perf(qwen35): fused Q4K/Q6K->int8 dequant + token-parallel GDN conv + lane-parallel fallback attention (+78% pp @32k on top of #465) by James-CUDA · Pull Request #464 · gittensor-ai-lab/sparkinfer

James-CUDA · 2026-07-16T06:01:51Z

Summary

Three profiling-guided fixes to the Qwythos (Qwen3.5) batched-prefill path. Rebased on and composing with the merged #463 (half-precision smem tiles) and #465 (int8-MMA prefill attention): the two big wins here are in stages #465 doesn't touch, and they stack on top of it.

Fused Q4_K/Q6_K → int8-row dequant (dequant_gguf.cu). Every projection dequantized the weight to a bf16 scratch (2B/value written) then re-read and row-quantized it to int8 for the perf(qwen35): int8 tensor-core prefill GEMM for Qwythos [DRAFT — awaits #398] #422 int8 tensor-core GEMM. deq_rows_i8_kernel decodes superblocks in registers and writes int8 + per-row scale directly (1B/value, no bf16 bounce), one block per row instead of one thread per superblock. The N <= 8192 int8-GEMM gate is also lifted — int8 scratch moves to its own arena, so an alloc failure at huge N degrades to the bf16 GEMMs instead of the token loop (this is why main's pp32k ran bf16 GEMMs until now).
Token-parallel GDN conv (batched_prefill.cu). pf_gdn_conv_kernel looped all N tokens sequentially with a __syncthreads() per token on only 64 blocks (2.7 ms/layer, ~21 ms/layer at 32k). The causal 4-tap conv reads only in-buffer raw inputs (the prompt starts at position 0), so it is token-parallel: one block per (token, head), ~0.1 ms/layer. Same taps/SiLU/L2-norm math and conv-state layout. SPARKINFER_PREFILL_GDN_CONV_SEQ=1 restores.
Lane-parallel prefill attention (prefill_attn_window.cu) — written before perf(qwen35): int8 tensor-core prefill attention for Qwythos #465 landed; with perf(qwen35): int8 tensor-core prefill attention for Qwythos #465's MMA kernel default-on this now only upgrades the SPARKINFER_PREFILL_ATTN_MMA=0 fallback path (the scalar kernels walk keys one per query-warp with a 5-shuffle reduce + two dependent expf per key; this one scores 32 keys per warp in parallel, 18.3 → 5.7 ms/layer, exact fp32 math). Kept env-gated (SPARKINFER_PREFILL_ATTN_LANEPAR=0 restores the scalar kernels); no effect on the default MMA path.

Decode paths, CUDA graphs, and the Qwen3.6 model are untouched.

Proof of speedup

Tested on RTX 5090 (sm_120)

Decode tok/s (end-to-end, from bench/scripts/bench.sh — fill if this PR targets decode):

	decode tok/s
before (main)	295.54
after (this PR)	295.34

This PR does not target decode and does not modify the decode path. The row above is a
no-regression check at ctx=4096, not a claimed gain (-0.07% is run noise). The prefill
table below is the actual claim.

Prefill pp tok/s (Qwythos / Qwen3.5 — fill if this PR targets prefill; use --ctx 4096, 32768,
65536, or 131072 and copy the prefill pp line — report your best context):

	prefill pp tok/s
before prefill (main)	8419.18
after prefill (this PR)	14951.55

# qwen3_gguf_bench (the binary bench/scripts/bench.sh wraps), RTX 5090 sm_120,
# Qwythos-9B-Claude-Mythos-5-1M-Q4_K_M, default path, post-warmup, 3-rep median.
# before = clean clone of main @ 9fdf68a (includes #463 + #465), same box, same build flags.

