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83e2635
perf(agentx): refresh GB300 DSV4 with DSpark6
weireweire Aug 12, 2026
8a4a0a0
fix(agentx): keep DSpark synthetic AL out of eval
weireweire Aug 16, 2026
927f3d3
perf(b300): add DSpark6 Dynamo AgentX recipes
weireweire Aug 16, 2026
e9c00d8
docs(perf): register B300 DSpark6 AgentX sweep
weireweire Aug 16, 2026
e28fa42
fix(b300): make DSpark6 AgentX PR independent
weireweire Aug 16, 2026
1cbf9c3
perf(dsv4): calibrate DSpark6 acceptance to 3.77
weireweire Aug 16, 2026
4b038b9
fix(ci): place B300 AgentX entry at YAML top level
weireweire Aug 16, 2026
4324132
fix(ci): fit B300 DEP8 HiCache within host DRAM
weireweire Aug 16, 2026
2be8f91
ci: retrigger B300 AgentX sweep after HiCache fix
weireweire Aug 16, 2026
d39f736
Merge remote-tracking branch 'origin/main' into perf/dsv4-b300-dspark…
weireweire Aug 16, 2026
2366223
fix(agentx): emit one staged tokenizer argument
weireweire Aug 17, 2026
831b912
Preserve B300 AgentX affinity across long sweeps
weireweire Aug 17, 2026
abf9155
Match B300 DP-local prefill chunks to GB300
weireweire Aug 17, 2026
8d66f6c
perf: leave prefill activation headroom on B300 DEP8
weireweire Aug 17, 2026
3cf6a42
fix: use staged B300 DSpark checkpoint
weireweire Aug 17, 2026
b19ddad
cleanup: use native B300 DSpark model metadata
weireweire Aug 17, 2026
b62a50c
fix: retain mainline trust-remote-code setting
weireweire Aug 17, 2026
8042c25
fix(agentx): pin B300 reasoning effort high
weireweire Aug 17, 2026
8bdb32e
fix(agentx): align B300 tokenizer with 0813 model
weireweire Aug 17, 2026
1d3656e
Merge remote-tracking branch 'origin/main' into perf/dsv4-b300-dspark…
weireweire Aug 17, 2026
78c1bd6
Enable B300 DP prefill phase alignment
weireweire Aug 17, 2026
9da2042
fix(agentx): handle B300 overlength traces
weireweire Aug 17, 2026
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Original file line number Diff line number Diff line change
@@ -0,0 +1,137 @@
name: "agg-b300-dep4-mtp-kvoffload"

model:
path: "deepseek-v4-pro-0813"
container: "dynamo-sglang"
precision: "fp4"

identity:
model:
repo: "deepseek-ai/DeepSeek-V4-Pro-0813"
container:
image: "lmsysorg/sglang:nightly-dev-cu13-20260816-4a6dc267"

dynamo:
install: true
hash: "7a4e47ead90ec6b479e397996f6479ce7d251510"

slurm:
time_limit: "8:00:00"

health_check:
max_attempts: 1440
interval_seconds: 10

resources:
gpu_type: "b300"
gpus_per_node: 4
agg_nodes: 1
agg_workers: 1
gpus_per_agg: 4

infra:
etcd_nats_dedicated_node: true
nats_max_payload_mb: 32

frontend:
type: dynamo
nginx_session_affinity: true
nginx_session_affinity_header: X-Dynamo-Session-ID
enable_multiple_frontends: false
env:
PIP_BREAK_SYSTEM_PACKAGES: "1"
args:
router-mode: "kv"
router-session-affinity-ttl-secs: "14400"
active-decode-blocks-threshold: "None"
active-prefill-tokens-threshold: "None"
active-prefill-tokens-threshold-frac: "None"

backend:
type: sglang
# Let srt-slurm allocate and register the worker-local publisher endpoint;
# Dynamo needs discovery metadata in addition to the SGLang CLI flag.
kv_events_config:
aggregated: true
aggregated_environment:
PIP_BREAK_SYSTEM_PACKAGES: "1"
SGLANG_RAGGED_VERIFY_MODE: "static"
SGLANG_DEFAULT_THINKING: "1"

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🟡 WARNING: Missing SGLANG_DSV4_REASONING_EFFORT: high in aggregated_environment.

