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feat(recipes): add DeepSeek-V4-Pro-0813 vLLM recipes and Fern docs (H200 + GB200) - #13783
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WalkthroughAdded DeepSeek-V4-Pro-0813 documentation, shared model-cache manifests, aggregated and disaggregated H200 vLLM deployments, and an AIPerf Mooncake trace benchmark Job. ChangesDeepSeek-V4-Pro-0813 recipes
Estimated code review effort: 4 (Complex) | ~45 minutes Merge Risk: 🟡 Moderate · up to The H200 aggregate manifests do not constrain workers to H200 nodes, and the disaggregated manifests rely on a default that can select aggregated mode instead of explicitly declaring prefill and decode. Those settings can prevent the intended deployment from starting or cause it to run with the wrong topology, so the PR is not merge-ready until they are corrected or explicitly accepted. 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
Full details: Docstring CoverageExplanation No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff. Analyzed 0 functions across 0 files. (9 skipped: 9 unsupported.) Full details: Title checkExplanation The title clearly identifies the main change: DeepSeek-V4-Pro-0813 vLLM recipes and documentation for H200. The GB200 reference is broader than the included changes, but it remains related to the documented branch scope. Full details: Description checkExplanation The description includes the overview, detailed changes, reviewer starting points, related-issues confirmation, test results, configurations, metrics, and caveats. It is complete and directly related to the pull request. ✨ Finishing Touches 💡 1🛠️ Fix failing CI checks 💡
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In `@recipes/deepseek-v4/deepseek-v4-pro-0813/perf/README.md`:
- Around line 53-55: Label the fenced code block containing the E2E tok/s/user
formula as a text block by changing its opening fence to use the text language
identifier, while leaving the formula unchanged.
In `@recipes/deepseek-v4/deepseek-v4-pro-0813/README.md`:
- Around line 11-19: Update the root README directory layout to include the
existing perf/ directory and its Mooncake benchmark resources alongside
model-cache/ and vllm/, accurately reflecting perf/perf.yaml and perf/README.md.
- Line 10: Update the fenced code block in the README to specify the text
language on its opening fence, preserving the existing directory listing
content.
- Around line 45-47: Pin the documented revision
72e1d3230f6c080a530b0a1d46f8eb4602340597 in the download job and all four
deployment manifests, passing it consistently to the download and model-loading
commands or using one shared revision source.
In `@recipes/deepseek-v4/deepseek-v4-pro-0813/vllm/agg-h200-agentic/deploy.yaml`:
- Line 43: Update both VllmDecodeWorker podTemplate.spec.nodeSelector sections
in
recipes/deepseek-v4/deepseek-v4-pro-0813/vllm/agg-h200-agentic/deploy.yaml:43-43
and recipes/deepseek-v4/deepseek-v4-pro-0813/vllm/agg-h200-1m/deploy.yaml:43-43
to require nvidia.com/gpu.product: NVIDIA-H200, ensuring both aggregate workers
are scheduled only on H200 nodes.
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recipes/deepseek-v4/deepseek-v4-pro-0813/README.mdrecipes/deepseek-v4/deepseek-v4-pro-0813/model-cache/model-cache.yamlrecipes/deepseek-v4/deepseek-v4-pro-0813/model-cache/model-download.yamlrecipes/deepseek-v4/deepseek-v4-pro-0813/perf/README.mdrecipes/deepseek-v4/deepseek-v4-pro-0813/perf/perf.yamlrecipes/deepseek-v4/deepseek-v4-pro-0813/vllm/agg-h200-1m/deploy.yamlrecipes/deepseek-v4/deepseek-v4-pro-0813/vllm/agg-h200-agentic/deploy.yamlrecipes/deepseek-v4/deepseek-v4-pro-0813/vllm/disagg-h200-1m/deploy.yamlrecipes/deepseek-v4/deepseek-v4-pro-0813/vllm/disagg-h200-agentic/deploy.yaml
