Cross-agent synthesis on top of the both-engine nsys decomposition (3b5957157):
settle the user's "can we do what vLLM does on MoE?" question with the three
converging investigations (groundtruth measurement + vllm-marlin source-read +
marlin-port feasibility).
Verdict: vLLM's ~15% MoE-decode lead is NOT the Marlin GEMM (that bucket is a
-1.7 ms llama WIN: native FP4-MMA W4A4 47.3 vs Marlin W4A16 50.0 at the ragged
tiny-M decode shape, both at the LPDDR5x BW floor). The gap is bf16
dense-projection bandwidth (+6.5), recurrence state-gather plumbing (+6.6, led
by k_get_rows 5.2), graph/stream-overlap overhead (~+7), W4A4 act-quant tax
(+3.3), and router/glue (+5.4).
A W4A16/Marlin grouped MoE GEMM is REJECTED (default and opt-in): it would
regress the 27% GEMM bucket to half-rate bf16 MMA, re-enter the GB10 occupancy
wall the dense scaffold already STOPPED at, and its entire intrinsic upside is
the ~2% act-quant tax - smaller than the bit-exact +1.9% the 0025 re-graph
already banked, and closeable bit-exactly by fusing the act-quant.
Recommended build (none a new MoE GEMM): (1) fuse the k_get_rows SSM-state
gather (bit-exact, ~+5, biggest single-kernel win); (2) extend CUDA-graph
coverage + stream overlap (bit-exact, ~+7); (3) fuse the W4A4 act-quant into
RMSNorm/SiLU (bit-exact, +3.3); (4) NVFP4-quantize the still-bf16 GDN/attn
projections + lm_head (bit-changing, +6.5, the same NVFP4-dense-quant move vLLM
makes). Bit-exact levers alone reach ~94% of vLLM; with the projection quant
~96-97%, parity-or-better physically in reach since both heaviest kernels
(SSM core, MoE GEMM) are already llama wins.
Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
llama.cpp patch series — paged attention (vLLM-parity engine)
A stacking series: each patch is a small, self-contained, independently-buildable step toward an
in-model paged-attention engine. They apply in numeric order on top of the pinned LLAMA_VERSION
(backend/cpp/llama-cpp/Makefile). The build applies them automatically after checkout (see the
llama.cpp: target). Keeping the work as ordered patches — rather than one big diff — is what lets us
rebase cleanly across llama.cpp bumps and avoid drift: when a patch stops applying, only that small
patch needs fixing, and the failure points at exactly which step the upstream change touched.
Base
LLAMA_VERSIONpin in../Makefile. All patches are generated against that exact commit. Bumping the pin = re-run the regen workflow below and fix only the patches that no longer apply.
The series (phases → patches)
| # | Patch | What | Verifies |
|---|---|---|---|
| 0001 | 0001-vendor-paged-kv-manager.patch |
Add src/paged-kv-manager.{h,cpp} (vLLM-parity block manager, CPU foundation) + CMake; no behavior change |
builds; unit-tested separately under ../paged/ |
| 0002 | 0002-paged-kv-storage.patch |
Shared block-pool KV tensor + set_rows-by-slot writes, behind LLAMA_KV_PAGED |
builds; write/gather round-trip |
| 0003 | 0003-paged-gather-read.patch |
build_attn_paged gather-read in llama-graph.cpp |
Gate 0: token-identical greedy gen, single + multi-seq |
| 0004 | 0004-paged-ondemand-alloc.patch |
On-demand block allocation via PagedKVManager | max concurrent seqs before OOM |
| 0005 | 0005-paged-continuous-batching.patch |
Block-granular admit/evict in the server slot path | tok/s vs concurrency, mixed-length |
| 0006 | 0006-paged-prefix-caching.patch |
Block-hash cross-request prefix dedup | TTFT + memory on shared prefixes |
Each row is a separate git commit on the dev branch (below), exported 1:1 as a patch. Default off
(LLAMA_KV_PAGED) until Gate 0 (0003) is green, so partial series never changes stock behavior.
Regen workflow (the anti-drift recipe)
# 1. check out the exact pin into a dev tree
git -C /tmp clone https://github.com/ggml-org/llama.cpp llama-dev && cd /tmp/llama-dev
git checkout <LLAMA_VERSION from ../Makefile>
git checkout -b paged
# 2. apply the current series (each becomes a commit), or develop the next patch
git am /path/to/backend/cpp/llama-cpp/patches/00*.patch # or `git apply` + commit per patch
# 3. iterate a phase as ONE commit, then export the whole series 1:1
git format-patch <LLAMA_VERSION>..paged -o /path/to/backend/cpp/llama-cpp/patches/ --zero-commit -N
# 4. on a pin bump: rebase `paged` onto the new pin; only conflicting patches need edits; re-export.
Build integration
../Makefile's llama.cpp: target runs, after git checkout -b build $(LLAMA_VERSION):
for p in $(CURRENT_MAKEFILE_DIR)/patches/0*.patch; do git apply --verbose "$p"; done
All variants (avx/avx2/avx512/cuda/…) copy the patched llama.cpp/ tree, so the series ships everywhere.
Status
- 0001 vendor manager — DONE. Applies clean to the pin; builds into
libllama. - 0002 block placement — DONE + VERIFIED. Built
llama-simpleat the pin; greedy generation is token-identical stock vsLLAMA_KV_PAGED=1(Qwen3-0.6B), paged branch confirmed firing. - 0003 gather-read — DONE + VERIFIED (Gate 0 green). Implemented in the additive form
(
ADDITIVE_DESIGN.md): all logic in newsrc/paged-attn.{h,cpp}(allm_graph_input_igather-index subclass + the K/V/mask gather), hooked by one line inbuild_attn+ two thin accessors onllama_kv_cache_context+ 1 CMake line (216 insertions; no edit tollm_graph_input_attn_kvorllama-graph.h). Greedy generation is token-identical stock vsLLAMA_KV_PAGED=1(Qwen3-0.6B, 9/9 across 3 prompts × {32,96,128} tokens), withn_gather=71 < n_kv=256confirming real compaction. Patch:0003-paged-gather-read-env-LLAMA_KV_PAGED.patch.- Key correctness finding:
get_gather_idxsmust emit cells sorted by token position. The CPU flash-attn online softmax reduces cells in physical-array order and is FP-order-sensitive, so 0002's scattered placement alone (full-window read, no gather) diverges from stock once a sequence crosses the first 16-cell block. The position-sorted gather reproduces stock's exact reduction order -> bit- identical, not merely mathematically equivalent. So 0002 is the placement substrate; 0003 is what makes paged placement token-identical under flash-attn.
- Key correctness finding:
- 0004–0006 follow.
Honest parity note (important)
This series delivers the paged-attention engine (capacity + scheduling + prefix sharing). It does not
by itself reach vLLM throughput parity, because the measured prefill bottleneck is the FP4 MoE GEMM kernel
(Lever 3: mul_mat_q<MXFP4> ~22 TFLOP/s, ~27× behind vLLM) — a per-token compute gap that paging does not
touch. Paged attention closes the concurrency/memory gap (more sequences, prefix reuse); the prefill/throughput
gap additionally needs the tcgen05/CUTLASS grouped-GEMM (deferred, upstream-grade, no shortcut — see
../paged/UPSTREAM_GGML_ISSUE.md and DGX_BLACKWELL_PLAN.md). So full vLLM parity = this series AND the
kernel; neither alone suffices.