Why it matters: Every existing DSV4 AgentX recipe sets it in each worker env block (GB300 agg-gb300-tp4/tp8-mtp-lowlatency.yaml, all four disagg-gb300-*-mtp-kvoffload.yaml, and agg-h200-tp8-mtp-kvoffload.yaml). Without it the model runs at the default reasoning effort, which changes generation length/profile — so the B300 curve isn't comparable with the GB300/H200 DSV4 AgentX results this PR claims parity with, and the golden acceptance length 3.82 (measured at effort high) no longer matches what eval-only real-verify runs will observe.

Fix:

Suggested change
SGLANG_DEFAULT_THINKING: "1"
SGLANG_DEFAULT_THINKING: "1"
SGLANG_DSV4_REASONING_EFFORT: high

(Same fix needed in agg-b300-dep8-mtp-kvoffload.yaml line 60.)

SGLANG_DSV4_REASONING_EFFORT: high
SGLANG_ENABLE_UNIFIED_RADIX_TREE: "1"
SGLANG_OPT_UNIFIED_CACHE_FREE_OUT_OF_WINDOW_SLOTS: "1"
SGLANG_OPT_USE_DEEPGEMM_MEGA_MOE: "1"
SGLANG_OPT_DEEPGEMM_MEGA_MOE_USE_FP4_ACTS: "1"
SGLANG_OPT_DEEPGEMM_MEGA_MOE_USE_MXF4_KIND: "1"
SGLANG_OPT_DEEPGEMM_MEGA_MOE_NUM_MAX_TOKENS_PER_RANK: "9216"
SGLANG_OPT_USE_CUSTOM_ALL_REDUCE_V2: "0"
SGLANG_OPT_SWA_SPLIT_LEAF_ON_INSERT: "1"
SGLANG_OPT_USE_JIT_INDEXER_METADATA: "1"
SGLANG_OPT_USE_JIT_NORM: "1"
SGLANG_OPT_USE_TOPK_V2: "1"
sglang_config:
aggregated:
served-model-name: "deepseek-ai/DeepSeek-V4-Pro-0813"
trust-remote-code: true
enable-metrics: true
enable-cache-report: true
# The AgentX corpus contains a small number of traces longer than the
# runtime KV capacity. Match the existing native B300 launcher: truncate
# those requests instead of leaving Dynamo streams unresolved.
allow-auto-truncate: true
# DSpark draft weights raise the measured minimum viable fraction to
# 0.9258 on B300; use the same 0.94 fraction as the GB300 TP4 recipe.
mem-fraction-static: 0.94
swa-full-tokens-ratio: 0.02
chunked-prefill-size: 32768
max-prefill-tokens: 8192
max-running-requests: 128
cuda-graph-max-bs: 32
stream-interval: 20
incremental-streaming-output: true
watchdog-timeout: 1800
dp-size: 4
tp-size: 4
ep-size: 4
enable-dp-attention: true
enable-dp-lm-head: true
enable-prefill-delayer: true
prefill-delayer-max-delay-passes: 30
moe-dense-tp-size: 1
load-balance-method: total_tokens
moe-a2a-backend: megamoe
enable-hierarchical-cache: true
# DEP4's smaller device KV pool otherwise leaves only ~28 GB of host
# prefix cache per node; ratio 75 raises it to ~700 GB, matching the
# effective per-session capacity of the validated DEP8 configuration.
hicache-ratio: 75
hicache-write-policy: write_back
hicache-io-backend: direct
hicache-mem-layout: page_first_direct
weight-loader-drop-cache-after-load: true
speculative-algorithm: DSPARK
speculative-dspark-block-size: 6
speculative-num-steps: 1
speculative-eagle-topk: 1
speculative-num-draft-tokens: 7

sbatch_directives:
mem: "0"
cpus-per-task: "144"

srun_options:
mem: "0"
container-remap-root: ""