### BEFORE (main @ 9fdf68a)
SWEEP_JSON {"4096":{"decode_tps":295.5379,"prefill_pp":8457.7366},
            "32768":{"decode_tps":294.1757,"prefill_pp":8419.1842}}

### AFTER (this PR, rebased on 9fdf68a)
SWEEP_JSON {"4096":{"decode_tps":295.3402,"prefill_pp":13987.3013},
            "32768":{"decode_tps":293.9429,"prefill_pp":14951.5538}}

ctx=4096   prefill pp   8457.74 -> 13987.30  (+65%;  llama.cpp same box: 11741.24)
ctx=32768  prefill pp   8419.18 -> 14951.55  (+78%;  llama.cpp same box: 10413.76)

Correctness. qwen3_gguf_prefill_check (batched prefill vs token-by-token fill, prefix 4096,
cont 16), same box, same build flags, both on top of the merged int8-MMA attention:
this PR top-1 13/16 · KL 0.0164 · seed 290 vs main @ 9fdf68a top-1 13/16 · KL 0.0152 ·
seed 290 — identical top-1 and seed, KL at the same level. (The fused dequant quantizes from
the exact fp32 dequant instead of the bf16-rounded scratch — pre-#465 it measured tighter than
the then-main: KL 0.0164 vs 0.0191.) ctest 7/7 pass.

skyrocket2026 · 2026-07-16T13:14:05Z

❌ sparkinfer auto-eval — `8e7e027`

metric	value
label	`eval:REJECT`
Qwen3.5 score	`eval-qwen35:REJECT` (fail)
Qwen3.6 score	`eval-qwen36:none` (pass)
Qwen3.5 scored decode (128 ctx)	? tok/s
Qwen3.5 correctness	top-1 0.0% · KL ?
Qwen3.6 vs same-box main	481.85 tok/s → +0.0% (+0.2)
Qwen3.6 scored decode (512 ctx · 512-context)	482.08 tok/s
Qwen3.6 correctness	top-1 93.0% · KL 0.0559
Qwen3.6 128-token no-regression gate	488.5 tok/s vs main 488.74 tok/s · pass
Qwen3.6 512-context no-regression gate	482.08 tok/s vs main 481.85 tok/s · pass
Qwen3.6 4k-context no-regression gate	462.55 tok/s vs main 462.59 tok/s · pass
Qwen3.6 16k-context no-regression gate	450.26 tok/s vs main 450.44 tok/s · pass
Qwen3.6 32k-context no-regression gate	425.39 tok/s vs main 425.37 tok/s · pass
Qwen3.5 optimize	`eval:REJECT` · ? tok/s · fail
Qwen3.6 optimize	`eval:none` · 482.08 tok/s · pass
Qwen3.6 optimize — Qwythos-9B (Q4_K_M) guard accuracy	top-1 94.0% · KL 0.0327 · pass
Qwen3.6 optimize — Qwen3.6-35B-A3B 128	488.5 tok/s · pass
Qwen3.6 optimize — Qwen3.6-35B-A3B 512	482.08 tok/s · pass
Qwen3.6 optimize — Qwen3.6-35B-A3B 4k	462.55 tok/s · pass
Qwen3.6 optimize — Qwen3.6-35B-A3B 16k	450.26 tok/s · pass
Qwen3.6 optimize — Qwen3.6-35B-A3B 32k	425.39 tok/s · pass

Rejected — primary (Qwythos-9B (Q4_K_M)) produced no verdict (infra error).

RTX 5090 (sm_120) · 128-token decode scored vs same-box main · built from source · correctness vs llama.cpp. Automated — not merged; merge manually after review.

James-CUDA · 2026-07-16T13:22:24Z

The eval:REJECT here is an infra failure, not an optimization/correctness failure — quoting the bot's own verdict line:

Rejected — primary (Qwythos-9B (Q4_K_M)) produced no verdict (infra error).

The primary Qwythos scoring run never executed (? tok/s, top-1 0.0% · KL ? = no data), so there is nothing measured to reject on. Every check that did run in the same eval passed:

Qwythos guard accuracy (same model, same build): top-1 94.0% · KL 0.0327 · pass — the very model the primary failed to score ran fine under the Qwen3.6 guard, which rules out a build or correctness problem with this PR.
All five Qwen3.6 no-regression gates pass (128/512/4k/16k/32k, all within noise of same-box main).
Built from source successfully; CI guard/gate/cap green.

For reference, measured on RTX 5090 (sm_120) against post-#463 main tooling, this PR's claim is prefill-only: Qwythos pp 2662.61 → 12900.33 tok/s @32k (numbers and qwen3_gguf_prefill_check top-1/KL in the PR body), decode untouched on both models.

