[docs] Retune the Qwen3.8-27B RTX 5090 DFLASH2 cells against 1cf2b8c

docs/cookbook/autoregressive/Qwen/Qwen3.8-27B.mdx

@@ -22,7 +22,11 @@ pip install uv
 uv pip install sglang
 
 # For the DFLASH2 cells only — DFlash2 selector support is newer than the
-# latest release, so install from source (Method 2) instead of the line above.
+# latest release, so build from the commit those cells were validated on
+# instead of the line above:
+# git clone https://github.com/sgl-project/sglang.git && cd sglang
+# git checkout 1cf2b8c54d81802abc15dcf23a29b9cc687bc01e   # PR #35496
+# uv pip install -e "python[all]"
 ```
 
 Then run the **Python** output of the command panel below in that environment.
@@ -34,9 +38,11 @@ Then run the **Python** output of the command panel below in that environment.
 ```bash Command
 docker pull lmsysorg/sglang:qwen38-27b
 
-# For the DFLASH2 cells only — that tag predates DFlash2 selector support,
-# so pull a nightly built from main instead:
-# docker pull lmsysorg/sglang:dev
+# For the DFLASH2 cells only — that tag predates DFlash2 selector support.
+# Build the image from the commit those cells were validated on instead:
+# git clone https://github.com/sgl-project/sglang.git && cd sglang
+# git checkout 1cf2b8c54d81802abc15dcf23a29b9cc687bc01e   # PR #35496
+# docker build -t sglang:dflash2 -f docker/Dockerfile .
 ```
 
 For how to launch the image, see [Install → Method 3: Using Docker](../../../docs/get-started/install#method-3-using-docker). Substitute the inner `sglang serve ...` with what the command generator below produces.
@@ -46,14 +52,16 @@ For how to launch the image, see [Install → Method 3: Using Docker](../../../d
 </Tabs>
 
 <Warning>
-**DFLASH2 needs a build that tracks main.** DFlash2 landed in
-[#35371](https://github.com/sgl-project/sglang/pull/35371), and DFlash2 + NVFP4
-— the quantized `lm_head` path — in
-[#35496](https://github.com/sgl-project/sglang/pull/35496). Both are newer than
-the pinned `lmsysorg/sglang:qwen38-27b` tag and than the latest PyPI release; a
-build without #35496 fails on the NVFP4 cells at boot with `requires a dense
-FP16/BF16/FP32 target lm_head`. Every other recipe on this page — no
-speculation, MTP, DSpark — runs on the pinned tag as written.
+**DFLASH2 needs a build from `1cf2b8c` (PR
+[#35496](https://github.com/sgl-project/sglang/pull/35496)) or newer.** DFlash2
+landed in [#35371](https://github.com/sgl-project/sglang/pull/35371) and its
+quantized-`lm_head` path — what the NVFP4 cells need — in #35496, both newer
+than the pinned `lmsysorg/sglang:qwen38-27b` tag and than the latest PyPI
+release. A build without #35496 fails on the NVFP4 cells at boot with `requires
+a dense FP16/BF16/FP32 target lm_head`. The DFLASH2 pins on this page were
+measured on `1cf2b8c` exactly, which is why both install paths above check that
+commit out rather than tracking a moving branch. Every other recipe on this
+page — no speculation, MTP, DSpark — runs on the pinned tag as written.
 </Warning>
 
 </Accordion>
@@ -269,18 +277,18 @@ checkpoint's calibration scales automatically.
   RTX PRO 6000 BF16/FP8 cells boot and serve; on H200, DGX Spark and GB300
   those cells carry the **Final Verification In Progress**
   badge. The
-  RTX PRO 6000 recipe needs no changes. On the 32GB RTX 5090 prefer
-  `--mamba-ssm-dtype bfloat16` at `--mem-fraction-static 0.90`: measured
-  strictly better than float32 for this draft (6.1 vs 8.3 ms TPOT, accept
-  3.30 vs 3.09) — the opposite of the EAGLE trade, so measure before assuming.
-  float32 still fits, but only at `--mem-fraction-static 0.945` with
-  `--mamba-full-memory-ratio 10` pinned in place of the balanced value: the
-  L = 9216 ratio leaves the fp32 state pool one slot short at every
-  serviceable mem-fraction (0.94 allocates four of Low-Latency's five slots;
-  0.95 OOMs at runtime), and the re-weighted split leaves the Low-Latency KV
-  pool a single-request envelope (~9.4k tokens) — no headroom for longer
-  requests or radix reuse. The panel's DFLASH2 option applies these re-pins
-  automatically.
+  RTX PRO 6000 recipe needs no changes. On the 32GB RTX 5090 the pins differ per
+  state dtype, and the panel applies them automatically. bfloat16 serves at
+  `--mem-fraction-static 0.88` on the balanced ratio (0.90, DSpark's pin, OOMs
+  on the first request). float32 reaches only the High-Throughput tier, at
+  `--mem-fraction-static 0.895` with `--mamba-full-memory-ratio 10` overriding
+  the balanced value — these cells pin `--max-running-requests 1`, so the
+  balanced ratio provisions KV for concurrency the recipe never uses and
+  starves the state pool of the slots fp32 needs. The Low-Latency tier is
+  greyed out for float32: it needs five fp32 slots plus a full request's KV,
+  and no mem-fraction holds both — buying the fifth slot cuts KV to 7,752