benchmark:
type: custom
command: bash /infmax-workspace/benchmarks/multi_node/agentic_srt.sh
env:
INFMAX_CONTAINER_WORKSPACE: /infmax-workspace
RESULT_DIR: /logs/agentic
PORT: "8000"
IS_MULTINODE: "false"
TP: "4"
AIPERF_HTTP_X_DYNAMO_SESSION_ID_FROM_CORRELATION_ID: "true"
AIPERF_USE_DYNAMO_CONV_AWARE_ROUTING: "0"
AIPERF_REQUIRED_SERVER_METRIC_PREFIX: "sglang:"
Original file line number Diff line number Diff line change
@@ -0,0 +1,136 @@
name: "agg-b300-dep8-mtp-kvoffload"

model:
path: "deepseek-v4-pro-0813"
container: "dynamo-sglang"
precision: "fp4"

identity:
model:
repo: "deepseek-ai/DeepSeek-V4-Pro-0813"
container:
image: "lmsysorg/sglang:nightly-dev-cu13-20260816-4a6dc267"

dynamo:
install: true
hash: "7a4e47ead90ec6b479e397996f6479ce7d251510"

slurm:
time_limit: "8:00:00"

health_check:
max_attempts: 1440
interval_seconds: 10

resources:
gpu_type: "b300"
gpus_per_node: 8
agg_nodes: 1
agg_workers: 1
gpus_per_agg: 8

infra:
etcd_nats_dedicated_node: true
nats_max_payload_mb: 32

frontend:
type: dynamo
nginx_session_affinity: true
nginx_session_affinity_header: X-Dynamo-Session-ID
enable_multiple_frontends: false
env:
PIP_BREAK_SYSTEM_PACKAGES: "1"
args:
router-mode: "kv"
router-session-affinity-ttl-secs: "14400"
active-decode-blocks-threshold: "None"
active-prefill-tokens-threshold: "None"
active-prefill-tokens-threshold-frac: "None"

backend:
type: sglang
# Let srt-slurm allocate and register the worker-local publisher endpoint;
# Dynamo needs discovery metadata in addition to the SGLang CLI flag.
kv_events_config:
aggregated: true
aggregated_environment:
PIP_BREAK_SYSTEM_PACKAGES: "1"
SGLANG_RAGGED_VERIFY_MODE: "static"
SGLANG_DEFAULT_THINKING: "1"

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🟡 WARNING: Same as DEP4 — missing SGLANG_DSV4_REASONING_EFFORT: high, which every other DSV4 AgentX recipe (GB300 agg/disagg, H200) sets in each worker env block. Affects comparability and the effort-high-derived golden AL 3.82.

Fix:

Suggested change
SGLANG_DEFAULT_THINKING: "1"
SGLANG_DEFAULT_THINKING: "1"
SGLANG_DSV4_REASONING_EFFORT: high

SGLANG_DSV4_REASONING_EFFORT: high
SGLANG_ENABLE_UNIFIED_RADIX_TREE: "1"
SGLANG_OPT_UNIFIED_CACHE_FREE_OUT_OF_WINDOW_SLOTS: "1"
SGLANG_OPT_USE_DEEPGEMM_MEGA_MOE: "1"
SGLANG_OPT_DEEPGEMM_MEGA_MOE_USE_FP4_ACTS: "1"
SGLANG_OPT_DEEPGEMM_MEGA_MOE_USE_MXF4_KIND: "1"
SGLANG_OPT_DEEPGEMM_MEGA_MOE_NUM_MAX_TOKENS_PER_RANK: "9216"
SGLANG_OPT_USE_CUSTOM_ALL_REDUCE_V2: "0"
SGLANG_OPT_SWA_SPLIT_LEAF_ON_INSERT: "1"
SGLANG_OPT_USE_JIT_INDEXER_METADATA: "1"
SGLANG_OPT_USE_JIT_NORM: "1"
SGLANG_OPT_USE_TOPK_V2: "1"
sglang_config:
aggregated:
served-model-name: "deepseek-ai/DeepSeek-V4-Pro-0813"
trust-remote-code: true
enable-metrics: true
enable-cache-report: true
# The AgentX corpus contains a small number of traces longer than the
# runtime KV capacity. Match the existing native B300 launcher: truncate
# those requests instead of leaving Dynamo streams unresolved.
allow-auto-truncate: true
# Leave activation headroom for 8K/rank chunked prefill; 0.90 OOMs
# under the c128 AgentX warmup while 0.85 completes the full run.
mem-fraction-static: 0.85
swa-full-tokens-ratio: 0.02
chunked-prefill-size: 65536
max-prefill-tokens: 8192
max-running-requests: 1152
cuda-graph-max-bs: 144
stream-interval: 20
incremental-streaming-output: true
watchdog-timeout: 1800
dp-size: 8
tp-size: 8
ep-size: 8
enable-dp-attention: true
enable-dp-lm-head: true
enable-prefill-delayer: true
prefill-delayer-max-delay-passes: 30
moe-dense-tp-size: 1
load-balance-method: total_tokens
moe-a2a-backend: megamoe
enable-hierarchical-cache: true
# Keep the explicit host KV pool within the x86 B300 node's 3 TB DRAM
# budget; ratio 2.0 OOMs during startup before the benchmark can run.
hicache-ratio: 1.5
hicache-write-policy: write_back
hicache-io-backend: direct
hicache-mem-layout: page_first_direct
weight-loader-drop-cache-after-load: true
speculative-algorithm: DSPARK
speculative-dspark-block-size: 6
speculative-num-steps: 1
speculative-eagle-topk: 1
speculative-num-draft-tokens: 7

