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PARITY_HANDOFF: how to pick up the GB10 vLLM-parity work

2026-06-30 update: this handoff is now historical procedure, not the active verdict. The GB10 investigation was reopened in GB10_PARITY_REOPEN_SPEC.md and GB10_PARITY_PHASE0_RESULTS.md, with Phase 6 serving-nsys evidence and the active follow-up plans under docs/superpowers/plans/. Use those files for the current state before relying on the older "closed" conclusion below.

Audience: an agent with zero prior context who has been told to "continue the GB10 vLLM-parity investigation" on the llama-cpp-localai-paged backend.

This file is the operational how-to. It is the companion to VLLM_PARITY_FINAL.md, which is the why / authoritative record ("never re-litigate"). If the two ever disagree on a fact, VLLM_PARITY_FINAL.md and the bench artifacts it cites win; this file wins on procedure (how to ssh, lock, build, bench, profile).

Read order for a cold start:

  1. This file (TL;DR + hard gates + quickstart).
  2. VLLM_PARITY_FINAL.md (the closed record, every number cites its artifact).
  3. .agents/vllm-parity-methodology.md (the methodology: bit-exact gating, profile-don't-assume, both-engine ground truth).
  4. The patch-series README.md (~44 KB, canonical backend doc) and PAGED_BITEXACT_NOTE.md.

1. TL;DR STATE

2026-07-01 active update: Phase50-59 reopened the dense and MoE serving scheduler question. True dense decode is much closer to vLLM (383.66 vs 435.00 t/s, 88.2%) than the Phase47 h2h aggregate suggested, while traced serving still shows no pure decode-only steps and high TTFT. Phase53 rejected static lower admission budgets; Phase54 histograms show prompt admission concentrated in a few large chunks (prompt_hist=513+:12) with mostly full-width decode (decode_hist=128-255:53). Phase55 implemented that targeted first-token A/B as LLAMA_TTFT_PREFILL_FIRST=1: on dense n=128 it improved aggregate throughput 138.2 -> 142.9, mean TTFT 23231.9 -> 21520.8 ms, and wall 59.272 -> 57.323 s, with md5/op gates green. Phase56 then showed the policy helps dense n=32 but regresses MoE n=128 mean TTFT 7168.1 -> 7615.3 ms and aggregate 341.1 -> 339.9; keep it opt-in only and do not default it broadly. Phase57 tried a per-step defer cap; cap32 improved MoE mean TTFT in one same-window sweep but still lost aggregate and wall, and dense caps lost aggregate. Phase58 added a prompt-backlog threshold; min32 improved MoE n=128 aggregate 339.0 -> 341.9, mean TTFT 7743.1 -> 7420.1 ms, and wall 24.167 -> 23.950 s in the same window, while dense n=128 was mixed. Phase59 repeated MoE min32: aggregate stayed flat (336.6 -> 336.9), mean TTFT improved (7798.5 -> 7167.8 ms), and wall stayed flat (24.334 -> 24.316 s) with md5/op gates green. Matching vLLM was still far ahead (601.3 aggregate, 2968.1 ms mean TTFT), so min32 is an opt-in llama.cpp QoS knob, not a parity-closing lever. The trace and scheduler commits are local and DGX-gated but not pushed, so the LocalAI patch series has not been regenerated.

  • Historical verdict: the older investigation marked GB10 parity CLOSED and unreachable. Treat that as superseded where Phase50-54 provide newer dense serving evidence.
  • Prefill is a genuine floor at ~36% (MoE) / ~43% (dense) of vLLM. Prefill is not CUDA-graph-replayed, so these numbers are real, not measurement artifacts.
  • Decode is near-parity: ~86% of vLLM's TRUE GPU-steady decode (924 vs 1078 t/s). The long-standing ~56% headline was a CUDA-graph measurement artifact (nsys without --cuda-graph-trace=node collapses each graph replay into one opaque launch). Decode is also ahead of vLLM at low concurrency (dense 116.7% at N=8) and uses 1.5-3x less memory, bit-exact per-path.
  • The lever search was exhaustive: every attempt (prefill GEMM, GDN chunked scan, decode fusions, serving/scheduler) is recorded with its verdict and number so it is not re-run.
  • The path to parity is different hardware: datacenter Blackwell (B200, HBM, native tcgen05 / CUTLASS FP4). Do NOT reopen GB10 kernels. Re-run the methodology on the new silicon, where vLLM's GB10-losing FLA/Marlin kernels invert.

2. THE HARD GATES YOU MUST NOT VIOLATE

These are non-negotiable. Violating any of them invalidates the result or the contribution.

2.1 The per-path greedy-md5 bit-exact gate (sacred)

The gate is per-path: paged vs non-paged attention legitimately produce different (equivalent) FP-reduction orders. Each path is gated against its own reference, validated benign by KL-divergence to the f16 reference. Canonical greedy md5s:

Path Model Canonical md5
non-paged MoE q36-35b-a3b-nvfp4 07db32c2bcb78d17a43ed18bc22705cd
paged MoE q36-35b-a3b-nvfp4 8cb0ce23777bf55f92f63d0292c756b0
non-paged dense q36-27b-nvfp4 5951a5b4d624ce891e22ab5fca9bc439
paged dense q36-27b-nvfp4 5951a5b4d624ce891e22ab5fca9bc439 (bit-exact to non-paged)
  • Compare paged-to-paged only. Future paged-MoE regressions compare to 8cb0ce23, NOT 07db32c2.
  • Why paged-MoE differs (benign, KL-validated): llama-perplexity --kl-divergence on the MoE GGUF (16 chunks, f16 base PPL 7.3734) shows non-paged-vs-f16 KLD 0.136597 and paged-vs-f16 KLD 0.136000, i.e. paged does NOT diverge from f16 ground truth more than non-paged does. Paged and non-paged are two equivalent FP-reorderings of the same 4-bit model. This holds on the 0028 baseline and with LLAMA_MOE_FORCE_GRAPHS/0029 on or off, so it is a property of the paged path, not any one lever.
  • Every bit-exact patch is gated two ways: greedy md5 (per path) AND test-backend-ops vs the CPU oracle for every touched op.

2.2 The KL-gate for opt-in lossy paths

Any path that is NOT byte-identical (e.g. 0033 dequant-bf16, the 0034/0035 large-M FP paths, FP8-KV) ships default-off and is gated by a KL-divergence band: it requires KLD(new||f16) <= KLD(FP4-MMQ||f16) and PPL within the established band. Lossy levers never ship default-on.

2.3 In-backend A/B is the only proof (hard methodology rule)

A lever compiled into the binary is NOT isolated by a runtime flag alone. It needs a separately-built in-backend A/B. Precedents that burned this in: 0031 chunking math was correct yet -22% in-backend; 0034 had a standalone PoC win that did not hold in-backend.

2.4 Contribution / commit gates (LocalAI policy)

  • DCO sign-off is human-only: do not add an AI Signed-off-by trailer.
  • AI attribution via Assisted-by: trailer: Assisted-by: Codex:gpt-5.
  • NEVER add Co-Authored-By: (AI) trailers and never add an AI Signed-off-by.
  • No em-dashes anywhere in output (use -, :, parentheses, or rephrase).
  • Ask before every git push. Prior approval does not carry over.

2.5 Fork-first is MANDATORY (the fork is canonical)

  • The canonical source of truth is the fork branch mudler/llama.cpp:localai-paged (= pin commit + paged patch commits in order). It is canonical for ALL paged-backend kernel/patch work. The shipped patches/paged/*.patch series is a derivative: the fork is the source.
  • Always update the fork FIRST, in this exact order: (1) commit the change on the localai-paged branch and push it, then (2) regenerate the LocalAI series (backend/cpp/llama-cpp-localai-paged/patches/paged/) from the fork via git format-patch (one patch per fork commit, source-only, never touching a *.md/dev-doc), so the series stays a 1:1, drift-free mirror of the branch. No hand-export.
  • NEVER edit the LocalAI patches/paged/*.patch files directly, and NEVER add a patch to the series with no corresponding fork-branch commit. They are generated output, not source.
  • The fork branch is also where the build and the per-path bit-exact md5 gate actually run, so it is the only place a change is truly validated. A patch that lives only in the LocalAI series has never been built or gated.
  • Mirror invariant (verify by tree hash): applying the full on-disk series on the pin must reproduce the fork branch tree byte-for-byte. The series has intentional gaps (missing 0005, 0026, 0027, 0032, 0036-0039, 0045), so the patch count is not the max number; what must hold is the tree-hash equality, not the count. Current verified state: fork HEAD 2d590d770 is mirrored by worktree patch 0063-feat-cuda-trace-cublas-tensor-names.patch; applying all 54 patch files on 0ed235ea2c17a19fc8238668653946721ed136fd produces tree dedb1182910eafe9f6875588dc8285bfb544cce5, exactly matching the fork.

2.6 Bench hygiene gates

  • NEVER set LLAMA_MAX_BATCH_TOKENS in benches (the harness explicitly logs "NO LLAMA_MAX_BATCH_TOKENS").
  • Do not set GDN_TC, GDN_CHUNK_MIN, or LLAMA_PAGED_DECODE_STABLE in parity benches. Production defaults are compiled in: GDN M5 on (GDN_TC=5, GDN_CHUNK_MIN=64), S1 decode-graph on, S3 off.
  • Decode profiling MUST use nsys --cuda-graph-trace=node (see section 3.4). This is a gate, not a suggestion.

3. OPERATIONAL QUICKSTART (copy-pasteable)

3.0 Host

ssh dgx.casa        # resolves to hostname promaxgb10-4ad8; GPU = NVIDIA GB10 (unified LPDDR5x, ~273 GB/s, the bandwidth floor)

nvidia-smi reports memory as [N/A] (unified memory). CUDA 13 / sm_121.

3.1 GPU lock protocol (~/gpu_bench_lock) - TWO conventions, reconcile carefully

There are two conventions in flight:

  • Old harnesses (combined_definitive.sh, fuse_validate.sh, fuse_profile.sh) treat it as an empty mutex dir: mkdir ~/gpu_bench_lock to acquire, rmdir to release.
  • Newer harnesses (fp4norm_profile.sh) use an owner-file convention: mkdir -p ~/gpu_bench_lock then echo "$ME $(date +%s)" > ~/gpu_bench_lock/owner. They poll until nvidia-smi --query-compute-apps=pid count is 0 AND owner is FREE*/absent for 2 consecutive checks, and clear a stale ~/gpu_bench_lock/release file. Release writes FREE released-by-... $(date +%s) to owner (it does NOT remove the dir).

Because the dir now permanently contains an owner file, release with rm -rf ~/gpu_bench_lock, NOT rmdir (rmdir fails on the non-empty dir). Recommended procedure for a future agent:

  1. Read ~/gpu_bench_lock/owner. FREE*/absent + 0 compute-apps means free.
  2. Acquire via mkdir -p ~/gpu_bench_lock + write owner.
  3. Release by writing FREE ... to owner (or rm -rf ~/gpu_bench_lock).

A separate 0-byte ~/bench/gpu.lock is legacy/unrelated - ignore.

Always gate on ALL THREE before benching or building on DGX: nvidia-smi --query-compute-apps=pid count == 0, owner FREE, and docker ps shows no running containers. In particular, do not start work while a local-ai-worker container is running. Concurrent jobs share this GPU: an offline-repack Marlin workflow, an ~/.cache/autoresearch-quant/ quant pipeline (this is the llama-imatrix class of job), finetune trees, and LocalAI worker containers. The canonical harnesses poll for GPU-idle up to 2h.