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…prefill Summary: - Pin the validated vLLM 0.28.0 image on both GB200 targets. On 0.27.1 this model loses accuracy: GPQA pass@1 82.07 aggregated / 75.88 disaggregated, versus 89.52 and 85.35 on 0.28.0 (nemo-evaluator ns_gpqa, pass@1 avg-of-4, 792 requests). Raw upstream 0.27.1 scores 81.94 with no recipe at all, so the loss is the vLLM version rather than this recipe. - Give VllmPrefill the same speculative config as VllmDecode. vLLM 0.28.0 added a NIXL compatibility-hash handshake whose hash covers the speculative config. With DSpark on decode only the hashes differ, no KV transfer succeeds, and the engine still answers HTTP 200 while emitting unusable output. Prefill does not run a draft model; this is a workaround to report upstream. - Make perf.yaml runnable on GB200. It had no arm64 nodeSelector or tolerations, no compiler for AIPerf's crick (no aarch64 wheel), and a 2 h deadline against runs that take up to 15,471 s. The nodeSelector ships commented because an unconditional arm64 selector would leave the H200 variants Pending. - Rename dsv4-0813-disagg-gb200-agentic to dsv4-pro-0813-disagg-gb200-agentic so all four targets share one naming scheme. - Refresh the GB200 performance numbers and correct the GB200 trace path. The disaggregated operating point moves from c=12 to c=10: on 0.28.0 c=12 measures 49.89 tok/s/user, below the recipe's own >= 50 gate, while c=10 gives 53.42. Validation: - Both GB200 recipes deployed and benchmarked end to end on 8x and 16x GB200 over the full 3,541-request Mooncake trace, cold cache, 3,526 ok / 15 errors. Aggregated c=8: 69.45 system tok/s/GPU, 51.85 tok/s/user, TTFT p50 403 ms. Disaggregated c=10: 50.58 system tok/s/GPU, 53.42 tok/s/user, TTFT p50 1,237 ms. Both clear the joint gate (>= 50 tok/s/user and TTFT p50 < 5 s). - GPQA measured on the same two deployments with the harness named above. - All changed YAML parses. H200 targets, numbers, and docs are untouched. Signed-off-by: Shubham <shugandhi@nvidia.com>
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…pin the GB200 image Two fixes for nvbug 6707747. Frontend now runs `nvcr.io/nvidia/ai-dynamo/dynamo-frontend` instead of the full vLLM runtime image. The Frontend container only runs `python3 -m dynamo.frontend` -- it needs no vLLM and no CUDA -- but it was pulling a 9.3 GB runtime image onto a CPU node. QA hit ephemeral-storage eviction three times across two nodes during that pull. dynamo-frontend is built for CPU nodes. Same fix as PR ai-dynamo#14229 for glm-5.3-flash (nvbug 6712108), and matches what qwen3-0.6b and the qwen3-32b cloud-provider recipes already do. Tags track each SKU's Dynamo line: H200 workers are 1.4.0 so the frontend is dynamo-frontend:1.4.0; GB200 workers are on the 1.5.0 line so the frontend is dynamo-frontend:1.4.2, the newest published tag. There is no dynamo-frontend:1.5.0 on NGC -- 1.3.0, 1.4.0, 1.4.1 and 1.4.2 exist, and a 1.5.0 reference in-repo is a test fixture, not a published artifact. The GB200 worker image is now digest-pinned: dynamoci.azurecr.io/ai-dynamo/dynamo:1.5.0-ci-f9e5a1b4...-vllm-runtime @sha256:428a72d7d9c4e90267c931ac9e35ef8eb3949b7299a246f2e54a98ce7bfba200 That is the multi-arch index digest, not the arm64 manifest, so the recipe stays resolvable on both architectures. This closes the "unpinned" half of QA's Issue 1; the registry half stays open until a vLLM 0.28.0 image is published to NGC, since the released vllm-runtime:1.5.0 carries 0.27.1 -- the version the README footnote records as having an accuracy regression on this checkpoint. Validation: both images pulled and both Frontends reached Running with zero restarts -- H200 dynamo-frontend:1.4.0 on nebius-2, GB200 dynamo-frontend:1.4.2 plus the digest-pinned worker on gcp-dev-02. The GB200 worker could not be taken to a live model in that namespace: its model-cache holds only the NVFP4 variant, and the shared-model-cache copy of the base checkpoint is incomplete (32 of 67 safetensors, 15 .incomplete blobs, no tokenizer.json).