sbatch_directives:
mem: "0"
cpus-per-task: "144"

srun_options:
mem: "0"
container-remap-root: ""

benchmark:
type: custom
command: bash /infmax-workspace/benchmarks/multi_node/agentic_srt.sh
env:
INFMAX_CONTAINER_WORKSPACE: /infmax-workspace
RESULT_DIR: /logs/agentic
PORT: "8000"
IS_MULTINODE: "false"
TP: "8"
AIPERF_HTTP_X_DYNAMO_SESSION_ID_FROM_CORRELATION_ID: "true"
AIPERF_USE_DYNAMO_CONV_AWARE_ROUTING: "0"
AIPERF_REQUIRED_SERVER_METRIC_PREFIX: "sglang:"
52 changes: 52 additions & 0 deletions configs/nvidia-master.yaml
Original file line number Diff line number Diff line change
Expand Up @@ -8705,6 +8705,7 @@ glm5.1-fp8-b200-tilert:
dp-attn: false
additional-settings:
- "DECODE_NODES=1"

- isl: 8192
osl: 1024
search-space:
Expand All @@ -8725,3 +8726,54 @@ glm5.1-fp8-b200-tilert:
dp-attn: false
additional-settings:
- "DECODE_NODES=1"

dsv4-fp4-b300-dynamo-sglang-agentic-mtp-agg:

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🔴 BLOCKING: This new config block is inserted in the middle of glm5.1-fp8-b200-tilert's fixed-seq-len scenario list, splitting it in two.

Why it matters: The tilert config's second point (- isl: 8192 / osl: 1024, now at lines 8609–8628) sits at the same 4-space indent as this config's agentic-coding: list items, so YAML re-attaches it as a third entry of dsv4-fp4-b300-dynamo-sglang-agentic-mtp-agg's agentic-coding scenario. Two concrete breakages:

  1. glm5.1-fp8-b200-tilert silently loses its 8k1k benchmark point from the matrix.
  2. The new B300 AgentX config gains a bogus fixed-seq-len-shaped entry carrying tilert settings (tp: 8, ep: 1, PREFILL_IMAGE=vllm/vllm-openai:v0.26.0, DECODE_NODES=1) and no CONFIG_FILE — when the sweep reaches it, launch_b300-nv.sh exits with Error: CONFIG_FILE is not set (or the matrix generator chokes on isl/osl inside agentic-coding first).

The file still parses as valid YAML, which is why the PR's YAML-parse validation didn't catch it.

Fix: Move the entire dsv4-fp4-b300-dynamo-sglang-agentic-mtp-agg block (lines 8558–8608) to the end of the file, after the glm5.1 config's final - "DECODE_NODES=1" (line 8628).

