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[docs] Retune the Qwen3.8-27B RTX 5090 DFLASH2 cells against 1cf2b8c (#35786)

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This commit is contained in:
Jimmy Shong
2026-08-20 21:54:51 -07:00
committed by GitHub
parent 78c964d9d7
commit 3efa057449
2 changed files with 71 additions and 37 deletions
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docs/cookbook/autoregressive/Qwen/Qwen3.8-27B.mdx
+32 -24
View File
@@ -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
+39 -13
View File
@@ -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",