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…ross all variants GB200 was on dynamo-frontend:1.4.2 and H200 on 1.4.0, for no reason beyond 1.4.2 being the newest published tag. Both are 1.4.x against their respective workers, so the version skew argument does not favour either: GB200's worker is on the 1.5.0 line, and 1.4.0 versus 1.4.2 does not change that. One version across all four variants is easier to reason about and to bump later. Validation: 1.4.0 is the tag that passed the end-to-end smoke test on H200 (3/3 coherent responses through the frontend, finish_reason=stop). On GB200 the pull and startup were verified with 1.4.2; the frontend image is architecture-independent and 1.4.0 pulls from the same repository, but the exact 1.4.0-on-GB200 combination has not been run.
… the job deadline Two defects found while verifying nvbug 6707747. perf/README.md documented H200 aggregated and disaggregated at concurrency 6. Both ship at 4 -- that is the operating point published in the recipe README and the docs catalog, established by the completed trace runs. Concurrency 6 was measured and misses the SLA gate (43.5 tok/s/user aggregated), so anyone following perf/README reproduced a failing configuration and would reasonably conclude the recipe does not meet its own numbers. This is the same defect class as nvbug 6682090: the README documenting a value the results do not use. activeDeadlineSeconds was 28800 (8 h), which is shorter than every H200 trace run we have completed: aggregated c4 50,337 s = 14.0 h disaggregated c4 41,206 s = 11.4 h disaggregated c5 37,715 s = 10.5 h As shipped, the Job is terminated mid-run on H200 and the user gets a partial result -- exactly how the earlier 93%-partial dataset was produced. Raised to 86400 (24 h), about 1.7x the worst measured run, which leaves room for the 17% run-to-run variance measured on this model. The two `kubectl wait --timeout` values in perf/README.md are raised to match, so the documented wait does not expire before the Job it is waiting on. REQUEST_TIMEOUT_SECONDS raised 1200 -> 3600 to match the value the published runs actually used. The longest single request observed was 832 s, so 1200 would not have failed, but it left only 1.4x headroom on a workload whose input lengths reach 1.04M tokens.
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dynamo-performance-codeowners review
Strong recipe set overall — trace-validated operating points on all four targets, honest H200/GB200 KV-capacity narrative, digest-pinned H200 runtime images, and the dynamo-frontend split are all good perf hygiene. The joint SLA definition (E2E >= 50 tok/s/user and TTFT p50 < 5 s) in the recipe README is the right gate for this workload.
Requesting one fix before merge: the default CONCURRENCY in perf/perf.yaml is still 6 even though the published H200 trace operating point is c=4 and c=6 misses the user-throughput gate. Commit 9f65be4d2dc779ca8d51852cb7517eecbc1e62ec fixed the variant table in perf/README.md but not the shipped Job default or the tunables table — anyone running kubectl apply -f perf.yaml without editing env will reproduce a failing configuration. Please align perf.yaml, the tunables default, and the stale C=2 comment to 4.
Non-blocking nits (inline): catalog/Fern SLA text should mention the TTFT half of the joint gate; README still references CPU offloading though these recipes ship without a KV offload tier; perf.yaml header comment still lists removed *-1m variants.
Once the concurrency default is aligned, I'm good to approve from the performance side. The GB200 CI image vs NGC release gap is understood as a follow-up.
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Additional actionable findings on the current PR head.
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…dey-nv and biswapanda Blocking fix (ynpandey-nv): the shipped `CONCURRENCY` default in perf.yaml was still 6 while the published H200 operating point is 4. At 6, agg-h200 measures 43.5 tok/s/user, below the >= 50 gate -- so `kubectl apply -f perf.yaml` reproduced a failing configuration out of the box. The earlier commit fixed the variant table but not the Job default or the tunables table; both are now 4, along with a stale "C=2" comment and a header still listing the removed `*-1m` variants. Docs bug (biswapanda): `docs/fern/index.yml` had `slug: deepseek-v4-pro` on the new 0813 entry, which both removed that slug from the existing DeepSeek-V4-Pro page and pointed /recipes/deepseek-v4-pro at the 0813 content. Restored the original slug and gave 0813 its own. Other review items: - catalog `sla` stated only the throughput half of the gate. It now reads `user output >= 50 tok/s and TTFT p50 < 5 s` on all four targets, so catalog consumers do not optimize on decode-only throughput. - README credited the traces to "KV-aware routing and CPU offloading", but no shipped config has a KV offload tier. Reworded to routing and prefix reuse. - perf/README claimed the Job runs a