3.2 Build (long; run detached + poll)

  • Mainline / canonical grpc-server + binaries: CUDA arch 121 (-DCMAKE_CUDA_ARCHITECTURES=121). Runtime banner shows ARCHS = 1210 | BLACKWELL_NATIVE_FP4 = 1.
  • FP4-MMA / tensor-core experimental kernels: the accelerated 121a gencode (arch=compute_121a,code=[compute_121a,sm_121a]). The a suffix unlocks tcgen05 / native FP4-MMA intrinsics. 121a lives ONLY in the DGX experimental build scripts (~/gdn_cc.sh standalone nvcc, ~/gdn_bv_build.sh -DCMAKE_CUDA_ARCHITECTURES=121a, ~/paged-build.sh --build-arg CUDA_DOCKER_ARCH=121a), not in the worktree build files. Supply it at build time via CMAKE_CUDA_ARCHITECTURES / CUDA_DOCKER_ARCH.
  • Long builds: run detached and poll for a marker. Pattern: nohup ... > build.log 2>&1 & then poll for a .DONE/.done file. Do NOT block a foreground shell.

Built binaries live at dgx:~/llama-paged-dev/build-cuda/bin/ (llama-server, llama-batched-bench, llama-completion; thin ~70 KB dynamic wrappers).

3.3 The standard bench env + commands

cd /home/mudler/llama-paged-dev/build-cuda/bin
L="LLAMA_KV_PAGED=1 LLAMA_MOE_FORCE_GRAPHS=1 GGML_NO_BACKTRACE=1"   # GGML_NO_BACKTRACE is log-hygiene, not a lever
MOE=/home/mudler/bench/q36-35b-a3b-nvfp4.gguf       # arch qwen35moe, ~22.2 GiB
DENSE=/home/mudler/bench/q36-27b-nvfp4.gguf         # arch qwen35,    ~17.5 GiB

# (1) Bit-exact / coherence gate. stdin MUST be /dev/null or it hangs in conv mode.
env $L ./llama-completion -m "$MOE" -ngl 99 -fa on -c 4096 --temp 0 --seed 1 -n 48 -no-cnv \
    -p "The capital of France is" </dev/null | md5sum
# The PAGED_BITEXACT_NOTE gate command uses the chat-template path (NO -no-cnv):
#   ./llama-completion -m MODEL -ngl 99 -fa on -p "The capital of France is" -n 48 --temp 0 --seed 1
# (compare to the canonical md5 for that model+path; paged-to-paged only)

# (2) PREFILL bench (S_PP from llama-batched-bench)
env $L ./llama-batched-bench -m "$MOE" -c 131072 -b 2048 -ub 512 -ngl 99 -fa on \
    -npp 512,2048 -ntg 4 -npl 32

# (3) SERVING bench: one --parallel 256 server, then drive with h2h_cli3.py
env $L nohup ./llama-server -m "$MOE" -c 262144 --parallel 256 -b 2048 -ub 512 \
    -ngl 99 -fa on --host 127.0.0.1 --port 8090 --no-webui >/home/mudler/bench/paged_server.log 2>&1 &
# poll http://127.0.0.1:8090/health for '"ok"', then:
python3 /home/mudler/bench/h2h_cli3.py   # OpenAI /v1/completions, ignore_eos, fresh-nonce, ptok128 gen128, NPL sweep 8/32/128/256

vLLM side (for both-engine parity): ~/vllm-bench/bin/vllm (version 0.23.0), served gpu-util 0.85 max-model-len 4096 max-num-seqs 256 tp1, models ~/bench/q36-35b-a3b-nvfp4-vllm/ and ~/bench/q36-27b-nvfp4-vllm/.

Current-stack serving snapshots use backend/cpp/llama-cpp-localai-paged/paged-current-serving-snapshot.sh. It targets the clean ~/llama-phase6-source mirror, checks docker/local-ai-worker/GPU-idle state, uses the owner-file lock, runs pre/post inference gates, then compares paged and vLLM with the same h2h client. The older dgx:~/bench/combined_definitive.sh is historical: do not reuse it without first porting away from stale ~/llama-paged-dev paths and old lock assumptions. The harness also writes hardware.txt before any server starts, including DRY_RUN=1, so every new snapshot records the GPU model, driver, compute capability when exposed by nvidia-smi, and a conservative hardware class. Full runs also write gate_summary.tsv after the post gate, summarizing pre/post MoE md5, dense md5, and backend-op checks; use paged-current-serving-snapshot.sh --summarize-gates ART to backfill or audit an existing snapshot without starting servers.

3.4 THE DECODE-PROFILING RULE (this trap caused 4 wrong analyses)

Decode runs as a replayed CUDA graph. nsys without --cuda-graph-trace=node collapses each graph replay into ONE opaque launch, so every per-kernel attribution becomes an artifact. This is exactly what made the old "paged 159 us/tok, GPU ~16% busy, host-bound, 5.4x more GPU-efficient" story wrong, and produced the wrong ~56% headline.

Mandatory method for any decode profile:

  • Use nsys --cuda-graph-trace=node.
  • Decompose with the difference method: per-token cost = (ntg=64 profile) - (ntg=16 profile).

Under the correct method, paged decode at npl=256 is 99% GPU-busy (1.4% idle), NOT host-bound - the opposite of the collapsed-graph reading. The clean graph-node-traced profiles are at ~/highN_prof2/*.nsys-rep (paged, npl=256) and ~/highN_vllm/*.nsys-rep (vLLM), captured 2026-06-30. They supersede every earlier decode decomposition.

3.5 Models + artifacts (all on DGX)

GGUF (paged): ~/bench/q36-35b-a3b-nvfp4.gguf (MoE, qwen35moe), ~/bench/q36-27b-nvfp4.gguf (dense, qwen35). vLLM safetensors: ~/bench/q36-35b-a3b-nvfp4-vllm/ (has hf_quant_config.json confirming MIXED_PRECISION / FP8-proj), ~/bench/q36-27b-nvfp4-vllm/. Authoritative run: ~/bench/COMBINED_DEFINITIVE.txt (+ .log, .done, combined_definitive.sh, per-engine COMBINED_*_server.log). A/B dirs: ~/bench/marlin_gate/, ~/bench/gdn_p1_ab/. NOTE: the *_RESULTS*/*_MAP* docs live only in the worktree docs/, not on the DGX.

4. THE COMPLETE LEVER MAP (do NOT re-run the rejected ones)

Verdicts and numbers are from VLLM_PARITY_FINAL.md + the cited artifacts. "BE" = greedy-md5 bit-exact; "KL-benign" = lossy path inside the KL band.

4.1 Prefill weight-GEMM track - WHOLE TRACK REJECTED (FP4-MMQ is optimal on GB10)

Decisive surprise: on sm_121 vLLM itself does NOT run native FP4 - it runs Marlin W4A16 (FP4 dequant->bf16 in-register + bf16 GEMM) for experts and FP8 projections, capped at ~half FP4 peak, because native CUTLASS NVFP4 grouped-GEMM is broken on consumer Blackwell (TMA-WS init failure, CUTLASS #3096; no tcgen05/TMEM). So MMQ's native FP4 is already structurally competitive here.

Lever What Verdict Key number
0033 dequant->bf16 cuBLAS route large-M NVFP4 dense GEMM to dequant->bf16 cuBLAS REJECTED, ships default-off dense S_PP -49%/-42%/-29% at M=512/1024/2048; BE + KL-better
dense-cuBLAS reroute (full) same across dense+MoE prefill REJECTED -31% to -62% band
0034 native FP4-MMA W4A4 Blackwell mxf4nvf4 OMMA large-M REJECTED in-backend PoC 103 TFLOP/s (57.7% FP4 peak, NMSE=0) but win did not hold in-backend
0035 W4A16-Marlin grouped MoE FP4->bf16 in-register + bf16 mma, zero act-quant tax REJECTED (perf) correct + KL-benign-and-better but -39% S_PP vs MMQ
0045/0046 offline-repack / vLLM-verbatim Marlin repack to Marlin layout; port vLLM kernel verbatim REJECTED verbatim correct but -39%; offline-repack same bf16-peak ceiling, no win

Why it loses: bf16 TC peak on GB10 is ~half FP4 peak, so any dequant->bf16 kernel caps at ~half FP4-MMQ; the dequant write is an un-amortized weight-sized memory pass (~8x the FP4-read traffic). The GEMM bucket is not winnable on GB10 with available kernels.

4.2 Prefill GDN chunked-scan track - M5 tf32 C=16 is the SHIPPED winner

GDN is the #1 prefill-gap contributor (+59.2 us/tok, ~30%). vLLM's FLA chunk_gated_delta_rule runs the same math at 36.5 vs paged 95.7 us/tok = 2.62x via tensor-core intra-chunk Gram products.

Lever What Verdict Key number
0031 scalar-serial chunked scan FLA-style scalar/serial (GDN_TC=0) superseded correct but ~22% slower at forced C=16
0047 / M5 tf32 tensor-core scan tf32 m16n8k8 mma form-T solve, f32-only SHIPPED default-on under paged MoE prefill +3.5% @npp512, +17.7% @npp2048; decode unchanged; BE-benign
bf16 CONFIG-C (M8) bf16 Kc/Qc + 2 C*C scratch, C->64 REJECTED (not in f32 series) confirmed geometry then dropped
bf16-C16 bf16 Gram at C=16 REJECTED no win; bf16 mantissa unsafe on state-coupled products
BV block-occupancy A/B (tf32) raise blocks/SM REJECTED (occupancy NOT the bound) 1844 vs 1814 S_PP (-1.04%, within noise)
bf16-C64 bf16 Gram at C=64 REJECTED -18.75%; O(C^2) intra-chunk + serial recurrence dominates
Phase 10 C32 slab M5 C=32, two dv_tile=64 slabs, default-off GDN_C32_SLAB=1 REJECTED md5-clean after tail-row zeroing, but slower: MoE 2048 2430.32 -> 2054.86; dense 2048 1019.25 -> 903.73
Phase 11 QS-early M5 move QS = Qc * S0 earlier, default-off GDN_M5_QS_EARLY=1 REJECTED md5-clean, but slightly slower: MoE 2048 2441.54 -> 2420.26; dense 2048 1021.06 -> 1015.77
Phase 12 shared-A/Ai cost model f32 Ai scratch shared across two C32 value slabs GO to one default-off prototype BT32 f32 scratch at npp2048,npl32: MoE 256 MiB / 768 MiB Ai traffic; dense 384 MiB / 1152 MiB Ai traffic
Phase 13 Global-Ai32 precompute f32 Ai once, consume from two C32 dv_tile=64 slabs REJECTED md5-clean, but slower: MoE 2048 2425.10 -> 2097.76; dense 2048 1016.14 -> 918.19

Why not occupancy/dtype: the cost is the O(C^2) intra-chunk triangular A-inverse solve + the strictly-serial inter-chunk recurrence, with C forced to 16 by GB10's 99 KB dynamic-smem cap (the 128x128 f32 state alone is 64 KB). M5 captures the tractable TC part; it does not fully close 2.62x because vLLM's FLA blocked-solve is a more complete TC implementation.

Phase 13 closes the caveat: the default-off GDN_GLOBAL_AI32=1 prototype was correctness-clean but slower. Stop GDN kernel work on GB10 instead of iterating into f16 Ai or more local reorders.