Could the bot re-run the Qwythos primary eval? Happy to rebase/push again if that's what re-triggers it.

skyrocket2026 · 2026-07-16T14:46:32Z

❌ sparkinfer auto-eval — `e2f8dce`

metric	value
label	`eval:REJECT`
Qwen3.5 score	`eval-qwen35:XL` (fail)
Qwen3.6 score	`eval-qwen36:REJECT` (fail)
Qwen3.5 vs same-box main	7662.72 tok/s → +83.6% (+6409.4)
Qwen3.5 scored decode (4096 ctx · 4k-context)	286.84 tok/s
Qwen3.5 scored prefill (32768 ctx · 32k-context)	14072.17 pp tok/s · `eval-prefill:XL`
Qwen3.5 correctness	top-1 93.3% · KL 0.038
Qwen3.5 128-token no-regression gate	296.29 tok/s vs main 296.7 tok/s · pass
Qwen3.5 4k-context no-regression gate	286.84 tok/s vs main 286.72 tok/s · pass
Qwen3.5 32k-context no-regression gate	285.42 tok/s vs main 285.38 tok/s · pass
Qwen3.5 64k-context no-regression gate	285.5 tok/s vs main 285.44 tok/s · pass
Qwen3.5 4k prefill no-regression gate	12821.44 pp tok/s vs main 7792.88 pp tok/s · pass
Qwen3.5 32k prefill no-regression gate	14072.17 pp tok/s vs main 7662.72 pp tok/s · pass
Qwen3.5 64k prefill no-regression gate	14182.92 pp tok/s vs main 7825.4 pp tok/s · pass
Qwen3.5 128k prefill no-regression gate	0.0 pp tok/s · pass
Qwen3.6 vs same-box main	462.2 tok/s → +0.0% (+0.0)
Qwen3.6 scored decode (4096 ctx · 4k-context)	462.76 tok/s
Qwen3.6 correctness	top-1 87.8% · KL 0.0629
Qwen3.6 128-token no-regression gate	488.49 tok/s vs main 488.75 tok/s · pass
Qwen3.6 512-context no-regression gate	481.81 tok/s vs main 481.93 tok/s · pass
Qwen3.6 4k-context no-regression gate	462.76 tok/s vs main 462.2 tok/s · pass
Qwen3.6 16k-context no-regression gate	450.12 tok/s vs main 450.27 tok/s · pass
Qwen3.6 32k-context no-regression gate	425.15 tok/s vs main 425.36 tok/s · pass
Qwen3.5 optimize	`eval:XL` · 14072.17 tok/s · fail
Qwen3.5 optimize — Qwen3.6-35B-A3B guard accuracy	top-1 87.8% · KL 0.0629 · FAIL
Qwen3.5 optimize — Qwythos-9B (Q4_K_M) 128	296.29 tok/s · pass
Qwen3.5 optimize — Qwythos-9B (Q4_K_M) 4k	286.84 tok/s · pass
Qwen3.5 optimize — Qwythos-9B (Q4_K_M) 32k	285.42 tok/s · pass
Qwen3.5 optimize — Qwythos-9B (Q4_K_M) 64k	285.5 tok/s · pass
Qwen3.6 optimize	`eval:REJECT` · 462.76 tok/s · fail
Qwen3.6 optimize — Qwythos-9B (Q4_K_M) guard accuracy	top-1 93.3% · KL 0.038 · pass
Qwen3.6 optimize — Qwen3.6-35B-A3B 128	488.49 tok/s · pass
Qwen3.6 optimize — Qwen3.6-35B-A3B 512	481.81 tok/s · pass
Qwen3.6 optimize — Qwen3.6-35B-A3B 4k	462.76 tok/s · pass
Qwen3.6 optimize — Qwen3.6-35B-A3B 16k	450.12 tok/s · pass
Qwen3.6 optimize — Qwen3.6-35B-A3B 32k	425.15 tok/s · pass

Rejected — correctness gate: top1=0.877551 (need >= 0.9), kl=0.062888 (need <= 0.2).

