diff --git a/backend/cpp/llama-cpp/paged/BLACKWELL_KERNEL_GAPS.md b/backend/cpp/llama-cpp/paged/BLACKWELL_KERNEL_GAPS.md
index 9fb414900..34d4d4657 100644
--- a/backend/cpp/llama-cpp/paged/BLACKWELL_KERNEL_GAPS.md
+++ b/backend/cpp/llama-cpp/paged/BLACKWELL_KERNEL_GAPS.md
@@ -101,3 +101,5 @@ GB10 peaks (measured): forums.developer.nvidia.com/t/351993, /360142, /373618. M
 arxiv 2408.11743, developers.redhat.com Marlin/Machete. MMQ untuned: llama.cpp docs/build.md, discussions/16578,
 DandinPower/llama.cpp_bench. FP4 landing/sm121: llama.cpp PR #17906/#20644, issues #19662/#18331. Roofline:
 vllm.ai/blog/2026-06-01-vllm-dgx-spark, lmsys.org DGX Spark.
+
+> **Correction (measured):** the earlier `GGML_CUDA_FORCE_CUBLAS` env test was a no-op because it's a *compile-time* `#ifdef`, not a runtime flag — cuBLAS never engaged. A real rebuild with `-DGGML_CUDA_FORCE_CUBLAS=ON` shows cuBLAS is **slower** than MMQ for dense Q4 (pp2048 690 vs 750) and runs an **Ampere `cutlass_80_tensorop` FP16 kernel** — cuBLAS-13.0 has no sm_121-tuned GEMM and falls back to sm_80. So *both* MMQ and cuBLAS sit at ~46 TFLOP/s (~21% of the 213 BF16 peak); there is **no library shortcut** to the ceiling on GB10 — a hand-tuned sm_120a kernel (Marlin-style) is required.
diff --git a/backend/cpp/llama-cpp/paged/W4A16_MARLIN_KERNEL_PLAN.md b/backend/cpp/llama-cpp/paged/W4A16_MARLIN_KERNEL_PLAN.md
new file mode 100644
index 000000000..3bcf6f44e
--- /dev/null
+++ b/backend/cpp/llama-cpp/paged/W4A16_MARLIN_KERNEL_PLAN.md
@@ -0,0 +1,61 @@
+# W4A16 Marlin-style GEMM for ggml-cuda on Blackwell (sm_120/121) — implementation plan
+
+The committed multi-week kernel. Goal: get 4-bit-weight dense matmul to the GB10 **BF16 ceiling (~213
+TFLOP/s ≈ ~3,300 t/s prefill on Qwen3-32B)**, ~4.3× over today's 765. This is the *match-vLLM* path; vLLM's
+own GB10 dense throughput runs on W4A16 Marlin (its FP4 path is broken on sm_121).
+
+## Why a custom kernel (validated, not assumed)
+
+On GB10 (sm_121), measured: **both** llama-MMQ (int8, Ampere-tuned) **and** cuBLAS-FP16 sit at ~46 TFLOP/s
+(~21% of peak). cuBLAS falls back to an Ampere `cutlass_80_tensorop` kernel (CUDA-13 has no sm_121 GEMM for
+these shapes); rebuilt with `-DGGML_CUDA_FORCE_CUBLAS=ON` it's *slower* than MMQ (690 vs 750). **No library
+path reaches the ceiling on consumer Blackwell** — a hand-tuned sm_120a kernel is required. `mmapeak` measures
+the 213 BF16 peak as reachable, and vLLM's Marlin hits it, so the ceiling is real; the work is reaching it.
+
+## What Marlin does (the design we mirror)
+
+Weights stored 4-bit, **dequantized in-register/shared-mem** in-flight; GEMM math on **FP16/BF16 tensor
+cores** (`mma.sync m16n8k16`). Speed comes from: `cp.async` global→shared with a **multi-stage double-buffered
+pipeline**, **offline weight reshuffle** into the MMA-friendly layout, activations kept resident in registers,
+and **Stream-K** partitioning. Sources: IST-DASLab/marlin, arXiv 2408.11743, vLLM machete (Hopper successor).
+
+## Phases (each ends with: numerical parity vs MMQ + a prefill benchmark)
+
+### P0 — Harness + baseline (do first)
+- Add a `test-backend-ops` MUL_MAT case for Q4_K/Q4_0 at prefill shapes (M=512/2048) — gives a numerical
+  reference and a microbench. Confirm baseline ~46 TFLOP/s.
+- Model-level gate: token-identical greedy generation (Qwen3) before/after, like the paged Gate 0.
+- Deliverable: a red/green parity check the kernel must pass at every phase.
+
+### P1 — Dispatch seam (no behavior change)
+- New `ggml/src/ggml-cuda/marlin-w4a16.cu` + a gated hook in `ggml_cuda_mul_mat` (dense, non-ids path),
+  behind `GGML_CUDA_W4A16` + sm_120/121 + type∈{Q4_0,Q4_K}. Initially returns false → falls back to MMQ.
+  (Mirror of the `fp4-grouped-moe.cu` scaffold seam.) Builds byte-identical by default.
+
+### P2 — Correctness-first kernel (slow OK)
+- Dequant Q4→BF16 (reuse ggml's `dequantize_block_q4_K`) into shared mem, naive `mma.sync m16n8k16` BF16
+  accumulate, small tiles. Goal: **bit-parity vs MMQ** (within fp tol) on the toy + the real model. Establishes
+  the data plumbing + the harness pass. Not expected to beat MMQ yet.
+
+### P3 — The Marlin pipeline (the speedup)
+- `cp.async` double/triple-buffered global→shared; offline weight reshuffle (a one-time repack of the Q4
+  tensor into the mma+pipeline layout — likely a load-time transform or a new tensor variant); register-
+  resident activation tiles; Stream-K split for the prefill M. Target: ≥150 TFLOP/s (≥~2,300 t/s), then ~213.
+
+### P4 — Tune
+- Tile (mmq_x/y analogues), warps, pipeline depth, occupancy. We have nsys (throughput) but **not ncu** on the
+  DGX — tuning is empirical (sweep configs, measure t/s). Note ncu would need sudo/driver perms we lack.
+
+### P5 — Enable
+- Default on for sm_120/121 + Q4_0/Q4_K dense when parity holds + faster; keep the flag as an escape hatch.
+  Ship as a LocalAI llama.cpp patch (the patches/ series) and/or upstream (ggml has no Marlin-equivalent —
+  issue #1519 — so it's net-new upstream value; float it with maintainers first).
+
+## Risks / notes
+- **Multi-week, expert-CUDA, DGX-only** (GB10 is the only sm_121). The session's network flakiness +
+  `llama-cli` hang make `llama-bench`/`test-backend-ops` the reliable verification tools (both work).
+- Quantization correctness: Q4_K's superblock structure (256-elem, 6-bit scales) is more complex to dequant
+  in-kernel than Q4_0; consider landing Q4_0 first, then Q4_K.
+- **Beat-path follow-on:** the FP4-MMA path (`mul_mat_q<MXFP4>`, ~5% of FP4 peak) tuned/fixed on sm_121 reaches
+  ~6,600 (2× BF16). Separate track; this W4A16 kernel is the match-path foundation.
+- Reuse ggml's `mma.cuh` tile abstractions (MMQ already uses them) rather than raw PTX where possible.