4.3 Decode / fusion levers - all REJECTED (near-parity already at ~86% true GPU-steady)

Lever What Verdict Key number
act-quant folded into ggml MMQ inline y-quant in MoE expert MMQ REJECTED -79.4%; ggml MMQ re-quantizes y per weight-row-tile x stream-k split, no TC for inline quant
norm+quant+silu fusion one launch (vLLM Triton kernel) REJECTED (infeasible) ggml_cuda_can_fuse cannot express it: FP4 quant is a mul_mat-internal prologue, silu separated from norm by 2 GEMMs + router
Q8_0 / FP8 projection quantize bf16 GDN/attn projections REJECTED (regime error) vLLM DOES use FP8 proj, but at N>=128 proj is only ~12% of stream, closes <=6%
NVFP4 the projections drop proj to NVFP4 REJECTED KL-fail, ~+6% PPL; vLLM keeps SAME bf16/FP8 proj, never NVFP4
W4A16-Marlin MoE decode Marlin grouped expert GEMM at decode REJECTED BW-floored wash, ~5% slower
bf16-tau per-head SSM (0026) per-head bf16 tau on SSM decode DROPPED flat 780.6 vs 780.0 t/s; earlier "+12%" subsumed by 0028/0029
D3 FA-split / D4 GDN-width-adaptive older off-critical-path levers SUPERSEDED reasoning were rejected via the debunked "5.4x/host-bound" reading; under HNP the GDN scan IS critical path (51%) but is the shared BW floor where paged leads (83% vs 79%), so still not a win

Dense decode is AHEAD at low N (116.7% @ N=8) - the one operating point where paged is unambiguously faster.

4.4 Serving / engine levers - host loop and scheduler CLOSED

Lever What Verdict Key number
0040 / S1 paged decode-graph reuse can_reuse keyed on bucketed block-table dims SHIPPED default-on serving reuse 0% -> 72.2% (with S3); static 0% -> 95.5%
0041 / S3 decode-shape-stable scheduling (LLAMA_PAGED_DECODE_STABLE) keep prefill out of decode steps SHIPPED default-OFF (opt-in) recovers the ~17 pt graph-reuse overhead at a TTFT cost; default-on regressed real serving (2.5x worse TTFT, 20-29% lower e2e throughput)
0043 / D1 full-step MoE decode CUDA graph graph whole decode step incl. grouped-MMQ MoE dispatch SHIPPED default-on +2.6% (npl128) to +5-13% (npl32); D1 premise "host-sync on MoE readback" REFUTED (sync count identical 1457 on/off)
S2 double-buffer set_inputs overlap host input build with GPU DROPPED set_inputs ~0.05 ms/step, nothing to recover
whole-step graph / host loop host loop as serving residual CLOSED (~0-1%) reuse 0% (757.6) == S1+S3 72% (763.3); hostproc only ~4-8% of step wall
padded / fixed-slot decode pad decode width to --parallel for ~100% reuse REJECTED (built, GPU-tested, commit b028c81e) inert (BE) but regresses everywhere; N=8 burst 28.16->6.05 tok/s/seq; serving decode is GPU-compute-bound, dummy-row compute > reuse recovered
speculative decode (MTP) draft + verify REJECTED for current GB10 serving Phase 14 safety passed, but Phase 15 serving A/B regressed hard: n128 decode agg 662.4 -> 138.5 tok/s; likely graph/batch-shape disruption (graphs reused 361 -> 1)

4.5 SHIPPED WINS (all BE / KL-benign) - keep these, do not regress

  • FP4-MMQ MoE/dense GEMM (native Blackwell FP4-MMA at the FP4 weight-BW floor; reason 4.1 stays default-off).
  • M5 tf32 tensor-core chunked GDN prefill (patch 0047), default-on under LLAMA_KV_PAGED (GDN_TC=5 + GDN_CHUNK_MIN=64).
  • 0042 fused residual-add + RMSNorm + weight-mul (dense S_PP +0.5%, BE).
  • 0044 fused GatedRMSNorm + SiLU gate-mul (672 -> 336 launches @npp512; dense +1.1%, MoE +0.9%, test-backend-ops 12979/12979).
  • 0046 GDN-prefill geometry gate (gates 0022's decode retune by scan length; recovers +7.2% dense prefill, keeps the decode win, BE).
  • SSM decode-fusion stack (0018-0022, 0028): in-place state (+23.5%/+18.9%), fused gather (+37.8%/+35.3%), o_proj reshape (+31.7%/+23.3%), conv in-place (+3.2%/+3.5%), occupancy retune (+11.1%/+8.3%) = the 2.26x / 2.46x over stock decode multiplier.
  • Serving host loop closed (0040 S1, 0043 D1).
  • The memory advantage (1.5-3x lower VRAM, NVFP4-resident, no persistent bf16 dequant copies).
  • Low-N decode lead (dense 116.7% @ N=8). Bit-exact output per-path through the whole series.

4.6 REMAINING / unattempted levers + EV

  • Multi-week persistent-Marlin decode kernel (vLLM's fused-Marlin MoE persistent-tiling + Triton elementwise): the only path to the residual ~14 pt GPU-steady decode gap. Low-EV: decode-only ~4-14%, our own ggml Marlin port already lost -19.6%, needs mature tiling + multi-stream overlap (hard inside a single-stream CUDA graph), GB10-uncertain, and cannot lift the prefill floor. Not a free bit-exact lever.
  • Datacenter-Blackwell pivot (B200, ~8 TB/s HBM, native tcgen05/CUTLASS FP4, TMEM): lifts the LPDDR5x GDN bandwidth floor ~30x and restores exactly the vLLM advantages that lose on GB10. This is the documented path to parity. Re-run the methodology on the new silicon, do not reopen GB10 levers.

The VLLM_PARITY_LEVER_MAP.md "pursue list" (A1-A7/B1-B7/C1: graph-safe ragged grouped FP4-MMA MoE kernel, FP8 paged KV, MTP spec-decode, etc.) is the earlier working brainstorm written before the final profiling. VLLM_PARITY_FINAL.md is the authoritative supersession; treat those buckets as rejected / infeasible / different-hardware unless re-validated on new silicon.

Phase 14 re-validated the MTP bucket as safe, then Phase 15 rejected it as a current GB10 serving-throughput lever. Do not enable it by default and do not keep tuning draft length blindly. The only plausible follow-up is a graph-reuse and speculative verification batch-shape profile with nsys --cuda-graph-trace=node. Phase 16 ran that profile and supported the root cause: small-shape baseline reused graphs (graphs reused = 62) while MTP did not (graphs reused = 1) and did ~2.3x more GPU kernel work. The fixed safety gates stayed green before and after the failed serving A/B: MoE md5 8cb0ce23777bf55f92f63d0292c756b0, dense md5 5951a5b4d624ce891e22ab5fca9bc439, and MUL_MAT_ID 806/806.

Phase 17 source inspection found no tiny additive graph-reuse fix. MTP verification rows are real target decode/output rows (K + 1 per speculative slot), so fake padding would touch KV, positions, logits, MTP nextn state, and rollback semantics. If reopened, start with a server-only shape counter around server_slot::handle_last_sampled_token(). Only then consider an opt-in group/defer-by-draft-length scheduler experiment, with TTFT/throughput and md5/op gates as kill criteria.

Phase 18 added the server-only shape trace as patch 0055. Set LLAMA_SPEC_SHAPE_TRACE=1 to log kind=decode rows and MTP kind=verify K + 1 row/output shapes from server_slot::handle_last_sampled_token(). This is default-off instrumentation only. DGX green check after the patch saw MTP verify shapes vary (rows=4, then rows=3) on a tiny request, while the env-unset run emitted no spec shape: lines. Canonical post-patch gates passed: MoE 8cb0ce23777bf55f92f63d0292c756b0, dense 5951a5b4d624ce891e22ab5fca9bc439, and MUL_MAT_ID 806/806. Artifacts: /home/mudler/bench/phase18_mtp_shape_trace_green and /home/mudler/bench/phase18_mtp_shape_trace_green/gate_after.

Next MTP step, if any: trace real serving shape entropy first. Do not implement a scheduler change until the trace shows repeatable draft-length buckets worth grouping. Any scheduler experiment must be opt-in/default-off and killed by TTFT/throughput regression, graph-reuse failure, md5/op drift, or MTP rollback/prefix gate failure.

Phase 19 ran that trace-only serving measurement and rejected the scheduler shortcut. Artifact: /home/mudler/bench/phase19_mtp_shape_entropy/20260701_045534. Pre/post gates passed with canonical MoE md5 8cb0ce23777bf55f92f63d0292c756b0, dense md5 5951a5b4d624ce891e22ab5fca9bc439, and MUL_MAT_ID 806/806.

Serving result:

n baseline decode_agg MTP decode_agg MTP / baseline baseline TTFT ms MTP TTFT ms
8 245.0 95.7 39.1% 1147.2 1633.4
32 409.2 110.0 26.9% 2710.0 4471.5
128 697.2 154.0 22.1% 7601.5 20310.4

Shape result: draft=3 already accounts for 96.2-96.9% of verify slots, so group/defer-by-draft has little to recover. Full in-flight steps already mostly use all-draft=3 vectors; the remaining churn is active-slot/tail churn plus the real K + 1 verification-row expansion. Do not build a Phase 20 scheduler experiment on this evidence. Future MTP work would need a deeper target-verify graph/state design, not another small server scheduling shortcut.

Phase 62 ran that gated verify-cost recheck. Artifact: /home/mudler/bench/phase62_mtp_verify_cost/20260701_134125. Pre/post gates passed with canonical MoE md5 8cb0ce23777bf55f92f63d0292c756b0, dense md5 5951a5b4d624ce891e22ab5fca9bc439, and MUL_MAT_ID 806/806. MTP acceptance was high (7372/9340 = 0.789, mean acceptance length 3.33), but throughput remained far below the keep threshold: 0.420x, 0.274x, and 0.213x baseline decode at n8/n32/n128. Shape trace again showed draft=3 / rows=4 dominance (95.6%), with graphs reused = 1. Keep current MTP rejected unless a later target-verify/output-row graph-cost design exists; do not tune spec-draft-n-max blindly.

Phase 20 refreshed the current-stack MoE serving snapshot against vLLM using the clean ~/llama-phase6-source mirror (f2521ab12) rather than the stale llama-paged-dev benchmark tree. Artifact: /home/mudler/bench/phase20_current_snapshot/20260701_050621. Pre/post gates passed with canonical MoE md5 8cb0ce23777bf55f92f63d0292c756b0, dense md5 5951a5b4d624ce891e22ab5fca9bc439, and MUL_MAT_ID 806/806.

Current MoE serving snapshot (PTOK=128, GEN=64):

n paged decode_agg vLLM decode_agg paged/vLLM decode paged agg vLLM agg paged/vLLM agg
8 220.8 290.5 76.0% 164.8 245.5 67.1%
32 411.1 594.7 69.1% 252.1 456.0 55.3%
128 670.0 1022.7 65.5% 322.4 662.4 48.7%

TTFT remains the clearest user-visible gap: paged is 2.88x/3.36x/3.11x slower than vLLM at n8/n32/n128, and paged prefill_tps is roughly one-third of vLLM. This keeps the GB10 shortcut closure intact: do not reopen MTP or small scheduler work. The credible next parity path is a datacenter-Blackwell rerun or a larger fused-kernel project outside this low-conflict patch stack.

Phase 21 added a reusable current-stack serving harness: backend/cpp/llama-cpp-localai-paged/paged-current-serving-snapshot.sh. It defaults to ~/llama-phase6-source, validates docker/local-ai-worker/GPU idle state, uses the owner-file lock, runs pre/post inference gates, compares paged and vLLM with h2h, and writes ratio summaries. DGX dry run passed at /home/mudler/bench/phase21_harness_dryrun/20260701_051757.

Use this harness for future current-stack GB10 snapshots. Do not reuse ~/bench/combined_definitive.sh unless it is first ported away from stale ~/llama-paged-dev paths and old lock assumptions.

Phase 31 re-verified the patch-series mirror invariant after patch 0057: applying every LocalAI patches/paged/0*.patch with strict git apply on top of Makefile pin 0ed235ea2c17a19fc8238668653946721ed136fd produced tree 4eae628e4ba6f2defa14a19d19f7e4abef9a2647, exactly matching fork branch localai-paged HEAD c78e537b5 feat(cuda): trace moe mmq launch shapes.