RTX 5090 (sm_120) · 128/512/4k/16k/32k guarded · Qwen3.5 prefill at 4k/32k/64k · scored vs same-box main · built from source · correctness vs llama.cpp. Automated — not merged; merge manually after review.

James-CUDA · 2026-07-16T14:49:01Z

The reject is a near-threshold flake on the Qwen3.6 guard, not a regression from this diff — three observations:

This PR cannot change Qwen3.6 output. All three changes live behind the batched-prefill path, which early-returns for the MoE hybrid (qwen35_prefill.cpp: if (!s.gguf || !c.hybrid || !c.dense_ffn || n <= 0) return -1;). The new dequant kernel is only called from that path, and the conv/attention kernels are only launched by it. Qwen3.6 runs the untouched sequential + decode path — the eval's own table confirms it: Qwen3.6 decode is +0.0% vs same-box main at all five contexts (128/512/4k/16k/32k, all pass).
The previous eval of this same functional diff passed the same guard with margin: 8e7e027 measured Qwen3.6 correctness top-1 93.0% · KL 0.0559 · pass. Between 8e7e027 and e2f8dce the only delta is a rebase over perf(qwen35): int8 tensor-core prefill attention for Qwythos #465/governance: block jsdevninja and james3773 (missing denylist entries) #468, neither of which touches Qwen3.6 either. 93.0% → 87.8% across two runs of identical Qwen3.6 code is measurement noise around the 90% bar (KL passes both times: 0.0559 / 0.0629 vs ≤ 0.2).
The Qwythos side — the model this PR actually changes — passed its accuracy guard (top-1 93.3% · KL 0.038) and scored eval-prefill:XL at +83.6% (7662.72 → 14072.17 pp @32k) with every decode/prefill no-regression gate green.

Could the Qwen3.6 guard be re-run? Happy to push a rebase to re-trigger if that's the preferred mechanism.

…nv + fused Q4K/Q6K->int8 dequant Three batched-prefill bottlenecks on the Qwythos (Qwen3.5) path, found by profiling against llama.cpp (which led Qwythos prefill 3.7x at pp4096): 1. Prefill attention (prefill_attn_window.cu): the windowed/tiled kernels walked keys one per query-warp — a 5-shuffle reduction plus two dependent expf per key, latency-bound at 18.3 ms/layer. New lane-parallel kernel: one key per lane (zero shuffles to score 32 keys), tile-level online softmax, 4 queries per warp so each K/V smem read feeds 4 accumulators. 5.7 ms/layer (3.2x), same sink+window semantics, LANEPAR=0 restores. 2. GDN conv (batched_prefill.cu): pf_gdn_conv_kernel looped all N tokens sequentially with a __syncthreads per token on 64 blocks. The causal 4-tap conv over the raw in-buffer inputs is token-parallel; new kernel is one block per (token, head). 2.7 ms -> ~0.1 ms per layer. 3. Weight dequant (dequant_gguf.cu): every projection dequanted Q4_K/Q6_K to a bf16 scratch then re-read + row-quantized it to int8 for the int8 tensor-core GEMM. Fused deq_rows_i8 kernel decodes superblocks in registers and writes int8 + per-row scale directly (1B/value written, no bf16 bounce), and the N<=8192 int8 gate is lifted (own arena, falls back to bf16 GEMMs on alloc failure instead of the token loop). RTX 5090 (sm_120), Qwythos-9B Q4_K_M, default window path: prefill pp4096 6724 -> 12848 tok/s (+91%) prefill pp32768 2663 -> 12791 tok/s (+380%) decode unchanged (294.3 vs 294.6 tok/s, run noise) prefill_check 4096/16: top-1 13/16, KL 0.0164 (main kernels: 13/16, 0.0191) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

James-CUDA · 2026-07-16T14:52:37Z

To be precise about which gate blocked what, since the dual-model table is easy to misread:

The withheld score is eval-qwen35:XL (+83.6% scored prefill) — blocked by its cross-model guard: "Qwen3.5 optimize — Qwen3.6-35B-A3B guard accuracy · top-1 87.8% · FAIL".
Qwythos's own correctness passed in both directions: 93.3% · KL 0.038 as the primary, and the same values as the guard of the Qwen3.6 pass.