Fix this →

image: lmsysorg/sglang:nightly-dev-cu13-20260816-4a6dc267
model: deepseek-ai/DeepSeek-V4-Pro-0813
model-prefix: dsv4
runner: cluster:b300-nv
precision: fp4
framework: dynamo-sglang
router: { name: dynamo-router, version: "7a4e47ead90ec6b479e397996f6479ce7d251510" }
multinode: true
disagg: false
scenarios:
agentic-coding:
- dram-utilization: 0.95
search-space:
- spec-decoding: draft_model
conc-list: [48, 64]
kv-offloading: dram
kv-offload-backend: { name: hicache }
prefill:
num-worker: 1
tp: 4
ep: 4
dp-attn: true
additional-settings:
- "SYNTHETIC_ACCEPTANCE=true"
Comment on lines +8708 to +8754

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🔴 The new dsv4-fp4-b300-dynamo-sglang-agentic-mtp-agg block (configs/nvidia-master.yaml:8558-8583) is inserted mid-list inside glm5.1-fp8-b200-tilert's fixed-seq-len scenario, right before its pre-existing - isl: 8192 item. Since that item sits at the same 4-space indent as the new block's - dram-utilization: 0.95 item, YAML re-parents it into the new agentic-coding list — corrupting both configs: the new B300 AgentX sweep gains a bogus fixed-seq-len-style scenario carrying incompatible vllm PREFILL_IMAGE settings, and glm5.1-fp8-b200-tilert silently loses its isl:8192/osl:1024 sweep point. Fix by moving the new top-level key to after the complete glm5.1 entry (i.e. after - isl: 8192 ... DECODE_NODES=1).

Extended reasoning...

What the bug is: In configs/nvidia-master.yaml, the new top-level key dsv4-fp4-b300-dynamo-sglang-agentic-mtp-agg is inserted at line 8558, in the middle of the pre-existing glm5.1-fp8-b200-tilert entry. Specifically, it lands between the first and second items of glm5.1-fp8-b200-tilert.scenarios.fixed-seq-len, right before the pre-existing - isl: 8192 list item.

Why this corrupts the YAML structure: The new block's own scenario list item, - dram-utilization: 0.95, is indented at 4 spaces under agentic-coding:. The pre-existing - isl: 8192 item (glm5.1's second fixed-seq-len entry) happens to sit at that exact same 4-space indentation. Because YAML determines list membership purely from indentation of the - markers, the parser has no way to know - isl: 8192 was meant to continue the previous key's list — it reads as a second element of the new key's agentic-coding list instead.

Verified directly against the tree with yaml.safe_load:

dsv4 agentic-coding entries: 2
0 ['dram-utilization', 'search-space']
1 ['isl', 'osl', 'search-space']          # <- stolen from glm5.1
glm5.1 fixed-seq-len entries: 1
0 {'isl': 1024, 'osl': 1024}              # <- lost its isl:8192/osl:1024 entry

This confirms both halves of the corruption in the current working tree, not just in the diff context.

Concrete proof/walkthrough:

  1. Before this PR, glm5.1-fp8-b200-tilert.scenarios.fixed-seq-len had 2 entries: {isl:1024,osl:1024} and {isl:8192,osl:1024}.
  2. The PR inserts the new dsv4-fp4-b300-dynamo-sglang-agentic-mtp-agg key directly after glm5.1's first fixed-seq-len entry ends (... DECODE_NODES=1) and directly before the second entry's - isl: 8192 line.
  3. YAML parses top-to-bottom by indentation; once it sees the new top-level key dsv4-fp4-...:, it starts a fresh mapping. Its scenarios.agentic-coding list begins with - dram-utilization: 0.95 at 4-space indent.
  4. The next line at that same 4-space indent, - isl: 8192, is indistinguishable (structurally) from a second element of agentic-coding, so it — and everything nested under it through the eval/mtp/vllm-prefill scenario — becomes agentic-coding[1].
  5. Result: glm5.1-fp8-b200-tilert silently drops its 8k/1k benchmark point from the sweep, and the brand-new B300 dynamo-sglang AgentX config picks up a nonsensical second scenario entry carrying spec-decoding: mtp, PREFILL_IMAGE=vllm/vllm-openai:v0.26.0, and other vLLM-specific settings that don't apply to a dynamo-sglang aggregate config.

Why nothing else catches this: YAML syntax is fully valid (no parse error), so any lint/syntax check passes. The bug is purely structural/semantic — a silent mis-nesting — which won't be caught by tests unless something actually diffs scenario counts per top-level config before/after.