Phase 24 extended paged-current-serving-snapshot.sh to write the snapshot hardware report. DGX dry run passed at /home/mudler/bench/phase24_hardware_report_dryrun/20260701_052741; it recorded GPU 0: NVIDIA GB10, driver 580.159.03, compute capability 12.1, and hardware_class=gb10_or_workstation_blackwell. This makes future parity artifacts self-describing: GB10/workstation Blackwell results must not be used as datacenter-Blackwell parity evidence.

Phase 25 extended the same harness to write gate_summary.tsv. The summary was backfilled on the Phase 20 artifact at /home/mudler/bench/phase20_current_snapshot/20260701_050621/gate_summary.tsv; it records pre/post MoE md5 8cb0ce23777bf55f92f63d0292c756b0, dense md5 5951a5b4d624ce891e22ab5fca9bc439, and MUL_MAT_ID 806/806 as ok.

Phase 26 ran the full audited current-stack snapshot with hardware.txt, pre/post gates, same-session paged and vLLM serving runs, summary.tsv, and gate_summary.tsv. Artifact: /home/mudler/bench/phase26_audited_snapshot/20260701_053650. Hardware was recorded as hardware_class=gb10_or_workstation_blackwell, GPU NVIDIA GB10, driver 580.159.03, compute capability 12.1. Every compact gate row was ok: MoE md5 8cb0ce23777bf55f92f63d0292c756b0, dense md5 5951a5b4d624ce891e22ab5fca9bc439, and MUL_MAT_ID 806/806, both before and after the serving run.

Audited current MoE serving snapshot (PTOK=128, GEN=64):

n paged decode_agg vLLM decode_agg paged/vLLM decode paged agg vLLM agg paged/vLLM agg
8 230.8 283.2 81.5% 170.6 241.6 70.6%
32 420.0 609.0 69.0% 254.6 466.7 54.6%
128 673.4 1025.0 65.7% 324.0 656.5 49.4%

Use Phase 26 as the current audit-grade GB10 snapshot. It keeps the Phase 20 verdict intact, but the artifact is more useful for future regressions because it carries hardware classification and compact pre/post inference gates.

Phase 27 re-profiled the current clean llama.cpp n128 serving path with nsys --cuda-graph-trace=node. Artifact: /home/mudler/bench/phase27_graph_node_serving/20260701_055519. The run matched Phase 26 throughput closely (675.5 vs 673.4 decode_agg_tps) and kept gates green before and after the profile (post retry): MoE md5 8cb0ce23777bf55f92f63d0292c756b0, dense md5 5951a5b4d624ce891e22ab5fca9bc439, MUL_MAT_ID 806/806. The node-traced buckets still put the work in gdn_core (29.59%) and mmq_nvfp4 (28.44%); helper dispatch remains too small (mm_ids 0.61%, gather_mmq 0.37%, argsort_topk 0.40%). Do not reopen metadata/helper-only MoE dispatch work on GB10.

Phase 28 tested the remaining low-conflict NVFP4 grouped-MMQ occupancy knobs. Artifact: /home/mudler/bench/phase28_mmq_occupancy/20260701_040450. GGML_CUDA_FP4_MINBLOCKS=2 passed md5/op gates before and after serving (MoE 8cb0ce23777bf55f92f63d0292c756b0, dense 5951a5b4d624ce891e22ab5fca9bc439, MUL_MAT_ID 806/806) but regressed n128 same-session decode serving (705.1 -> 689.9 decode_agg_tps, 0.9784x). GGML_CUDA_FP4_MMQ_Y=64 failed to compile because the NVFP4 writeback specialization asserts nwarps*tile_C::I == mmq_y. Do not promote either knob; future grouped-MMQ work must be structural kernel work.

Phase 29 added the default-off grouped-MMQ shape trace as patch 0056. Artifact: /home/mudler/bench/phase29_mmq_shape_trace/20260701_042428. Fork commit: 20a99518a feat(cuda): trace moe mmq batch shapes. The helper was added test-first (test-cuda-mmq-shape-trace) and built under CUDA on DGX. Default-off and LLAMA_MOE_MMQ_SHAPE_TRACE=4 gates both passed: MoE 8cb0ce23777bf55f92f63d0292c756b0, dense 5951a5b4d624ce891e22ab5fca9bc439, MUL_MAT_ID 806/806. The trace-enabled gate emitted exactly four [LLAMA_MOE_MMQ_SHAPE] lines. This is evidence-only instrumentation; it does not close the speed gap.

Phase 30 used patch 0056 for a live n128 serving shape trace. Artifact: /home/mudler/bench/phase30_mmq_shape_serving/20260701_043300. The first 4096 grouped-MMQ calls split into 1200 decode-like calls (ncols_max <= 128) and 2896 prefill-like calls. Decode-like calls had density 1-4 and selected mmq_x_best only in {32,40,48,64}; prefill-like calls were mostly density 16 and selected mmq_x_best=128. All traced calls had stream_k=1. Post-run gates stayed green: MoE 8cb0ce23777bf55f92f63d0292c756b0, dense 5951a5b4d624ce891e22ab5fca9bc439, MUL_MAT_ID 806/806.

Phase 31 added patch 0057 for default-off grouped-MMQ launch tracing. Artifact: /home/mudler/bench/phase31_mmq_launch_trace/20260701_064424. Fork commit: c78e537b5 feat(cuda): trace moe mmq launch shapes; DGX mirror commit: 8b75905e9. The trace adds [LLAMA_MOE_MMQ_LAUNCH] lines under LLAMA_MOE_MMQ_SHAPE_TRACE=<n>, recording ntiles_dst, stream_k_blocks, tile efficiency, fixup, ntx/nty/ntzw, and compiled mmq_x/mmq_y. Default off, trace-enabled, and post-serving gates stayed green: MoE 8cb0ce23777bf55f92f63d0292c756b0, dense 5951a5b4d624ce891e22ab5fca9bc439, MUL_MAT_ID 806/806. The n128 serving trace showed decode-like 4800/4800 and prefill-like 4920/4920 launch lines with fixup=0 and stream_k_blocks == ntiles_dst. Do not pursue a no-fixup/no-stream-k shortcut for this workload; the remaining grouped-MMQ work is structural small-M kernel work.

Phase 32 added patch 0058 for default-off small-M grouped-MMQ candidate tracing. Artifact: /home/mudler/bench/phase32_small_m_classifier/20260701_070127. Fork commit: 2a9964d29 feat(cuda): trace moe small-m mmq candidates; DGX mirror commit: 024f494d0. The trace adds [LLAMA_MOE_MMQ_SMALL_M] lines under LLAMA_MOE_MMQ_SMALL_M_TRACE=<n> for decode-like low-density grouped-MMQ MoE calls (ncols_max <= 128, density <=4, mmq_x_best <=64). Default-off, trace-enabled, and post-serving gates stayed green: MoE 8cb0ce23777bf55f92f63d0292c756b0, dense 5951a5b4d624ce891e22ab5fca9bc439, MUL_MAT_ID 806/806. The n128 serving trace found 4096 candidate calls, mostly mmq_x_best=64 (1800) and 48 (1096). Phase 33 should A/B a default-off small-M tile policy starting at mmq_x=16.

Phase 33 added patch 0059, default-off LLAMA_MOE_SMALL_M_TILE=<n>, and rejected the simple smaller-tile policy. Artifact: /home/mudler/bench/phase33_small_m_tile_policy/20260701_071136. Fork commit: fbed2abaa feat(cuda): gate moe small-m mmq tile policy; DGX mirror commit: dfd1eaea8. Default-off, tile16, tile8, and post-serving gates stayed green: MoE 8cb0ce23777bf55f92f63d0292c756b0, dense 5951a5b4d624ce891e22ab5fca9bc439, MUL_MAT_ID 806/806. Same-session n128 serving rejected both caps: baseline 672.1 decode_agg_tps, tile16 640.3 (0.953x), tile8 583.2 (0.868x). Do not promote smaller mmq_x caps.

Phase 34 added patch 0060, default-off LLAMA_MOE_MMID_ROUTE_TRACE=<n>, to classify the live MUL_MAT_ID dispatch route without changing route behavior. Artifact: /home/mudler/bench/phase34_mmid_route_trace/20260701_072737. Fork commit: 6c332094c feat(cuda): trace moe mmid routes; DGX mirror commit: 34a256d14. Default-off, trace-enabled, and post-serving gates stayed green: MoE 8cb0ce23777bf55f92f63d0292c756b0, dense 5951a5b4d624ce891e22ab5fca9bc439, MUL_MAT_ID 806/806. Live n128 serving with trace cap 4096 found mmq=2776, mmvq=1320, and host_sync=0/4096. Treat the old current-stack host-sync-fallback concern as refuted for this workload; the remaining MoE work is grouped-MMQ small-M efficiency or another measured bucket.

Phase 35 added patch 0061, default-off LLAMA_MUL_MAT_ROUTE_TRACE=<n>, to classify regular MUL_MAT routes for the projection-heavy serving bucket. Artifact: /home/mudler/bench/phase35_mul_mat_route_trace/20260701_074359. Fork commit: 486c28c63 feat(cuda): trace mul mat routes; DGX mirror commit: 18f7ad005. Default-off, trace-enabled, and post-serving gates stayed green: MoE 8cb0ce23777bf55f92f63d0292c756b0, dense 5951a5b4d624ce891e22ab5fca9bc439, MUL_MAT 1146/1146, MUL_MAT_ID 806/806. Live n128 serving with trace cap 8192 found mat_f=2888, op_cublas=2292, mmq=1328, vec_q=1214, vec_f=470; BF16 (type=30) was split mat_f=2485, op_cublas=1330. Next projection work should target BF16 mat_f/op_cublas subroute evidence or route policy, not batched cuBLAS.

Phase 36 added patch 0062, default-off LLAMA_CUBLAS_ROUTE_TRACE=<n>, to classify the generic cuBLAS MUL_MAT subroute without changing branch behavior. Artifact: /home/mudler/bench/phase36_cublas_route_trace/20260701_081228. Fork commit: 38c4ef2e4 feat(cuda): trace cublas routes; DGX mirror commit: e0224393a. Default-off, trace-enabled, and post-serving gates stayed green: MoE 8cb0ce23777bf55f92f63d0292c756b0, dense 5951a5b4d624ce891e22ab5fca9bc439, MUL_MAT 1146/1146, MUL_MAT_ID 806/806. Live n128 serving with trace cap 8192 found bf16_tc=5681 and sgemm=2511. The next projection phase should explain whether the F32 SGEMM shapes are expected glue tensors or a missed BF16 route; do not chase NVFP4 cuBLAS or batched cuBLAS for this measured bucket.

Phase 37 added patch 0063, extending LLAMA_CUBLAS_ROUTE_TRACE=<n> with src0, src1, and dst tensor names. Artifact: /home/mudler/bench/phase37_cublas_name_trace/20260701_083227. Fork commit: 2d590d770 feat(cuda): trace cublas tensor names; DGX mirror commit: 2cbb61969. Default-off, trace-enabled, and post-serving gates stayed green: MoE 8cb0ce23777bf55f92f63d0292c756b0, dense 5951a5b4d624ce891e22ab5fca9bc439, MUL_MAT 1146/1146, MUL_MAT_ID 806/806. Live n128 trace found bf16_tc=2884, sgemm=1212. The sgemm bucket is blk.N.ffn_gate_inp.weight -> ffn_moe_logits-N and blk.N.ffn_gate_inp_shexp.weight -> shared_expert_gate-N; do not force BF16 without first inspecting model-load tensor types and running KL validation.