So the only failing measurement in the whole eval is taken on Qwen3.6-35B — the model this diff never executes on (batched prefill early-returns on !dense_ffn; Qwen3.6 decode measured +0.0% vs same-box main at all 5 contexts in this same run; the prior eval measured the same guard at 93.0% pass on a functionally identical build). Re-triggered at b699eb5 — if the guard lands back at its usual ~93%, the XL score should stand.

skyrocket2026 · 2026-07-16T15:53:25Z

❌ sparkinfer auto-eval — `b699eb5`

metric	value
label	`eval:REJECT`
Qwen3.5 score	`eval-qwen35:XL` (fail)
Qwen3.6 score	`eval-qwen36:REJECT` (fail)
Qwen3.5 vs same-box main	7662.72 tok/s → +83.7% (+6416.8)
Qwen3.5 scored decode (32768 ctx · 32k-context)	285.42 tok/s
Qwen3.5 scored prefill (32768 ctx · 32k-context)	14079.51 pp tok/s · `eval-prefill:XL`
Qwen3.5 correctness	top-1 93.8% · KL 0.0244
Qwen3.5 128-token no-regression gate	296.25 tok/s vs main 296.7 tok/s · pass
Qwen3.5 4k-context no-regression gate	286.52 tok/s vs main 286.72 tok/s · pass
Qwen3.5 32k-context no-regression gate	285.42 tok/s vs main 285.38 tok/s · pass
Qwen3.5 64k-context no-regression gate	285.49 tok/s vs main 285.44 tok/s · pass
Qwen3.5 4k prefill no-regression gate	12818.75 pp tok/s vs main 7792.88 pp tok/s · pass
Qwen3.5 32k prefill no-regression gate	14079.51 pp tok/s vs main 7662.72 pp tok/s · pass
Qwen3.5 64k prefill no-regression gate	14186.56 pp tok/s vs main 7825.4 pp tok/s · pass
Qwen3.5 128k prefill no-regression gate	0.0 pp tok/s · pass
Qwen3.6 vs same-box main	425.36 tok/s → -0.1% (-0.3)
Qwen3.6 scored decode (32768 ctx · 32k-context)	425.05 tok/s
Qwen3.6 correctness	top-1 0.0% · KL 99.0
Qwen3.6 128-token no-regression gate	488.31 tok/s vs main 488.75 tok/s · pass
Qwen3.6 512-context no-regression gate	455.95 tok/s vs main 481.93 tok/s · fail
Qwen3.6 4k-context no-regression gate	460.63 tok/s vs main 462.2 tok/s · pass
Qwen3.6 16k-context no-regression gate	449.68 tok/s vs main 450.27 tok/s · pass
Qwen3.6 32k-context no-regression gate	425.05 tok/s vs main 425.36 tok/s · pass
Qwen3.5 optimize	`eval:XL` · 14079.51 tok/s · fail
Qwen3.5 optimize — Qwen3.6-35B-A3B guard accuracy	top-1 0.0% · KL 99.0 · FAIL
Qwen3.5 optimize — Qwythos-9B (Q4_K_M) 128	296.25 tok/s · pass
Qwen3.5 optimize — Qwythos-9B (Q4_K_M) 4k	286.52 tok/s · pass
Qwen3.5 optimize — Qwythos-9B (Q4_K_M) 32k	285.42 tok/s · pass
Qwen3.5 optimize — Qwythos-9B (Q4_K_M) 64k	285.49 tok/s · pass
Qwen3.6 optimize	`eval:REJECT` · 425.05 tok/s · fail
Qwen3.6 optimize — Qwythos-9B (Q4_K_M) guard accuracy	top-1 93.8% · KL 0.0245 · pass
Qwen3.6 optimize — Qwen3.6-35B-A3B 128	488.31 tok/s · pass
Qwen3.6 optimize — Qwen3.6-35B-A3B 512	455.95 tok/s · fail
Qwen3.6 optimize — Qwen3.6-35B-A3B 4k	460.63 tok/s · pass
Qwen3.6 optimize — Qwen3.6-35B-A3B 16k	449.68 tok/s · pass
Qwen3.6 optimize — Qwen3.6-35B-A3B 32k	425.05 tok/s · pass
regressions	regression-qwen35-32k, regression-qwen35-32k

No context cleared the 2% significance gate while at least one context regressed. Auto-closing this PR.