+  tokens against the 9,216 one 8192/1024 request needs, while every
+  mem-fraction with a large enough pool dies in prefill graph capture.
 - **Hardware fit**: FP8 weights ~28.5GB (not serviceable beyond bs≤2 on
   32GB cards); NVFP4 weights ~16.5GB (recommended for RTX 5090-class GPUs).
 - `--mamba-radix-cache-strategy extra_buffer_lazy` lowers the state cost per

docs/src/snippets/configs/Qwen/qwen3.8-27b.jsx

@@ -128,10 +128,8 @@ export const config = {
           disabled: (sel) => sel.hw === "rtx5090" && sel.quant !== "nvfp4",
           disableReason:
             "On the 32GB RTX 5090 the DFlash2 draft model only fits on top of the NVFP4 weights",
-          // 5090: mem-fraction re-pins like DSPARK's, and fp32 additionally
-          // re-pins the ratio — the balanced L=9216 value leaves the state
-          // pool one slot short at every serviceable mem-fraction (see the
-          // DFlash2 bullet in Configuration Tips).
+          // fp32 is the one case that needs the balanced ratio overridden, so
+          // that family is stripped too and re-emitted below.
           stripPrefixes: (sel) =>
             sel.hw === "rtx5090"
               ? sel.ssmDtype === "float32"
@@ -142,15 +140,19 @@ export const config = {
             "--speculative-algorithm DFLASH",
             "--speculative-draft-model-path incoai/Qwen3.8-27B-DFlash2",
             "--speculative-num-draft-tokens 8",
-            // Measured on the 5090: bf16 state serves at 0.90 (DSPARK's
-            // pin); fp32 fits only at 0.945 + ratio 10 (0.94 is one state
-            // slot short, 0.95 OOMs at runtime) and leaves the Low-Latency
-            // KV pool a single-request envelope.
+            // Measured on the 5090 at commit 1cf2b8c, the build the Install
+            // accordion pins for this pick. bf16 serves at 0.88 on the balanced
+            // ratio (0.90, DSPARK's pin, OOMs on the first request). fp32 needs
+            // 0.895 AND the balanced ratio overridden to 10: these cells pin
+            // --max-running-requests 1, so the balanced value provisions KV for
+            // concurrency this recipe never uses, starving the state pool of the
+            // slots fp32 needs. Only High-Throughput reaches fp32 (S=4); the SSM
+            // dtype row greys fp32 out for Low-Latency (S=5).
             ...(sel.hw === "rtx5090"
               ? sel.ssmDtype === "float32"
-                ? ["--mem-fraction-static 0.945",
+                ? ["--mem-fraction-static 0.895",
                    "--mamba-full-memory-ratio 10"]
-                : ["--mem-fraction-static 0.90"]
+                : ["--mem-fraction-static 0.88"]
               : []),
           ],
         },
@@ -186,9 +188,33 @@ export const config = {
       title: "Mamba SSM Dtype",
       default: "float32",
       options: [
-        // Open on every platform, including with DSPARK on the 5090 (serves
-        // at mem-fraction 0.92 with the engine-default 2048 prefill chunk).
-        { id: "float32", label: "float32", flags: ["--mamba-ssm-dtype float32"] },
+        // Open on every platform except the 32GB RTX 5090 under DFLASH2:
+        // there the fp32 state pool and the prefill CUDA-graph capture cannot
+        // both fit, at any mem-fraction. Measured on main (2026-08-21, ratio
+        // pinned at 10 so slots are not the binding term): 0.945 and 0.92 OOM
+        // inside `Capture target prefill CUDA graph`, 0.90 OOMs on the first
+        // request, and 0.88 / 0.86 / 0.84 size the state pool below the 5 (LL)
+        // / 4 (HT) slots one request needs. bf16 state halves the pool and
+        // serves, and is the faster cell there anyway.
+        {
+          id: "float32", label: "float32",
+          // Only the Low-Latency tier is out of reach: it needs S=5 fp32 slots
+          // (735MB) plus >=9216 KV tokens for one request, and no mem-fraction
+          // holds both -- at 0.8975/r14 the pool buys the 5th slot but KV falls
+          // to 7752 tokens and generation stops after one token, while every
+          // mem-fraction with a big enough pool (>=0.90) dies in graph capture.
+          // High-Throughput needs one slot fewer and does fit; see the DFLASH2
+          // option's pins.
+          disabled: (sel) =>
+            sel.hw === "rtx5090" &&
+            sel.spec === "dflash" &&
+            sel.tier === "low-latency",
+          disableReason:
+            "On the 32GB RTX 5090 the Low-Latency tier cannot hold five fp32 state " +
+            "slots and a full request's KV at once — use bfloat16, or the " +
+            "High-Throughput tier which fits fp32",
+          flags: ["--mamba-ssm-dtype float32"],
+        },
         {
           id: "bfloat16", label: "bfloat16",
           disabled: (sel) => sel.hw === "rtx5090" && sel.quant !== "nvfp4",