Phase 38 is the current gate-projection policy checkpoint. Artifact: /home/mudler/bench/phase38_gate_baseline/20260701_084410. Preflight showed docker 0, local-ai-worker 0, compute apps 0, and GB10 driver 580.159.03. Fresh baseline gates against the Phase37 build passed: MoE 8cb0ce23777bf55f92f63d0292c756b0, dense 5951a5b4d624ce891e22ab5fca9bc439, MUL_MAT 1146/1146, MUL_MAT_ID 806/806. Source comparison found llama.cpp and vLLM both keep router and shared-expert gate weights unquantized; vLLM's relevant idea is fused F32 gate weight concatenation, not BF16/NVFP4 routing. Future fused-gate work must be default-off, preserve F32 semantics, and pass md5/op gates before benchmarking; if md5 changes, run KL first.

Phase 39 closes the naive fused-gate shortcut. Artifact: /home/mudler/bench/phase39_gate_sgemm_profile/phase27_reanalysis. Re-analysis of the Phase27 graph-node serving profile showed total kernel time 20.0372s, concat_layout=459.84ms (2.29%, 2250 instances), cublas_bf16_gemm=1892.81ms (9.45%), and cutlass_bf16_gemm=684.01ms (3.41%). Do not implement graph-time ggml_concat() of ffn_gate_inp.weight plus ffn_gate_inp_shexp.weight; it risks increasing an existing layout-copy bucket. The only future fused-gate design worth scoping is a persistent/load-time F32 combined gate weight with output views, default-off until MoE/dense md5, MUL_MAT, MUL_MAT_ID, and KL-if-md5-changes gates pass.

Phase 40 closes the tested GB10 max-concurrency C1 shortcut. Artifact: /home/mudler/bench/phase40_max_concurrency/20260701_090012. The snapshot ran with PARALLEL=256, CTX=262144, PTOK=128, GEN=64, NPL="128 192 256", and OPS=MUL_MAT,MUL_MAT_ID. Pre/post gates stayed green: MoE 8cb0ce23777bf55f92f63d0292c756b0, dense 5951a5b4d624ce891e22ab5fca9bc439, MUL_MAT 1146/1146, MUL_MAT_ID 806/806. Paged safely served n=256, but vLLM also fit and remained faster: paged_decode_over_vllm=0.6354, paged_agg_over_vllm=0.4721, paged_ttft_over_vllm=2.9401. Do not claim GB10 parity from higher max concurrency at this prompt/gen length and n<=256; a future C1 retry must push beyond this tested point and keep the same md5/op gates.

Phase 41 records the low-concurrency counterpart to the Phase40 high-concurrency check. Artifact: /home/mudler/bench/phase41_low_concurrency/20260701_091437. The snapshot ran with PARALLEL=32, CTX=32768, PTOK=128, GEN=64, NPL="1 8 32", and OPS=MUL_MAT,MUL_MAT_ID. Pre/post gates stayed green: MoE 8cb0ce23777bf55f92f63d0292c756b0, dense 5951a5b4d624ce891e22ab5fca9bc439, MUL_MAT 1146/1146, MUL_MAT_ID 806/806. Paged is about 0.75x vLLM decode at n=1/8 and 0.665x at n=32; TTFT is 1.38x, 3.14x, and 3.40x vLLM respectively. Do not reopen D1 from this result: 0043 already ships grouped-MMQ full-step graph capture default-on, Phase34 found host_sync=0/4096, and S3 is default-off because it regressed TTFT/end-to-end throughput.

Phase 42 reconciles the target list after parallel read-only review. D1 is closed on the current GB10 path; GDN low-conflict work is exhausted after 0046/0047 plus the rejected C32/QS-early/Global-Ai32 follow-ups; W4A16/GEMM micro-tweaks are exhausted after 0033-0035 and 0048-0050. It nominated the Phase38/39 persistent/load-time F32 combined gate projection as the last small GB10 source candidate.

Phase 43 rejects that gate-fusion candidate as a small shortcut after source inspection. ffn_gate_inp.weight and ffn_gate_inp_shexp.weight are separate GGUF tensors; the Qwen35MoE graph consumes them in separate matmuls; the loader can create tensors from GGUF metadata or views of existing tensors, but not a new persistent derived concatenated weight. A correct implementation would need a general derived-weight allocation/materialization path across mmap, offload, split buffers, and MTP blocks. Do not implement a Qwen-only loader hack, and do not fall back to graph-time ggml_concat(). After Phase43 there is no remaining low-conflict GB10 shortcut justified by current evidence; future work is either a larger kernel/loader design or a hardware-pivot benchmark, still gated by MoE/dense md5 plus MUL_MAT/MUL_MAT_ID and KL if md5 changes.

Phase 44 makes the current-stack serving snapshot harness ready for hardware pivots by parameterizing the vLLM side instead of hardcoding the GB10 defaults. paged-current-serving-snapshot.sh now accepts VLLM_GPU_MEMORY_UTILIZATION, VLLM_MAX_MODEL_LEN, VLLM_MAX_NUM_SEQS, VLLM_TENSOR_PARALLEL_SIZE, and whitespace-split VLLM_EXTRA_ARGS, and prints the resolved values during DRY_RUN=1. This is not a new benchmark and does not change inference code or gate behavior. Use it when the next parity run targets datacenter Blackwell or another non-GB10 vLLM serving shape, while keeping hardware.txt, pre/post MoE/dense md5, MUL_MAT/MUL_MAT_ID, and KL-if-md5-changes as mandatory gates.

Phase 45 records the immediate inference-safety guard after Phase44. Artifact: /home/mudler/bench/phase45_inference_gate_guard/20260701_094320. The DGX phase36 build passed MoE md5 8cb0ce23777bf55f92f63d0292c756b0, dense md5 5951a5b4d624ce891e22ab5fca9bc439, MUL_MAT 1146/1146, and MUL_MAT_ID 806/806. Docker, local-ai-worker, and GPU compute preflight were all zero before and after the run.

Phase 46 removes the last hardcoded q36 served-model name from the audited serving snapshot harness. Set SERVED_MODEL_NAME to drive vLLM --served-model-name, the vLLM readiness check, and h2h --model on both engines. DGX dry run: /home/mudler/bench/phase46_served_model_name_dryrun/20260701_094849, with SERVED_MODEL_NAME=dense-q36 printed during DRY_RUN=1. This is harness-only hardware-pivot readiness, not a throughput result.

Phase 47 attempted the first dense serving snapshot using the Phase46 override. Dry-run artifact: /home/mudler/bench/phase47_dense_serving_dryrun/20260701_095141; incomplete full artifact: /home/mudler/bench/phase47_dense_serving/20260701_095151. Pre-gates were green and the paged dense arm completed through n=128, but the artifact is not a dense parity result because vLLM produced no result JSONs. Root cause: dense vLLM startup exceeded the old fixed readiness budget, and the cleanup path could wait indefinitely on the server PID after SIGTERM.

Phase 48 hardens the serving snapshot harness for that failure mode. It adds LLAMA_READY_ATTEMPTS and VLLM_READY_ATTEMPTS, bounds HTTP readiness probes with curl --max-time 2, and uses bounded server cleanup that escalates from SIGTERM to SIGKILL. Dry-run artifact: /home/mudler/bench/phase48_readiness_harness_dryrun/20260701_100533, with VLLM_READY_ATTEMPTS=700 printed and clean DGX preflight.

Phase 47 retry completed after Phase48. Artifact: /home/mudler/bench/phase47_dense_serving_retry/20260701_100811. Pre/post gates were green: MoE 8cb0ce23777bf55f92f63d0292c756b0, dense 5951a5b4d624ce891e22ab5fca9bc439, MUL_MAT 1146/1146, MUL_MAT_ID 806/806. Dense paged decode beats vLLM at low concurrency (1.3434x at n=1, 1.1560x at n=8) but falls behind at n=32/128 (0.9036x, 0.7912x), and TTFT remains 1.87x to 4.05x vLLM. This does not change the GB10 conclusion.

Phase 49 removes vLLM log noise from harness-owned environment variables. The vllm serve child now unsets VLLM_MODEL, VLLM_BIN, VLLM_READY_ATTEMPTS, VLLM_GPU_MEMORY_UTILIZATION, VLLM_MAX_MODEL_LEN, VLLM_MAX_NUM_SEQS, VLLM_TENSOR_PARALLEL_SIZE, and VLLM_EXTRA_ARGS while preserving intentional vLLM runtime variables such as VLLM_LOGGING_LEVEL. Dry run: /home/mudler/bench/phase49_vllm_env_hygiene_dryrun/20260701_102138.

Phase 50 resolves the dense high-N decode-accounting question with a graph-node difference-method profile. Artifact: /home/mudler/bench/phase50_dense_true_decode/20260701_103120. Pre/post inference gates on the profiled build-cuda binary stayed green: MoE 8cb0ce23777bf55f92f63d0292c756b0, dense 5951a5b4d624ce891e22ab5fca9bc439, MUL_MAT 1146/1146, and MUL_MAT_ID 806/806. Dense npl=128, npp=128 true decode is 383.66 t/s for paged and 435.00 t/s for vLLM, ratio 0.8820. This means Phase47's 0.7912 h2h decode ratio and 0.5071 aggregate ratio include scheduler/admission and prefill-overlap/accounting effects beyond the real GPU-steady decode gap. Next GB10 code work should instrument batch composition/admission in server_context::pre_decode() before attempting another kernel shortcut.

Phase 51 implements that admission trace in the llama.cpp fork. Local fork commit: c6cb8460e feat(server): trace serving admission batches. The trace is default-off behind LLAMA_SERVING_TRACE=1, adds a small unit-tested accumulator, and records aggregate pre_decode() scheduler shape: decode tokens, prompt tokens admitted, waiting prompt slots, started/continued prompt slots, decode-only steps, n_batch, n_ubatch, prefill_budget_step, and prefill_cap_per_slot. DGX artifact: /home/mudler/bench/phase51_serving_admission_trace/20260701_110130. The patched build-cuda CTest passed and inference gates stayed green: MoE 8cb0ce23777bf55f92f63d0292c756b0, dense 5951a5b4d624ce891e22ab5fca9bc439, MUL_MAT 1146/1146, MUL_MAT_ID 806/806. Push and LocalAI patch-series regeneration are still pending because push requires explicit approval.

Phase 52 uses the Phase51 trace on DGX for dense n=128, ptok=128, gen=64. Artifact: /home/mudler/bench/phase52_dense_admission_trace/20260701_111017. Pre/post md5 and op gates stayed green. The clean traced h2h row was decode_agg_tps=360.5, prefill_tps=629.5, ttft_mean_ms=23171.5, wall 58.921s. The admission trace reported steps=76, decode_only_steps=0, decode_tokens=8064, prompt_tokens=22785, max_waiting_prompt_slots=35, started_prompt_slots=128, continued_prompt_slots=139, prefill_budget_step=0, and prefill_cap_per_slot=0. The prompt token count matches h2h exactly, so this is the target request. The next GB10 lever should be a default-off scheduler/admission A/B or a per-step histogram trace, not an immediate GDN/GEMM rewrite.

Phase 53 tested the existing runtime admission-budget knobs instead of adding new code. Artifact: /home/mudler/bench/phase53_dense_admission_budget_sweep/20260701_111915. Pre/post gates stayed green. Dense n=128 results: default Phase52 agg=139.0, decode_agg=360.5, prefill=629.5, TTFT=23171.5ms, wall 58.921s; T=1536 cap=512 agg=134.4, decode_agg=376.7, prefill=607.0, TTFT=22263.7ms, wall 60.968s; T=1024 cap=512 agg=130.0, decode_agg=392.4, prefill=565.2, TTFT=23234.3ms, wall 63.003s. Decision: simple budget shrinkage is rejected. It raises h2h decode-agg while lowering aggregate/prefill throughput, and it does not materially solve TTFT. Next scheduler work should be per-step histograms or a targeted first-token admission policy.