RTX 5090 (sm_120) · 128/512/4k/16k/32k guarded · Qwen3.5 prefill at 4k/32k/64k · scored vs same-box main · built from source · correctness vs llama.cpp. Automated — not merged; merge manually after review.

James-CUDA · 2026-07-16T15:58:50Z

Ran the accuracy gate locally to characterize the failing Qwen3.6 guard — full seed matrix, both trees built clean from source on an RTX 5090 (main = 9fdf68a, PR = b699eb5), pinned GGUF (sha ac0e2c11… verified), bench/scripts/accuracy.sh unmodified:

`SPARKINFER_EVAL_SEED`	main top-1 / KL	this PR top-1 / KL
`fixed`	0.910000 / 0.0749	0.910000 / 0.0749
`12345`	0.887324 ❌ / 0.0431	0.887324 ❌ / 0.0431
`777`	0.925287 / 0.0327	0.925287 / 0.0327
`20260716`	0.901460 / 0.0809	0.901460 / 0.0809

Two facts fall out:

The PR is bit-identical to main on Qwen3.6 — all four seed pairs match to six decimal places (same tokens, same KL, same PPL), consistent with the code being unreachable for the MoE hybrid.
The guard's top-1 straddles the 0.90 bar on pristine main as a function of the prompt seed alone: range 0.887–0.925 across four seeds, with seed 12345 failing the bar on main itself (0.887 — right where this PR's eval landed, 0.877). KL passes everywhere with 3-6x margin.

So the ≥0.90 top-1 bar sits inside the prompt-sampling noise band of the Qwen3.6 UD-Q4_K_M weights themselves — any PR (including an empty diff) has roughly a coin-flip guard on an unlucky seed. Re-run requested at b699eb5; longer-term it may be worth pinning the guard prompt or averaging top-1 over 2-3 seeds so the gate measures the diff rather than the draw.

skyrocket2026 · 2026-07-16T16:04:24Z

⚠️ Infra invalidation — `b699eb5` eval (2026-07-16)

The posted eval:REJECT on the latest run is not a valid regression verdict. Remote log shows:

>> [guard36] model Qwen3.6-35B-A3B-UD-Q4_K_M.gguf ...
>> WARN: bench sweep failed (rc=1): [FAIL] load

After the Qwythos primary prefill sweep, the 35B guard pass failed to load (GPU memory). The harness continued and reported top1=0.0% · KL=99.0 — the default when accuracy.sh produces no METRIC line, not a real correctness failure.

Valid results from that run:

Qwythos prefill XL (+83.7% pp vs same-box main)
Qwythos correctness 93.8% top-1
All Qwythos decode + prefill no-regression gates pass

Harness fixes are landing (fail-closed on [FAIL] load / missing METRIC, longer GPU cooldown between bidir passes). Re-running eval after box cleanup.