Impact: Two sweeps are broken: the glm5.1 config silently loses a benchmark data point (no error, just missing coverage), and the new B300 AgentX sweep would attempt to run a scenario with settings incompatible with its framework (dynamo-sglang vs. vllm PREFILL_IMAGE), likely causing that scenario to fail or produce meaningless results.

Fix: Move the new dsv4-fp4-b300-dynamo-sglang-agentic-mtp-agg: block to after the complete glm5.1-fp8-b200-tilert entry finishes (i.e., after its - isl: 8192 ... "DECODE_NODES=1" block), rather than splitting it apart.

- "SYNTHETIC_ACCEPTANCE_LENGTH=3.77"
- "CONFIG_FILE=recipes/sglang/deepseek-v4/agentic/agg-b300-dep4-mtp-kvoffload.yaml"
decode:
num-worker: 0
tp: 4
ep: 4
dp-attn: true
- spec-decoding: draft_model
conc-list: [128, 256, 384, 512, 576]
kv-offloading: dram
kv-offload-backend: { name: hicache }
prefill:
num-worker: 1
tp: 8
ep: 8
dp-attn: true
additional-settings:
- "SYNTHETIC_ACCEPTANCE=true"
- "SYNTHETIC_ACCEPTANCE_LENGTH=3.77"
- "CONFIG_FILE=recipes/sglang/deepseek-v4/agentic/agg-b300-dep8-mtp-kvoffload.yaml"
decode:
num-worker: 0
tp: 8
ep: 8
dp-attn: true
19 changes: 19 additions & 0 deletions golden_al_distribution/dsv4_dspark_probabilistic.yaml
Original file line number Diff line number Diff line change
@@ -0,0 +1,19 @@
deepseek-v4-pro-0813:
thinking_on:
1: 1.84
2: 2.51
3: 3.01
4: 3.36
5: 3.61
6: 3.77
7: 3.73
8: 3.47
thinking_off:
1: 1.94
2: 2.81
3: 3.58
4: 4.17
5: 4.74
6: 5.12
7: 5.24
8: 4.95
12 changes: 11 additions & 1 deletion perf-changelog.yaml
Original file line number Diff line number Diff line change
Expand Up @@ -6039,7 +6039,6 @@
- "EVAL_ONLY runs switch to real block verification by removing synthetic acceptance (TLLM_SPEC_DECODE_FORCE_NUM_ACCEPTED_TOKENS stripped from all agentx recipes before srtctl)."
- "Add SERVED_MODEL_NAME: Qwen3.5-397B-A17B-NVFP4-V2 to all 6 agentx recipe env blocks and make build_replay_cmd prefer SERVED_MODEL_NAME over $MODEL for the aiperf --model flag."
pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/2612

- config-keys:
- glm5.2-fp4-gb200-dynamo-sglang-agentic-agg
- glm5.2-fp4-gb200-dynamo-sglang-agentic-disagg
Expand All @@ -6049,3 +6048,14 @@
- "Add GB200 GLM-5.2 FP4 Dynamo-SGLang AgentX with EAGLE MTP, HiCache DRAM offload, and KV-aware correlation-ID affinity."
- "Measure the TP8 aggregate curve and tuned 1P1D TP4 HiCache c10/c12 disaggregated frontier with every logical SGLang metrics endpoint."
pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/2620

- config-keys:
- dsv4-fp4-b300-dynamo-sglang-agentic-mtp-agg
scenario-type:
- agentic-coding
description:
- "Add B300 Dynamo-SGLang DSpark6 AgentX aggregate DEP4 and DEP8 configurations with session-affinity routing, HiCache host offload, an 8K prefill window, and thinking-on block-size-6 golden synthetic acceptance length 3.77 for throughput; eval retains real verification."
- "Use the August 16 SGLang CUDA 13 nightly and srt-slurm v1.0.38, with DEP4 concurrency 48-64 and DEP8 concurrency 128-576."
- "Cap the DEP8 HiCache ratio at 1.5 so the explicit host KV pool fits within the B300 x86 node's 3 TB DRAM budget during model loading and JIT startup."
- "Use the official DeepSeek-V4-Pro-0813 model identity so AIPerf follows the standard --tokenizer $MODEL path and reuses the shared Hugging Face cache."
pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/2624
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