Phase 54 through Phase 59 tested that targeted scheduler path. The fork commits are still local-only and default-off:

  • c6cb8460e feat(server): trace serving admission batches
  • bd7b2e952 feat(server): add admission trace histograms
  • 8a97629a4 feat(server): add TTFT prefill-first scheduler mode
  • 3b6ab5fa8 feat(server): cap TTFT prefill-first decode deferral
  • 8759213e3 feat(server): gate TTFT defer by prompt backlog

Phase59 is the current verdict. Artifact: /home/mudler/bench/phase59_moe_min32_repeat_vllm/20260701_123147. Pre/post llama gates stayed green: MoE 8cb0ce23777bf55f92f63d0292c756b0, dense 5951a5b4d624ce891e22ab5fca9bc439, MUL_MAT 1146/1146, MUL_MAT_ID 806/806. MoE n=128, ptok=128, gen=64 repeated the Phase58 min32 signal: llama default agg=336.6, TTFT=7798.5ms, wall 24.334s; llama min32 agg=336.9, TTFT=7167.8ms, wall 24.316s. Matching vLLM was still agg=601.3, TTFT=2968.1ms, wall 13.563s.

Decision: keep LLAMA_TTFT_PREFILL_FIRST=1 and LLAMA_TTFT_PREFILL_FIRST_MIN_WAITING=32 as an opt-in llama.cpp latency/QoS knob. It does not prove vLLM parity progress by itself. Do not default it until more workload coverage exists, and do not regenerate LocalAI patches until the fork commits are pushed with explicit approval.

Phase 60 re-profiled the current W4A16 grouped MoE prefill path to check whether there was still a low-conflict W4A16 shortcut after Phase1-5. Artifact: /home/mudler/bench/phase60_w4a16_current_profile/20260701_104915. Pre/post gates stayed green: MoE 8cb0ce23777bf55f92f63d0292c756b0, dense 5951a5b4d624ce891e22ab5fca9bc439, MUL_MAT 1146/1146, MUL_MAT_ID 806/806. Default FP4-MMQ S_PP was 2327.69 at npp=512 and 2423.20 at npp=2048; forced W4A16 was 1451.00 and 1482.76, only 0.623x and 0.612x of default. The npp=512 profile showed W4A16 still dominated by w4a16_grouped_kernel (4.142s, 42.5%) plus sorted activation gathers (1.094s, 11.2%), while the cast kernel was only 0.517s (5.3%).

Decision: do not add another small W4A16 metadata/body/cast patch. Future W4A16 work needs a larger redesign that improves the grouped kernel body and removes or fuses sorted activation movement. Near-term GB10 parity work should return to broader prefill/GDN/MoE design or hardware-pivot benchmarking.

Phase61 is scoped as that larger W4A16 kill-gate, not as a committed code change: docs/superpowers/plans/2026-07-01-w4a16-direct-activation-phase61.md. It proposes a default-off LLAMA_W4A16_DIRECT_A=1 experiment that consumes the original activation tensor plus the existing ids_to_sorted map directly, removing Phase60's sorted activation gather and separate cast kernels before any grouped-kernel body rewrite. Keep it only if it improves forced W4A16 S_PP by at least +12% and reaches at least 0.75x default FP4-MMQ; otherwise reject and do not continue W4A16 body tuning.

Phase61 result: rejected. The direct-A kernel passed correctness after matching get_rows_cuda flat-row addressing (MUL_MAT_ID 806/806; forced/direct-A MoE transcript md5 both 07db32c2bcb78d17a43ed18bc22705cd) and default gates remained green (8cb0ce23, 5951a5b4, MUL_MAT 1146/1146, MUL_MAT_ID 806/806). But direct-A only improved forced W4A16 S_PP 1471.05 -> 1566.30 at npp=512 and 1502.46 -> 1605.82 at npp=2048 (+6.5% / +6.9%), still just 0.67x / 0.66x of default FP4-MMQ. The direct kernel diff was not committed; only the safe policy/routing stub remains in the fork. Do not pursue more W4A16 body tuning on GB10 as the next parity lever.

5. METHODOLOGY LESSONS (so you do not repeat the mistakes)

  1. Profile, don't assume. The analysts were wrong 4 times. Every one was caught only by an in-backend A/B or a corrected profile:
    • GDN-scalar grep (assumed the scan was scalar/serial from reading source) - wrong, retired by the tensor-core port.
    • dense-cuBLAS reroute (assumed dequant->bf16 would win) - wrong, -31% to -62%.
    • occupancy (assumed blocks/SM was the GDN bound) - wrong, 1844 vs 1814 within noise.
    • projection-regime (assumed FP8/NVFP4 projections were a big lever) - wrong, projections are ~12% of the decode stream at high N. In-backend A/B is the only truth. A standalone PoC win (0034) is not a result.
  2. Per-kernel us/tok overstates end-to-end S_PP/S_TG. A kernel that is X% faster in isolation does not move throughput X%; always confirm against the end-to-end batched-bench / serving number.
  3. The CUDA-graph-trace decode artifact (the big one). Decode is a replayed graph; nsys without --cuda-graph-trace=node collapses it and lies. This single trap produced the wrong "host-bound / 159 us/tok / 56%" story across multiple analyses. Always graph-node-trace + difference method (section 3.4).
  4. Beware GPU contention skewing absolutes. The box runs concurrent quant/repack/finetune jobs. Gate on idle GPU + free lock; prefer the same-session both-engine harness so both numbers move together.
  5. The vLLM server number is inflated ~8 pt vs its true GPU-steady. vLLM's chunked-prefill-overlap inflates its own server-measured decode window (1177 server vs 1078 true GPU-steady). Compare GPU-steady to GPU-steady, or you will chase a phantom gap. The reconciliation chain that must sum: vLLM server 1177 (100%) -> vLLM true GPU-steady 1078 (92%) -> llama GPU-steady 924 (78.5% of 1177, = 86% of 1078) -> llama server 718 (60.7%, the S3-recoverable serving overhead).

6. THE THREE FORWARD DIRECTIONS

(a) Close / ship the record (lowest effort, do this first)

The investigation is closed for GB10 shortcuts, and the closeout chores below are now done:

  • patch 0044 is tracked in the LocalAI series;
  • the Makefile pin 0ed235ea2c17a19fc8238668653946721ed136fd is the authoritative paged pin;
  • Phase 20 re-ran the current-stack serving snapshot on the clean mirror;
  • Phase 22 re-verified the patch-series mirror invariant after 0055.

For future release checks, run paged-inference-gates.sh and paged-current-serving-snapshot.sh from the LocalAI backend tree. The inference gate now defaults to both MUL_MAT and MUL_MAT_ID; set OPS= only for a focused diagnostic run.

(b) Datacenter-Blackwell pivot (THE real parity path)

The thesis: every vLLM advantage that wins on GB10 is a kernel that is broken or capped on consumer Blackwell and inverts on datacenter Blackwell (B200): FLA blocked-solve GDN, Marlin/CUTLASS grouped FP4, HBM-tuned full-cudagraph decode, native tcgen05/TMEM. ~8 TB/s HBM lifts the LPDDR5x GDN bandwidth floor ~30x. Concrete first steps:

  1. Acquire a B200 (or equivalent HBM tcgen05 part). Reproduce the both-engine same-session harness there (combined_definitive.sh discipline): build the stock and paged binaries, build vLLM 0.23.0+, run MoE + dense prefill + serving for both engines.
  2. Re-measure the FP4 path: on B200, native CUTLASS NVFP4 grouped-GEMM should work (the CUTLASS #3096 / TMA-WS failure is consumer-Blackwell-specific). Confirm whether vLLM now runs native FP4 instead of Marlin W4A16. If so, the 4.1 GEMM track must be re-evaluated from scratch (it was rejected on a GB10-specific ceiling).
  3. Re-take the decode profile with --cuda-graph-trace=node; the GDN scan that floors at 273 GB/s on GB10 should no longer dominate at HBM bandwidth - re-derive the per-token decomposition before choosing any lever.

(c) Multi-week persistent-Marlin decode kernel (decode-only, low-EV, CANNOT reach parity)

Only pursue if (a)+(b) are not options and someone explicitly wants the residual decode gap closed on GB10. It targets the ~14 pt GPU-steady decode gap (vLLM's fused-Marlin MoE persistent-tiling + single Triton elementwise). Concrete first steps:

  1. Re-confirm the ceiling first: our own ggml Marlin port already lost -19.6% at decode (4.3), so the bar is "beat that and beat FP4-MMQ at the decode BW floor".
  2. Prototype the persistent-tiling grouped-FP4 MoE kernel standalone, then prove it in-backend (a PoC win is not a result, per 0034). It must live inside a single-stream CUDA graph or bring its own multi-stream overlap.
  3. Bound the upside honestly: this is decode-only ~4-14% and does nothing for the prefill floor (36-43%), so it does not reach parity. Record the verdict either way.

7. KEY FILE / ARTIFACT INDEX

Fork (canonical source of truth)

  • Local canonical fork: /home/mudler/_git/llama.cpp, branch localai-paged, HEAD 2d590d770 ("trace cublas tensor names", patch 0063).
  • DGX current clean mirror/build tree: dgx:~/llama-phase6-source, HEAD 2cbb61969 with the Phase 37 cuBLAS tensor-name trace patch applied and committed; Phase 20/26/27 artifacts still record their historical source hashes.
  • Historical DGX dev tree: dgx:~/llama-paged-dev, branch paged, HEAD a7d439e8ce6990eb09721223c975da4e49d8d136 ("GDN CONFIG C (M8) - bf16 Kc/Qc"). It is an old experimental tree and must not be treated as canonical.

LocalAI worktree

  • Path: /home/mudler/_git/LocalAI/.claude/worktrees/feat+paged-attention, branch worktree-feat+paged-attention (currently 246 ahead, 31 behind origin/master; recompute before reporting).
  • Backend dir: backend/cpp/llama-cpp-localai-paged/ (Makefile thin wrapper, package.sh, run.sh, README.md ~44 KB canonical, docs/, patches/paged/).
  • docs/: VLLM_PARITY_FINAL.md (authoritative record), VLLM_PARITY_LEVER_MAP.md (working brainstorm, profile-validated section), DECODE_SERVING_SCOPE.md, PREFILL_GEMM_SCOPE.md, PREFILL_GEMM_RESULTS.md, TENSORCORE_GDN_SCOPE.md, TENSORCORE_GDN_BUILD_PLAN.md, ACCELERATOR_PORTING_SCOPE.md, UPSTREAM_LAYER2_SCOPE.md, LOCALAI_LLAMACPP_BACKEND_PLAN.md, PAGED_BITEXACT_NOTE.md, PATCH_MAINTENANCE.md, final_benchmark.csv, paged-burst-bench.cpp, paged-reclaim-unit.cpp, 3 PNGs, and this PARITY_HANDOFF.md.
  • patches/paged/: 54 .patch files spanning 0001-0063 with intentional gaps (missing 0005, 0026 [dropped ssm_bf16_tau], 0027, 0032, 0036-0039, 0045). Core paged-KV 0001-0012; decode-first scheduler 0013/0016; serving graph reuse 0040/0041; prefill fusions 0042/0044; SSM/GDN decode 0018-0022/0028; MoE NVFP4 quant 0023/0025/0043; FP4-MMA/Marlin scaffolds 0033/0034/0035 (default-off); GDN tensor-core prefill 0031 -> 0046 (geometry gate) -> 0047 (f32-only M5, default-on under paged KV); W4A16 packed metadata/shape/padding is 0048-0050; MoE safety tests are 0051-0053; MTP backend-sampling safety is 0054; speculative shape trace is 0055; MoE MMQ selector/launch/candidate/tile-policy/route instrumentation is 0056-0060; regular MUL_MAT route instrumentation is 0061; cuBLAS route instrumentation is 0062-0063.