skyrocket2026 · 2026-07-16T16:17:32Z

✅ sparkinfer auto-eval — `b699eb5`

metric	value
label	`eval:XL`
Qwen3.5 score	`eval-qwen35:XL` (pass)
Qwen3.6 score	`eval-qwen36:none` (pass)
Qwen3.5 vs same-box main	7662.72 tok/s → +83.5% (+6395.1)
Qwen3.5 scored decode (65536 ctx · 64k-context)	285.21 tok/s
Qwen3.5 scored prefill (32768 ctx · 32k-context)	14057.87 pp tok/s · `eval-prefill:XL`
Qwen3.5 correctness	top-1 93.4% · KL 0.0442
Qwen3.5 128-token no-regression gate	295.97 tok/s vs main 296.7 tok/s · pass
Qwen3.5 4k-context no-regression gate	286.22 tok/s vs main 286.72 tok/s · pass
Qwen3.5 32k-context no-regression gate	284.84 tok/s vs main 285.38 tok/s · pass
Qwen3.5 64k-context no-regression gate	285.21 tok/s vs main 285.44 tok/s · pass
Qwen3.5 4k prefill no-regression gate	12808.24 pp tok/s vs main 7792.88 pp tok/s · pass
Qwen3.5 32k prefill no-regression gate	14057.87 pp tok/s vs main 7662.72 pp tok/s · pass
Qwen3.5 64k prefill no-regression gate	14149.94 pp tok/s vs main 7825.4 pp tok/s · pass
Qwen3.5 128k prefill no-regression gate	0.0 pp tok/s · pass
Qwen3.6 vs same-box main	462.2 tok/s → +0.2% (+0.7)
Qwen3.6 scored decode (4096 ctx · 4k-context)	462.9 tok/s
Qwen3.6 correctness	top-1 92.4% · KL 0.0597
Qwen3.6 128-token no-regression gate	488.89 tok/s vs main 488.75 tok/s · pass
Qwen3.6 512-context no-regression gate	482.16 tok/s vs main 481.93 tok/s · pass
Qwen3.6 4k-context no-regression gate	462.9 tok/s vs main 462.2 tok/s · pass
Qwen3.6 16k-context no-regression gate	449.92 tok/s vs main 450.27 tok/s · pass
Qwen3.6 32k-context no-regression gate	425.5 tok/s vs main 425.36 tok/s · pass
Qwen3.5 optimize	`eval:XL` · 14057.87 tok/s · pass
Qwen3.5 optimize — Qwen3.6-35B-A3B guard accuracy	top-1 92.4% · KL 0.0597 · pass
Qwen3.5 optimize — Qwythos-9B (Q4_K_M) 128	295.97 tok/s · pass
Qwen3.5 optimize — Qwythos-9B (Q4_K_M) 4k	286.22 tok/s · pass
Qwen3.5 optimize — Qwythos-9B (Q4_K_M) 32k	284.84 tok/s · pass
Qwen3.5 optimize — Qwythos-9B (Q4_K_M) 64k	285.21 tok/s · pass
Qwen3.6 optimize	`eval:none` · 462.9 tok/s · pass
Qwen3.6 optimize — Qwythos-9B (Q4_K_M) guard accuracy	top-1 93.4% · KL 0.0442 · pass
Qwen3.6 optimize — Qwen3.6-35B-A3B 128	488.89 tok/s · pass
Qwen3.6 optimize — Qwen3.6-35B-A3B 512	482.16 tok/s · pass
Qwen3.6 optimize — Qwen3.6-35B-A3B 4k	462.9 tok/s · pass
Qwen3.6 optimize — Qwen3.6-35B-A3B 16k	449.92 tok/s · pass
Qwen3.6 optimize — Qwen3.6-35B-A3B 32k	425.5 tok/s · pass

Verified speedup over same-box origin/main — 14057.87 tok/s (main was 7662.72 tok/s).

RTX 5090 (sm_120) · 128/512/4k/16k/32k guarded · Qwen3.5 prefill at 4k/32k/64k · scored vs same-box main · built from source · correctness vs llama.cpp. Automated — not merged; merge manually after review.

skyrocket2026 · 2026-07-16T16:17:50Z

✅ Auto-merged as the round's merge-first winner — verified same-box speedup over main, all checks green. Thanks for the contribution!

dashboard: backfill PRs #455 #463 #464 #465 from latest evals

@64k

…tensor-ai-lab#464 gittensor-ai-lab#465 from latest evals Record merged Qwen3.5 prefill wins (gittensor-ai-lab#455 XL @64k, gittensor-ai-lab#465 XL, gittensor-ai-lab#463 M, gittensor-ai-lab#464 XL @32k 14057.87 pp tok/s) and repair optimization journey.

James-CUDA marked this pull request as draft July 16, 2026 06:03

James-CUDA marked this pull request as ready for review July 16, 2026 06:24

James-CUDA force-pushed the perf/prefill-attn-lane-par branch from 706b1fc to 8e7e027 Compare July 16, 2026 12:34

skyrocket2026 added area:runtime subsystem (emission weight 0.26) area:kernels subsystem (emission weight 0.42) test-on-5090 Maintainer-approved to evaluate on RTX 5090 (greenlight) eval:REJECT sparkinfer auto-eval verdict: REJECT eval-qwen35:REJECT eval-qwen36:none labels Jul 16, 2026

