[Bugfix][Kernel] Bump DeepGEMM pin for SM120 32-state page support (DeepGEMM#14)

[xzwgit]

Purpose

Bump the pinned DeepGEMM revision (both cmake/external_projects/deepgemm.cmake and tools/install_deepgemm.sh, which are documented to be kept in sync) from e1f418c to 6901431, so the build picks up the SM120 32-state page support from vllm-project/DeepGEMM#14.

Why

DeepSeek-V4.1's sparse attention mixes compression ratios 1 and 2. With the 64-token kernel block required on SM120, ratio-2 layers yield 32-state indexer pages. The currently pinned DeepGEMM rejects that in the SM120 FP8 paged-MQA path:

Assertion error (.../deepgemm-src/csrc/apis/attention.hpp:484):
(arch_major == 10 and (block_kv == 32 or block_kv == 64 or ...))

DeepGEMM#14 (merged 2026-09-22 into the fork's dev branch) permits block_kv in {32, 64} for SM120 at the API and FP8 launcher boundaries. Without it, DeepSeek-V4.1-Flash cannot serve on any SM120 device regardless of the vLLM-side geometry fix (see the three-piece dependency chain in #59203).

Test evidence (8x RTX PRO 6000, SM120, TP8)

With this bump (plus FlashInfer built from main for its own SM120 page-32 kernels, and the vLLM-side geometry from #57292), DeepSeek-V4.1-Flash serves correct output on 8x RTX PRO 6000:

$ curl .../v1/chat/completions -d '{"model":"deepseek-v4.1-flash",
    "messages":[{"role":"user","content":"What is 17*19? Answer with just the number."}],
    "max_tokens":100,"chat_template_kwargs":{"thinking":false}}'
'323'

vllm bench serve, random dataset + --ignore-eos (official tool), all tiers zero failures:

Input/Output	c	Output tput (tok/s)	TTFT (s)	TPOT (ms)
1K/1K	1	90.0	0.16	11.0
1K/1K	4	301.6	0.19	13.1
4K/4K	1	88.1	1.95	10.9
4K/4K	4	300.0	0.32	13.3
8K/8K	1	91.3	0.28	10.9
8K/8K	4	299.5	0.41	13.3

(Same node with #56509's geometry instead of #57292's produced all-NaN logits — the DeepGEMM page-32 support is necessary for the #57292 geometry.)

Note on the jump

6901431 is the fork's dev head. It contains, on top of the current pin: #10 (SM120 device layer vendor), #14 (page_kv=32), #17 (NVFP4 MegaMoE), #19 (per-token FP32 scales). If maintainers prefer minimal jumps, 17ca18f7 (just #14) is the smallest change that unblocks SM120 — but 6901431 is the revision we built and validated end-to-end.

Cross-refs: #59203 (issue), #57292 (vLLM-side geometry PR).

Environment details and full tier table: #59203 (comment)

[claude]

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[xzwgit]

Thank you for the maintainer guidelines.

On the failing pre-run-check: this appears to be the first-PR guard (new contributor with no ready/verified label and fewer than 4 merged PRs), not a content issue. DCO and Summary have both passed. Could a maintainer add the ready label (or otherwise authorize a CI run) when convenient? Happy to run /ci run afterwards.

For reviewers' context, this is the last missing piece of the SM120 enablement chain described in #59203:

• vLLM-side geometry: [Bugfix][DSv4.1] Use 64-token sparse-MLA pages on SM120 #57292 (separate PR)
• FlashInfer: SM120 page-32 kernels (main branch, [Frontend] Customizable RoPE theta #5197)
• DeepGEMM: this bump — without it, the SM120 FP8 paged-MQA path asserts on block_kv == 32, so even with the [Bugfix][DSv4.1] Use 64-token sparse-MLA pages on SM120 #57292 geometry a stock vLLM build cannot serve DeepSeek-V4.1-Flash on SM120 devices

We built and validated exactly this revision (6901431) end-to-end on 8x RTX PRO 6000: correct output, 99.0% GSM8K (100q), and ~90 tok/s single-stream / 300 tok/s at c4. Full environment inventory and per-tier tables are in #59203.

Note the diff is intentionally minimal (2 files, +5/-3); both pinned locations are updated together as their comments require.

Thanks!

[xzwgit]

Adding the multimodal (vision) validation for the same pinned revision. Since this pin is one link in the chain that makes SM120 work end-to-end, it is worth recording that it does not break the vision path either.

Same setup as above (8x RTX PRO 6000, SM120, TP8, vLLM 0.30.1rc1.dev382, FlashInfer 0.7.1 built from main, DeepGEMM 6901431), with the server started without --language-model-only so the DeepseekV4ViT tower + aligner are loaded. No extra patching is needed for the vision path: the tower's attention goes through the generic MMEncoderAttention wrapper, and the encoder runner comes up normally (encoder_runner.py: Encoder cache will be initialized with a budget of 8192 tokens).