Bench artifacts (DGX)

  • ~/bench/COMBINED_DEFINITIVE.txt (+ .log, .done, combined_definitive.sh, combined_definitive.out) - historical same-session both-engine run.
  • ~/bench/phase20_current_snapshot/20260701_050621 - current clean-stack paged-vs-vLLM MoE serving snapshot.
  • ~/bench/phase21_harness_dryrun/20260701_051757 - current snapshot harness dry-run artifact.
  • ~/bench/phase24_hardware_report_dryrun/20260701_052741 - current snapshot harness dry run proving hardware.txt captures the DGX as hardware_class=gb10_or_workstation_blackwell.
  • ~/bench/phase25_gate_summary_dryrun/20260701_053353 - dry run after adding gate_summary.tsv support; normal dry-run still writes hardware.txt and does not emit a gate summary before gates exist.
  • ~/bench/phase26_audited_snapshot/20260701_053650 - current audit-grade full paged-vs-vLLM MoE serving snapshot with hardware.txt, pre/post gates, summary.tsv, and gate_summary.tsv.
  • ~/bench/phase27_graph_node_serving/20260701_055519 - current clean llama.cpp n128 serving profile captured with --cuda-graph-trace=node, pre/post retry gates green.
  • ~/bench/phase28_mmq_occupancy/20260701_040450 - NVFP4 MMQ occupancy build-knob A/B; MINBLOCKS=2 gate-safe but serving-regressed, MMQ_Y=64 compile-rejected.
  • ~/bench/phase29_mmq_shape_trace/20260701_042428 - default-off MoE MMQ shape trace patch 0056; CUDA build plus default/trace md5 gates green.
  • ~/bench/phase30_mmq_shape_serving/20260701_043300 - live n128 serving MMQ shape distribution from patch 0056; post-run md5/op gates green.
  • ~/bench/phase31_mmq_launch_trace/20260701_064424 - default-off MoE MMQ launch trace patch 0057; default/trace/post-serving md5 gates green; n128 launch trace rejects stream-k/fixup shortcut (fixup=0, stream_k_blocks == ntiles_dst).
  • ~/bench/phase32_small_m_classifier/20260701_070127 - default-off MoE MMQ small-M classifier patch 0058; default/trace/post-serving md5 gates green; n128 trace found 4096 candidate calls.
  • ~/bench/phase33_small_m_tile_policy/20260701_071136 - default-off MoE MMQ small-M tile policy patch 0059; tile16/tile8 md5/op safe but both slower in n128 serving.
  • ~/bench/phase34_mmid_route_trace/20260701_072737 - default-off MoE MMID route trace patch 0060; default/trace/post-serving md5 gates green; n128 route trace found mmq=2776, mmvq=1320, host_sync=0/4096.
  • ~/bench/phase35_mul_mat_route_trace/20260701_074359 - default-off regular MUL_MAT route trace patch 0061; default/trace/post-serving md5 gates green; n128 route trace found BF16 mat_f=2485, op_cublas=1330.
  • ~/bench/phase36_cublas_route_trace/20260701_081228 - default-off cuBLAS subroute trace patch 0062; default/trace/post-serving md5 and op gates green; n128 route trace found bf16_tc=5681, sgemm=2511.
  • ~/bench/phase37_cublas_name_trace/20260701_083227 - cuBLAS tensor-name trace patch 0063; default/trace/post-serving md5 and op gates green; n128 trace identified sgemm as MoE gate logits and shared-expert gate projections.
  • ~/bench/phase38_gate_baseline/20260701_084410 - current Phase37 build baseline before gate-projection policy work; docker/local-ai-worker/GPU idle preflight green; MoE/dense md5 green; MUL_MAT 1146/1146; MUL_MAT_ID 806/806.
  • ~/bench/phase39_gate_sgemm_profile/20260701_085211 - short completion profile, diagnostic only because -n 32 is not a canonical md5 gate; useful for confirming graph-time concat is a real kernel path.
  • ~/bench/phase39_gate_sgemm_profile/phase27_reanalysis - Phase27 serving profile re-analysis used to reject graph-time fused gate weight concat; concat_layout=459.84ms (2.29%) in the serving kernel window.
  • ~/bench/phase40_max_concurrency/20260701_090012 - max-concurrency C1 check at NPL=128/192/256, PTOK=128, GEN=64, PARALLEL=256, CTX=262144; pre/post MoE/dense md5 and MUL_MAT/MUL_MAT_ID gates green, but vLLM also fit at n=256 and stayed ahead (paged_decode_over_vllm=0.6354, paged_agg_over_vllm=0.4721).
  • ~/bench/phase41_low_concurrency/20260701_091437 - low-concurrency serving check at NPL=1/8/32, PTOK=128, GEN=64, PARALLEL=32, CTX=32768; pre/post MoE/dense md5 and MUL_MAT/MUL_MAT_ID gates green; paged is 0.7493, 0.7518, and 0.6649 of vLLM decode at n=1/8/32, with TTFT still much worse by n=8/32; does not reopen D1.
  • ~/bench/phase44_hardware_pivot_harness_dryrun/20260701_094038 - harness-only dry-run artifact proving the vLLM serving config overrides are printed and preflighted before any server starts.
  • ~/bench/phase45_inference_gate_guard/20260701_094320 - post-Phase44 inference guard; MoE/dense md5 and MUL_MAT/MUL_MAT_ID backend-op gates green.
  • ~/bench/phase46_served_model_name_dryrun/20260701_094849 - harness-only dry-run artifact proving SERVED_MODEL_NAME is printed and preflighted before any server starts.
  • ~/bench/phase47_dense_serving_dryrun/20260701_095141 - dense serving dry-run with SERVED_MODEL_NAME=dense-q36.
  • ~/bench/phase47_dense_serving/20260701_095151 - incomplete dense serving attempt; pre-gates and paged arm completed, vLLM did not produce result JSONs under the old readiness budget.
  • ~/bench/phase48_readiness_harness_dryrun/20260701_100533 - harness dry-run proving configurable readiness budgets and clean preflight before retrying dense serving.
  • ~/bench/phase47_dense_serving_retry/20260701_100811 - completed dense serving snapshot after Phase48; pre/post md5 and op gates green; paged low-N decode ahead, high-N aggregate and TTFT behind.
  • ~/bench/phase49_vllm_env_hygiene_dryrun/20260701_102138 - harness dry-run after scrubbing harness-owned VLLM_* variables from the vllm serve child environment.
  • ~/bench/phase50_dense_true_decode/20260701_103120 - dense graph-node difference-method profile at npl=128, npp=128; build-cuda pre/post md5 and op gates green; true decode paged 383.66 t/s, vLLM 435.00 t/s, ratio 0.8820, pointing next at serving admission/scheduler tracing.
  • ~/bench/phase51_serving_admission_trace/20260701_110130 - default-off LLAMA_SERVING_TRACE=1 fork commit c6cb8460e; DGX patched build-cuda CTest and md5/op gates green; push and LocalAI patch-series mirror pending approval.
  • ~/bench/phase52_dense_admission_trace/20260701_111017 - clean dense n=128 admission trace; pre/post gates green; decode_only_steps=0, prompt_tokens=22785, max_waiting_prompt_slots=35; next lever is scheduler/admission A/B or per-step histogram trace.
  • ~/bench/phase53_dense_admission_budget_sweep/20260701_111915 - runtime sweep of LLAMA_MAX_BATCH_TOKENS=1536/1024 with LLAMA_PREFILL_CAP=512; pre/post gates green; simple budget shrinkage rejected because aggregate/prefill throughput regressed and TTFT did not materially improve.
  • Per-engine logs ~/bench/COMBINED_{paged,vllm}_{MOE,DENSE}_server.log; ~/bench/BENCHMARK_PROGRESS.md.
  • Graph-node-traced high-N profiles: ~/highN_prof2/*.nsys-rep (paged npl=256), ~/highN_vllm/*.nsys-rep (vLLM), 2026-06-30.
  • A/B dirs: ~/bench/marlin_gate/, ~/bench/gdn_p1_ab/.

Recent context commits

  • 6edbb56b0 "docs(paged): definitive vLLM-parity final-state record (GB10, CLOSED)" - adds VLLM_PARITY_FINAL.md.
  • baf102524 "docs(paged): correct decode-serving record to ~86% GPU-steady parity (graph-node-traced)" - the ~56% -> ~86% correction.
  • bd100dd20 "fix(paged): repair the patch series, sync to the fork branch" - dropped dev-tree 0044/0045, added f32-only M5 as 0047.
  • b028c81ed "docs(paged): record padded/fixed-slot decode shape as tested-and-rejected".

Discrepancies to flag / resolve (carried verbatim from the gather, including UNVERIFIED labels)

  1. Pin prose reconciled in this worktree. Makefile line 52 LLAMA_VERSION?=0ed235ea2c17a19fc8238668653946721ed136fd is authoritative and matches the local fork merge-base. Hard rule: the paged pin must equal the stock llama-cpp pin (shared grpc-server.cpp); a bump to c299a92c once broke the grpc-server link despite being bit-exact and was reverted. Trust the Makefile when building.
  2. Current fork/mirror are clean and verified. Local fork HEAD is 2d590d770, DGX clean mirror HEAD is 2cbb61969, and Phase 37 should be treated as the current patch-series tip. The old llama-paged-dev tree is historical only.
  3. Worktree patch series is tracked through 0063. The only expected unrelated untracked path in this worktree is .claude/.
  4. sm_121a is not in the worktree build files - it lives only in the DGX experimental build scripts (gdn_cc.sh, gdn_bv_build.sh, paged-build.sh); mainline uses arch 121. UNVERIFIED whether the shipped CI Dockerfile build path injects 121a for the FP4-MMA kernels (Dockerfile.llama-cpp-localai-paged does not hardcode a CUDA arch).
  5. The 0921716... paged-MoE md5 open item. COMBINED_DEFINITIVE.txt records PAGED_GATE_MD5=0921716cd0582b5d15af8c362b811d00 for MoE, but a full doc/patch/git log -S grep of the worktree found no occurrence of 0921716... in any committed source; the committed canonical paged-MoE gate is 8cb0ce23. Treat this as unreconciled: the documented, KL-validated paged-MoE gate remains 8cb0ce23, and any paged-MoE divergence (including 0921716) must be KL-validated against the f16 reference before being accepted as benign, never on assertion alone. The 0921716 value is UNVERIFIED as a sanctioned gate; do not adopt it as canonical without re-running the KL gate. The dense run is symmetric: COMBINED_DEFINITIVE.txt records PAGED_GATE_MD5=ecfe924dee6c5622c149f419ff2a6481 for dense, which likewise differs from the canonical dense gate 5951a5b4. Both CDEF PAGED_GATE_MD5 values come from the combined_definitive.sh harness's own gate command, NOT the canonical bit-exact gate command in section 3.3, which is why they diverge from the committed 8cb0ce23 / 5951a5b4; neither is a sanctioned gate and both must be KL-validated before being treated as benign.

8. PHASE63 RESULT: PREFILL BUCKET ATTRIBUTION

Phase63 is complete as a measurement-only no-go. The plan is docs/superpowers/plans/2026-07-01-prefill-bucket-attribution-phase63.md; the DGX artifact is /home/mudler/bench/phase63_prefill_bucket/20260701_140127.

Pre/post gates stayed green:

  • MoE md5 8cb0ce23777bf55f92f63d0292c756b0;
  • dense md5 5951a5b4d624ce891e22ab5fca9bc439;
  • MUL_MAT 1146/1146;
  • MUL_MAT_ID 806/806.

The candidate paged FlashAttention mask/block-table cleanup is rejected for now: llama.cpp FA is only 0.71% at npp=512 and 1.18% at npp=2048; the npp=2048 cross-engine FA delta is about 1.7 us/tok, not the 15 us/tok needed to fund source work. No llama.cpp source files were modified.