warmup and listed a `warmup/` artifact directory; perf.yaml goes straight from readiness to the measured run. Both claims removed rather than adding a warmup, since the published numbers are cold-cache runs and adding one would invalidate them. - pvc-helper slept 3600 s but H200 trace runs take 11-14 h, so it exited before the artifacts it is meant to copy existed. Raised to 86400 s to match the Job deadline, added a readiness wait before the first exec, and documented recreating it if it has already gone. - perf.yaml installs aiperf on `python:3.12-slim`; aiperf 0.10.0 pulls `crick`, which has no arm64 wheel and needs a compiler. Added `build-essential` so the GB200/arm64 path does not fail during pip install. - GB200 wait commands used 3600 s while the GB200 manifests allow 21600 s for engine ready and observed loads exceed 2.5 h. Raised the waits and made the load-time statement target-specific. - Smoke test used `max_tokens: 256` and printed `.message.content`, which the same page warns can be null on a reasoning model -- a healthy deployment could look broken. Now 2048 tokens with `.content // .reasoning_content`. - perf/README documented a symlink target with an extra `deepseek-v4/` segment that resolves nowhere; corrected to match the committed symlink. - Registered the recipe in `recipes/README.md` and `recipes/deepseek-v4/README.md`. Adding the rows alone was not enough: the family README still described a two-model family and globbed `deepseek-ai/DeepSeek-V4-*` before stating "H200 Pro is capped at max_model_len=86,016", which is wrong for 0813 (its H200 variants run at 1,048,576). Added a carve-out noting 0813 is a separate checkpoint rather than a revision, that the two must not share a model cache, and that the cap does not apply to it. - disagg-gb200 header comment now states plainly that the `networking.gke.io/*` annotations and resource requests are one provider's worked example, naming the interface and network values a reader must replace. Comment only -- the config is unchanged, since converting to the Kustomize provider-networking pattern mid-release would change the artifact QA is testing.
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…recipes # Conflicts: # .lycheeignore # docs/fern/pages/recipes/model-recipes/overview.mdx
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…pshot migration Recipe Check fails on this branch with: kustomize-matrix: Generated Kustomize OpenAPI schema is stale: recipes/kustomize/components/dynamo-openapi/dynamo-openapi.json The cause is not in this PR. `scripts/generate_kustomize_openapi.py` derives the schema from `deploy/operator/config/crd/bases/*.yaml`, and ai-dynamo#14076 ("migrate checkpoints to standalone Snapshot") removed the DynamoCheckpoint CRD without regenerating the checked-in output. The stale copy still carried `nvidia.com.v1alpha1.DynamoCheckpoint` definitions for a CRD that no longer exists. Regenerated with the documented command rather than hand-edited, per the generated-artifact rule in AGENTS.md. The result drops the 352 lines of DynamoCheckpoint definitions and leaves the ten definitions backed by the six CRDs still on disk. `python3 scripts/kustomize-matrix.py check` now exits 0. This surfaced here because merging main into this branch brought the CRD deletion in while the branch's CI runs the check.
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… image Replace the internal dynamoci.azurecr.io CI image with the public nvcr.io/nvidia/ai-dynamo/vllm-runtime:1.5.0-deepseek-v4-pro-0813-dev.1 tag across the three GB200 workers (agg decode, disagg prefill, disagg decode) and the two Fern catalog entries. External users cannot pull from the ACR registry, so the GB200 variants were not reproducible outside NVIDIA CI. Note: the digest pin is dropped with the tag change. Re-pin to the multi-arch index digest before release, since -dev.N tags are mutable. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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Overview
Adds Dynamo + vLLM serving recipes for the
deepseek-ai/DeepSeek-V4-Pro-0813checkpoint on H200.GB200 recipes for this checkpoint are a separate contribution from a teammate and will land on
this same branch; the README already has GB200 columns marked TBD.
Details
Four profiles: aggregated and disaggregated, each for the agentic (64K) and 1M-context workloads.
Agentic workload is 64K ISL / 400 OSL / 90% prefix reuse. Gate is E2E ≥ 50 tok/s/user AND
TTFT p50 < 5 s, jointly, where
E2E = OSL / (TTFT + OSL × ITL). The 1M profiles are a batchcapability, not interactive.
Accuracy —
gpqa_diamond, 198 questions x 4 repeats (792 requests/run, 792/792 successful):reasoning_effortagg-h200-agenticdisagg-h200-agenticagg-h200-agenticmaxAggregated and disaggregated agree (90.53 vs 90.66) — the NIXL KV path between prefill and
decode does not affect output quality. Worth confirming, since neither throughput benchmarks nor
needle-in-haystack would detect subtle corruption there.