Objective correctness - 10/10

Images are generated programmatically and the expected answers are known exactly, so this is mechanically checked rather than judged:

case	what the image contains	asked	expected	got
ocr_code	"The access code is 8135"	access code	8135	8135
ocr_order_id	"Order ID: A7X-2291 Total: 486 yuan"	order id	A7X-2291	A7X-2291
small_text	"Serial number 90517-B" (30 px font)	serial number	90517-B	90517-B
img_math	"128 - 49 = ?"	result	79	79
count_red	8 circles, mixed red/blue	count only red	5	5
table_cell	3x3 numeric table	row 2, column 3	66	66
two_images	"42" and "17" as two images in one request	larger number	42	42
bar_max	4-bar chart with printed values	max value	45	45
solid_color	uniform red field	colour	red	red
text_control	no image	17*19	323	323

Positive control that images are really encoded (not silently dropped): prompt_tokens is 228-438 with an image versus 43 for the text-only request.

Official benchmark - vllm bench serve --dataset-name random-mm

1 image per request (--random-mm-base-items-per-request 1), 1024 text input tokens / 1024 output tokens, buckets {(512,512,1):1.0} and {(1024,1024,1):1.0}. All 9 tiers completed with 0 failed requests.

tier	images/req	image tok/req	Successful	Total in tok	Total out tok	Output tput (tok/s)	TTFT mean (ms)	TTFT P95 (ms)	TPOT mean (ms)	TPOT P95 (ms)
mm512-c1	1	215	1	1239	1024	87.1	155.9	155.9	11.34	11.34
mm512-c2	1	215	2	2478	2048	171.6	264.2	315.8	11.40	11.44
mm512-c4	1	215	4	4956	4096	278.9	591.4	913.0	13.75	14.08
mm1024-c1	1	683	1	1707	1024	85.2	436.7	436.7	11.32	11.32
mm1024-c2	1	683	2	3414	2048	167.3	532.5	592.3	11.43	11.48
mm1024-c4	1	683	4	6828	4096	268.3	1188.7	1370.7	13.72	13.87
mm2img-c1	2	401	1	1425	1024	86.3	283.1	283.1	11.32	11.32
mm2img-c2	2	401	2	2850	2048	170.7	329.9	383.6	11.40	11.45
mm2img-c4	2	1103	4	8508	4096	270.1	1112.1	1497.3	13.52	13.74

Observations within this dataset:

• Decode is essentially untouched by images: TPOT stays ~11.3 ms at c1 whether the prompt carries 215 or 683 image tokens; the image cost is paid once, in prefill/TTFT.
• TTFT tracks the image token count: 156 ms at 215 image tokens vs 437 ms at 683 (single stream). Under 4-way concurrency the encoder plus the longer prefill push mean TTFT to ~0.6-1.2 s.
• A 512x512 image expands to ~215 tokens and 1024x1024 to ~683 (vision_config.max_image_tokens is 1024 for this checkpoint).

Full environment inventory, NIC/perf context and the profiler traces are in #59203. Vision + MTP/DSpark is not tested here (the source notes the draft heads are unsupported for the vision variant, and this run has speculative_config=None).

[xzwgit]

Cross-architecture check on the pinned revision, for the record.

We now have SM100 hardware available, so we built against exactly this pin on 8x NVIDIA B300 SXM6 (SM100, 275 GB/GPU) and served DeepSeek-V4.1-Flash with TP8 and the recipe's Blackwell configuration - the settings that are rejected on SM120 (indexer_kv_dtype=mxfp4, indexer_sparse_logits=true) plus the B300 capture/seq limits.

Build: vLLM 0.30.1rc1.dev412 + deep_gemm 2.8.0+6901431 (the revision this PR pins).

DeepGEMM is exercised and healthy on SM100 - the startup log shows the DeepGEMM path in use:

Using 'DEEPGEMM_MXFP4' Mxfp4 MoE backend.
DeepGEMM PDL enabled on deep_gemm.
DeepGEMM E8M0 enabled on current platform.
Available KV cache memory: 186.04 GiB

Serving is correct: 17 * 19 -> 323, /v1/models reports max_model_len 1048576.

Throughput (vllm bench serve, random dataset, --ignore-eos, 1024 in / 1024 out, shape warmed up first):

tier	Successful	Failed	Output tok/s	TTFT mean (ms)	TPOT mean (ms)
1K x c1	1	0	146.6	68.3	6.76
1K x c4	4	0	566.9	125.1	6.93

We also ran the same model on 8x RTX PRO 6000 (SM120) with this pin earlier (see #59203): correct output, GSM8K 99/100, and the pinned revision is what makes the SM120 page-32 path work at all. So the bump is validated on both SM120 and SM100 - no regression on the non-SM120 side.

Two notes for reviewers that came out of this, in case they are useful:

• Impact scope: the official vllm/vllm-openai:nightly image does not ship DeepGEMM at all (import deep_gemm -> ModuleNotFoundError), so this pin only affects source builds. The pip wheel and Docker paths are untouched.
• In our source-built SM100 environment we had to select --kernel-config '{"moe_backend":"deep_gemm"}' explicitly: the wheel-resolved FlashInfer's flashinfer_trtllm MoE JIT fails to compile on this box (duplicate CommonUtils.h definitions between its batched_gemm and fused_moe_trtllm_sm100 source trees, ninja: build stopped). That is a FlashInfer packaging issue, unrelated to this pin - mentioning it only so the numbers above are reproducible.

Raw data, startup scripts and bench scripts for both boxes: https://github.com/xzwgit/llm-test/tree/master/deepseek-v4.1-flash-tp8-8xb300