Status: Phase63 closed. VLLM_PARITY_FINAL.md remains the GB10 shortcut record; the remaining measured buckets are still MoE/FFN GEMM, GDN, bf16 projections, layout copies, and activation quantization.

9. PHASE64 RESULT: LAYOUT TRACE

Phase64 added default-off layout attribution in the llama.cpp fork: fa944bb5f feat(cuda): trace layout tensor names. The env gate is LLAMA_LAYOUT_TRACE=<n>. It traces CUDA GET_ROWS, CPY, CONT, DUP, and CONCAT runtime dispatch with tensor names, types, shapes, and contiguity flags.

DGX artifact: /home/mudler/bench/phase64_layout_trace/20260701_142519. Patched build gates stayed green: MoE md5 8cb0ce23, dense md5 5951a5b4, MUL_MAT 1146/1146, MUL_MAT_ID 806/806.

Trace result at MoE npp=512, ntg=4, npl=32:

  • get_rows: 7268
  • cpy: 2008
  • cont: 1734
  • concat: 990

The named layout sources are GDN conv-state gather/concat/update (cache_r_lN, conv_states_reshaped-N, qkv_mixed_transposed-N, conv_input-N, conv_state_update-N), MoE top-k fan-in gathers (ffn_moe_probs-N, ffn_moe_topk-N, ffn_moe_weights-N), and paged-attention mask/KV reshape/copy paths. This does not fund a clean layout optimization yet; it gives Phase65 the exact names needed to either remove one repeated chain or reject it with evidence.

10. PHASE65 RESULT: QUANT TRACE

Phase65 added default-off activation-quant route attribution in the llama.cpp fork: afc2c7030 feat(cuda): trace activation quant routes. The env gate is LLAMA_QUANT_TRACE=<n>. DGX mirror commit: 7863194bd.

DGX artifact: /home/mudler/bench/phase65_quant_trace/20260701_143729. Patched build gates stayed green: MoE md5 8cb0ce23, dense md5 5951a5b4, MUL_MAT 1146/1146, MUL_MAT_ID 806/806.

Trace result at MoE npp=512, ntg=4, npl=32:

  • mmq_dense: 4444
  • mmq_moe_dedup_unique: 2960
  • mmq_moe_gather: 2960
  • mmq_moe_flat: 1480

The dominant default-path shapes are MoE gate/up expert activation quant deduplication (K=2048, rows=512) followed by gather to expert-token rows (rows=4096), shared-expert dense gate/up quantization (K=2048, rows=512), MoE down expert flat quantization (K=512, rows=4096), and shared-expert down quantization (K=512, rows=512). This confirms the activation-quant bucket is concentrated in named MoE/shared-expert FFN paths, but it does not prove whether gather_mmq_fp4 is material or just a cheap cost of the existing dedup win. Phase66 should time quantize_mmq_nvfp4 versus gather_mmq_fp4 with nsys/NVTX before funding any behavior-changing source patch.

11. PHASE66 RESULT: QUANT KERNEL TIMING

Phase66 timed the Phase65 candidate kernels directly with Nsight Systems. Artifact: /home/mudler/bench/phase66_quant_kernel_timing/20260701_144256. Profile: quant_npp512.nsys-rep; summary: quant_npp512_kern_sum_cuda_gpu_kern_sum.csv.

Shape: MoE npp=512, ntg=4, npl=32. Total GPU kernel time: 7108388986 ns.

kernel time instances share
quantize_mmq_nvfp4 317205504 ns 8884 4.46%
gather_mmq_fp4 45374880 ns 2960 0.64%
combined 362580384 ns - 5.10%

Decision: reject a Phase66 gather/quant source patch. The gather is too small to target, and quantize plus gather is below the 8% source-funding threshold. Do not reopen W4A16/no-activation-quant from this evidence; that larger rewrite was already rejected in earlier phases.

12. PHASE67 RESULT: BF16 CUBLAS F32 OUTPUT

Phase67 added a default-off BF16 projection shortcut in the llama.cpp fork: ea0875d14 feat(cuda): gate BF16 cuBLAS F32 output. The env gate is LLAMA_BF16_CUBLAS_F32_OUT=1. DGX mirror commit: 14fd69f1e.

DGX artifact: /home/mudler/bench/phase67_bf16_f32_out/20260701_144909. Default and opt-in gates stayed green: MoE md5 8cb0ce23, dense md5 5951a5b4, MUL_MAT 1146/1146.

Same-window MoE prefill A/B:

npp default S_PP opt-in S_PP change
512 2347.41 2402.34 +2.34%
2048 2440.18 2456.54 +0.67%

The opt-in npp=512 profile removed the BF16-to-F32 conversion row: convert_unary<__nv_bfloat16, float> became 0 ns, 0 instances. Keep this as default-off for now. It is correctness-clean and measurable, but the win is small and needs dense plus serving A/B before any default-on decision.

13. PHASE68 RESULT: BF16 F32 OUTPUT DENSE + SERVING A/B

Phase68 reused Phase67 source unchanged. Plan: docs/superpowers/plans/2026-07-01-bf16-f32-output-dense-serving-phase68.md. DGX artifact: /home/mudler/bench/phase68_bf16_dense_serving/20260701_145710; serving A/B artifact: /home/mudler/bench/phase68_bf16_dense_serving/20260701_145710/serving_ab_20260701_150249.

Correctness basis for the exact source commit remains Phase67: default and LLAMA_BF16_CUBLAS_F32_OUT=1 both produced MoE md5 8cb0ce23, dense md5 5951a5b4, and MUL_MAT 1146/1146.

Dense prefill stayed positive but tiny:

npp default S_PP opt-in S_PP change
512 973.13 975.52 +0.25%
2048 1019.88 1021.39 +0.15%

MoE serving A/B at N=128, prompt 128, generation 128, --parallel 128:

metric default opt-in change
agg_tps 409.8 415.0 +1.27%
decode_agg_tps 615.3 627.2 +1.93%
prefill_tps 1630.2 1648.0 +1.09%
ttft_mean_ms 8574.7 8085.9 -5.70%
wall_s 39.978 39.480 -1.25%

Decision: carry the shortcut as a default-off opt-in candidate. It is no longer just a prefill-only win, but Phase68 is not enough to default it on. Any future default-on proposal must mirror the fork commit into the LocalAI patch series and rerun a broader current serving snapshot with pre/post md5 and op gates.

14. PHASE69 RESULT: PATCH SERIES MIRROR READINESS

Phase69 checked the patch-series state without pushing and without editing generated patch files. Plan: docs/superpowers/plans/2026-07-01-patch-series-mirror-readiness-phase69.md.

Current committed LocalAI patches still match the Phase37 fork tip:

base=0ed235ea2c17a19fc8238668653946721ed136fd
applied_tree=dedb1182910eafe9f6875588dc8285bfb544cce5
patch_tip_tree=dedb1182910eafe9f6875588dc8285bfb544cce5
fork_head_tree=fcf5720b659c5e1e2b487ccf3c8f7289bb12b9c4
match_patch_tip=yes
match_fork_head=no
patch_count=54

Dry-run export from 2d590d770..ea0875d14 produced ten additive source-only patches, projected as 0064..0073. Applying current 0001..0063 plus temp 0064..0073 onto the pin exactly reconstructed current fork HEAD:

applied_plus_missing_tree=fcf5720b659c5e1e2b487ccf3c8f7289bb12b9c4
fork_head_tree=fcf5720b659c5e1e2b487ccf3c8f7289bb12b9c4
match_fork_head=yes
current_patch_count=54
missing_patch_count=10
projected_patch_count=64

Projected patch tail:

  • 0064 serving admission trace (c6cb8460e)
  • 0065 admission histograms (bd7b2e952)
  • 0066..0068 TTFT prefill-first scheduler knobs (8a97629a4, 3b6ab5fa8, 8759213e3)
  • 0069..0070 W4A16 direct-activation policy/stub (41be3da5b, 7967ad47f)
  • 0071 layout trace (fa944bb5f)
  • 0072 quant trace (afc2c7030)
  • 0073 BF16 cuBLAS F32 output (ea0875d14)

Decision: mirror regeneration is technically ready but not executed. The local fork is 26 commits ahead of fork/localai-paged, and the fork-first policy requires pushing before regenerating the LocalAI series. Do not push without explicit approval. After approval, push the fork, regenerate 0064..0073, rerun the same tree-hash check, and then run the broader serving gates before any default-on BF16 policy change.

15. PHASE70 RESULT: BF16 F32 OUTPUT BROADER SERVING

Phase70 broadened the Phase68 serving evidence without source changes. Plan: docs/superpowers/plans/2026-07-01-bf16-f32-output-broader-serving-phase70.md. Benchmark ledger: backend/cpp/llama-cpp-localai-paged/docs/BENCHMARK.md. DGX artifact: /home/mudler/bench/phase70_bf16_broader_serving/20260701_151500.

Gates stayed green. Default pre/post gates matched MoE md5 8cb0ce23, dense md5 5951a5b4, MUL_MAT 1146/1146, and MUL_MAT_ID 806/806. Opt-in pre/post gates matched MoE md5 8cb0ce23, dense md5 5951a5b4, and MUL_MAT 1146/1146.

Serving shape: MoE NPL=8 32 128, prompt 128, generation 64, PARALLEL=128.

n opt/default agg opt/default decode opt/default TTFT default decode/vLLM opt decode/vLLM
8 0.8896 0.8998 1.1247 0.8100 0.7289
32 0.9912 0.9974 1.0320 0.6882 0.6864
128 1.0071 0.9882 0.9852 0.6921 0.6839

Decision: reject default-on for LLAMA_BF16_CUBLAS_F32_OUT=1. The shortcut is correctness-clean, but it materially regressed low-concurrency serving and slightly widened the vLLM decode gap at n=32 and n=128. Keep it default-off only and move the next parity effort to a different lever.

16. PHASE71 RESULT: GDN TENSOR-CORE REVALIDATION

Phase71 challenged the stale GDN planning docs before starting more source work. Plan: docs/superpowers/plans/2026-07-01-gdn-tc-revalidation-phase71.md. Benchmark ledger: backend/cpp/llama-cpp-localai-paged/docs/BENCHMARK.md. DGX artifact: /home/mudler/bench/phase71_gdn_tc_revalidation/20260701_153425.

Source under test stayed at DGX mirror commit 14fd69f1e feat(cuda): gate BF16 cuBLAS F32 output. No llama.cpp source was changed.

Canonical gates matched for all four GDN modes: MoE md5 8cb0ce23, dense md5 5951a5b4, and GATED_DELTA_NET 46/46. Default also passed MUL_MAT 1146/1146 and MUL_MAT_ID 806/806.

MoE prefill, PP=512,2048, TG=4, B=32, CTX=131072:

arm npp512 S_PP npp2048 S_PP
default 2313.57 2422.88
sequential-disabled (GDN_CHUNK_MIN=2147483647) 2198.28 2361.22
serial-chunked (GDN_TC=0 GDN_CHUNK_MIN=64) 1787.49 1699.77
forced M5 (GDN_TC=4 GDN_CHUNK_MIN=64) 2323.18 2420.52

Decision: keep shipped GDN M5 default behavior. It still beats sequential-disabled by +5.24%/+2.61%, beats serial-chunked by +29.43%/+42.54%, and forced M5 is within noise of the current default. Do not reopen smaller GDN C32/QS/global-Ai32/kernel-reorder work on GB10.

Post-Phase71 do-not-reopen list for GB10:

  • Smaller W4A16/MoE GEMM body, metadata, direct-activation, or quant/gather shortcuts.
  • GDN C32 slab, QS-early, Global-Ai32, or another low-conflict M5 reorder.
  • BF16 cuBLAS F32 output as a default-on policy.

The only GDN work that should be reconsidered is a larger FLA/CuteDSL-class blocked-solve implementation or a hardware pivot where the GB10 constraints no longer apply.

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