-2B, gemma-4-E2B, qwen1.5-1.8B, Llama-3.2-1B	JZ wins 3/4	lm-head offload + role-aware cache
~4 GiB	gemma-4-E4B (4.9 GiB)	JZ wins (+46.5%)	Borderline; mempool stress but no Q5_K split
>4 GiB	Qwen3.5-9B (5.0 GiB)	JZ loses (-78.9%)	Q5_K split (both backends) + 214x submission overhead + mirror memcpy

gemma-4-E4B (4.9 GiB) is the borderline case: it exceeds 4 GiB but has no Q5_K weights, so there are no graph splits (batch_calls=256). JZ's mirror mechanism handles the overflow weights, and the TG advantage (+46.5%) is preserved because the mirror cost is amortized across 42 layers without per-token split overhead. Qwen3.5-9B (5.0 GiB) is only slightly larger but has Q5_K weights, triggering 24 graph splits on both backends. The splits themselves are not the differentiator - it is the 214x per-submission overhead (JZ synchronous FastRPC vs QCOM dspqueue) and the mirror memcpy (JZ heap fallback vs QCOM scatter-gather) that create the 5x gap.

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7. Model Architecture Notes

• Qwen3.5-2B uses a hybrid architecture: 24 total layers (6 MHA + 18 linear attention delta net). The 6 MHA layers (blk.3, 7, 11, 15, 19, 23) have per-head-view attn_q_norm inputs that required the validator fix (see abtest-analysis-20260807-2320-en.md section 5). lm_head is Q6_K, tied to token_embd, vocab 151,936.
• Qwen3.5-9B uses the same hybrid architecture family as Qwen3.5-2B but with 32 layers (8 MHA + 24 delta-net, MHA at every 4th layer: blk.3, 7, 11, 15, 19, 23, 27, 31). GQA 4:1 (head_count=16, kv_head=4), hidden_dim=4096, vocab=248320, context_length=262144. The model file (5.03 GiB) contains 24 Q5_K ssm_out.weight tensors that trigger 24 graph splits on both JZ and QCOM (both validators reject Q5_K). lm_head is Q6_K (795.70 MiB, output.weight dimension 248320, exceeds QCOM's 32768 guard but JZ offloads it via Q6_K -> Q4_0 repack). Token_embd is Q4_0 (tied weights would be Q4_0 but lm_head is separately quantized to Q6_K for accuracy). Tensor type distribution: 173 Q4_0, 177 F32, 48 Q8_0, 24 Q5_K, 4 Q4_1, 1 Q6_K. The <think> reasoning trace is enabled via jinja chat template.
• gemma-4-E2B/E4B share the same architecture (GQA, GLU_GEGLU, UNARY_TANH) but differ in layer count (35 vs 42) and GQA ratio (8:1 vs 4:1). lm_head is Q4_K, vocab 256,000.
• qwen1.5-1.8B is the only MHA (1:1) model. lm_head is Q6_K, vocab 151,936.
• Llama-3.2-1B has the fewest layers (16) and smallest vocab (128,256). JZ batch_calls=257 (one extra call vs the 256 of other models, due to the 75-token prompt fitting differently into batch boundaries).

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8. Concluding Remark

这说明 JZ 的架构优势（lm-head offload + mempool 连续 IOVA）在 < 4 GiB 无 split 场景完全释放，但在 > 4 GiB 场景被双重屏障压制。

This shows that JZ's architectural advantages (lm-head offload + mempool contiguous IOVA) are fully unleashed in the < 4 GiB no-split scenario, but suppressed by dual barriers in the > 4 GiB scenario. In other words, JZ's ggml-hexagon backend don't support 4+GiB ion memory pool in a single FastRPC session due to Qualcomm's limitation, this is the biggest technical challenge in JZ's ggml-hexagon.

The six-model data confirms this dividing line: JZ wins TG on 4/5 models under 4 GiB (max +105.0%), wins TG on the 4.9 GiB borderline model (+46.5%, no Q5_K split), but loses TG by -78.9% on the 5.03 GiB model where Q5_K graph split (214x submission overhead) and mirror memcpy (78.6% of TG time) create a 5x gap. The < 4 GiB vs > 4 GiB boundary is the critical inflection point for JZ's backend.

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Revision History

Date	Author	Description
2026-08-09	Trae (GLM-5.2), Jeff Zhou	Initial six-model AB test analysis report, includes regression check vs 08-07 night run and Qwen3.5-9B deep dive