Three findings that change how this model should be evaluated:
max_new_tokensdominates sampling. Truncated responses scored 0 correct in every run —truncation is a total loss, not partial. Excluding them: 91.25 / 91.46 / 93.85.
reasoning_effort=maxmore than doubles reasoning length (5,114 → 11,941 tokens) and doesproduce better answers (93.85 when allowed to finish), but at a 65,536 cap it loses the gain to
truncation, so as-scored pass@1 is unchanged. Ceiling is 83,455.
reasoning_effortmust be a top-level request field. Thechat_template_kwargsform isaccepted but inert.
--kv-cache-dtype fp8is required, not a tuning choice — vLLM assertsDeepseekV4 fp8_ds_mla layout only supports fp8 kv-cache. With MXFP4 experts in the checkpoint,serving precision is fixed by the model.
Sampling parameters made no measurable difference (model card recommends temp 1.0 / top_p 1.0;
0.6 / 0.95 gave 90.66 vs 90.53 — within noise).
Against the published 92.8 (Artificial Analysis, pass@1) we measure ~90.6, i.e. −2.2 pp.
Four hypotheses were tested and eliminated — token cap, sampling params, reasoning effort, and KV
quantization (untestable: fp8 is mandatory). The remaining candidate is harness-level (AA uses
5 repeats + regex extraction; this used 4 repeats +
mcq-4choices), which cannot be testedwithout their harness. Reported as unexplained rather than papered over.
Engine choices, each A/B'd rather than inherited:
cudagraph_mode: FULL_AND_PIECEWISEinstead ofFULL_DECODE_ONLY: −11% TTFT, +4.8% tok/s/GPU.The sibling
deepseek-v4-proH200 recipe still carriesFULL_DECODE_ONLY, which appears neverto have been A/B'd — possibly a free win there too, though I have not measured it on that
checkpoint.
--moe-backend marlinretained;autois a wash and cancels the cudagraph gain when combined.SimpleCPUOffloadConnector; on disagg the NIXL slot is taken, so the offloadtier is stacked via
MultiConnectoron the decode worker only.disagg-h200-1mprefill uses mnbt 4096 + gpu-mu 0.97 — at mnbt 16384 it cannot start (24.06 GiBKV needed for one 1M request vs 20.75 available). That check is GPU-KV only.
Where should the reviewer start?
recipes/deepseek-v4/deepseek-v4-pro-0813/README.md, then the fourvllm/*/deploy.yaml.Related Issues
Caveats
disagg-h200-agenticoperating point is C≈6.4, from N=1. An earlier figure of C≈7.95 /24.84 tok/s/GPU was published and is retracted: it was measured before
--disaggregation-modewas known to be missing, so those workers may not have been trulydisaggregated. Re-measured on a verified 1P1D deployment (flags confirmed in
/proc/1/cmdline, prefill/decode throughput split confirmed, NIXL KV transfers observed),the crossing is C≈6.36 → 19.88 tok/s/GPU. Run-to-run variance on this setup is 17%, so
repeat before quoting precisely.
baseline (C=2 · 56.40 · 11.85 tok/s/GPU) came from a different cluster and understates this
one by ~20%; on the same nodes the recipe clears the gate at C=4 (52.98, 25.00 tok/s/GPU).
Also N=1.
disagg-h200-1mmay not needMultiConnector. GPU KV is 2.7–3.1 M tokens, far above the1.03 M served, so the host tier was loaded but almost certainly never spilled. A NIXL-only A/B
should decide whether to drop it.
this checkpoint. The figures above came from AIPerf's synthetic agentic profile, which replays
a different length distribution and is not comparable to the trace numbers other recipes publish
in that table.
sglang/variants.Test plan
All four configs deployed and exercised on 8×/16× H200 (
vllm-runtime:1.4.0, vLLM 0.26.0):AIPerf sweeps at the agentic profile, needle-in-haystack ladders for the 1M configs (lengths read
from the server's
usage.prompt_tokens, not client-side estimates), and the two accuracy gates.360/360 and 300/300 benchmark requests valid with zero errors; 792/792 and 2,164/2,164 accuracy
requests successful.
The image digest is taken from the
imageIDof the pods that produced these numbers, so it isprovably the benchmarked image.