James-CUDA force-pushed the perf/prefill-attn-lane-par branch from 8e7e027 to e2f8dce Compare July 16, 2026 14:07

James-CUDA changed the title ~~perf(qwen35): lane-parallel prefill attention + token-parallel GDN conv + fused Q4K/Q6K->int8 dequant (4.8x pp @32k)~~ perf(qwen35): fused Q4K/Q6K->int8 dequant + token-parallel GDN conv + lane-parallel fallback attention (+78% pp @32k on top of #465) Jul 16, 2026

skyrocket2026 added eval:REJECT sparkinfer auto-eval verdict: REJECT eval-qwen35:XL eval-qwen36:REJECT 4k-context UI-only: strongest measured context in sparkinfer eval and removed eval:REJECT sparkinfer auto-eval verdict: REJECT eval-qwen35:REJECT eval-qwen36:none labels Jul 16, 2026

James-CUDA force-pushed the perf/prefill-attn-lane-par branch from e2f8dce to b699eb5 Compare July 16, 2026 14:50

skyrocket2026 added eval:REJECT sparkinfer auto-eval verdict: REJECT eval-qwen35:XL and removed eval:REJECT sparkinfer auto-eval verdict: REJECT eval-qwen35:XL eval-qwen36:REJECT labels Jul 16, 2026

skyrocket2026 reopened this Jul 16, 2026

skyrocket2026 added eval:XL sparkinfer auto-eval verdict: XL eval-qwen35:XL eval-qwen36:none and removed eval-qwen35:XL eval:REJECT sparkinfer auto-eval verdict: REJECT eval-qwen36:REJECT labels Jul 16, 2026

skyrocket2026 added the merge-first Round's biggest verified speedup — merge this first label Jul 16, 2026

skyrocket2026 merged commit cb34dc1 into gittensor-ai-lab:main Jul 16, 2026
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This was referenced Jul 16, 2026

fix(eval): surface infra failures instead of false PR rejects #470

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dashboard: backfill PRs #455 #463 #464 #465 from latest evals #471

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skyrocket2026 added a commit that referenced this pull request Jul 16, 2026

Merge pull request #471 from gittensor-ai-lab/dashboard/backfill-455-465

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dashboard: backfill PRs #455 #463 #464 #465 from latest evals

blinkeye-lcm mentioned this pull request Jul 16, 2026

perf(qwen35): int8-native prefill GEMM (mma.sync m16n8k32) for Qwythos #474

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skyrocket2026 mentioned this pull request Jul 16, 2026

dashboard: Qwen3.5 prefill optimization journey #478

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skyrocket2026 removed the merge-first Round's biggest verified speedup — merge this first label Jul 16, 2026

fansilas mentioned this pull request Jul 17, 2026

perf(qwen35): chunk-parallel GDN scan + vector-store int8 dequant + single-pass row quantize + skinny GEMM (+23% prefill pp) #511

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perf(qwen35): fused Q4K/Q6K->int8 dequant + token-parallel GDN conv + lane-parallel fallback attention (+78% pp @32k on top of #465)#464

perf(qwen35): fused Q4K/Q6K->int8 dequant + token-parallel GDN conv + lane-parallel fallback attention (+78% pp @32k on top of #465)#464
skyrocket2026 merged 1 commit into
gittensor-ai-lab:mainfrom
James-CUDA:perf/prefill-attn-lane-par

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Summary

Proof of speedup

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skyrocket2026 commented Jul 16, 2026

❌ sparkinfer auto-eval — 8e7e027

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❌ sparkinfer auto-eval — e2f8dce

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skyrocket2026 commented Jul 16, 2026

❌ sparkinfer auto-eval — b699eb5

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⚠️ Infra invalidation — b699eb5 eval (2026-07-16)

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✅ sparkinfer auto-eval — b699eb5

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James-CUDA commented Jul 16, 2026 •

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❌ sparkinfer auto-eval — `8e7e027`

❌ sparkinfer auto-eval — `e2f8dce`

❌ sparkinfer auto-eval — `b699eb5`

⚠️ Infra invalidation — `b699eb5` eval (2026-07-16)

✅ sparkinfer auto-eval — `b699eb5`