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484c8a5
feat(qwen3.5-gb300-agentx): add Qwen3.5-NVFP4 GB300 SGLang AgentX ben…
xinli-sw Jul 15, 2026
b078ddf
Merge remote-tracking branch 'origin/main' into nv-qwen35-agentx-gb300
csahithi Jul 15, 2026
f0f925f
qwen3.5 agentic: image 20260715-50d1edaa; prefill NEXTN MTP; fix agg …
csahithi Jul 15, 2026
028f2ff
qwen3.5 agentic: bump image to 20260716-b0b2dfbd
csahithi Jul 20, 2026
c808170
Merge remote-tracking branch 'origin/main' into nv-qwen35-agentx-gb300
csahithi Jul 20, 2026
f1e6bca
runners: add gb300-nv hardware metadata (available-cpu-dram-mib, gpus…
csahithi Jul 20, 2026
a46700e
qwen3.5 agentic: bump image to 20260724-433429b1
csahithi Jul 24, 2026
3dc16dc
Merge remote-tracking branch 'origin/main' into nv-qwen35-agentx-gb300
csahithi Jul 24, 2026
32289c3
run-sweep: drop unneeded agentic collect-results gate
csahithi Jul 24, 2026
75473fe
qwen3.5 agentic: pin Dynamo to ai-dynamo/dynamo#12081 (read-only Serv…
csahithi Jul 27, 2026
a258569
Merge remote-tracking branch 'origin/main' into nv-qwen35-agentx-gb300
csahithi Jul 27, 2026
5c871a2
qwen3.5 agentic: migrate disagg recipes to X-Dynamo-Session-ID sessio…
csahithi Jul 27, 2026
90d2947
Merge remote-tracking branch 'origin/main' into nv-qwen35-agentx-gb300
csahithi Jul 27, 2026
b547248
benchmark_lib: honor AIPERF_HTTP_X_DYNAMO_SESSION_ID_FROM_CORRELATION_ID
csahithi Jul 27, 2026
3f5d74e
qwen3.5 c128: raise SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK 25…
csahithi Jul 28, 2026
220701e
Merge remote-tracking branch 'origin/main' into nv-qwen35-agentx-gb300
csahithi Jul 28, 2026
64729c6
Merge remote-tracking branch 'origin/main' into pr-2121-reuse-74060
Ankur-singh Jul 30, 2026
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6 changes: 5 additions & 1 deletion benchmarks/benchmark_lib.sh
Original file line number Diff line number Diff line change
Expand Up @@ -1810,7 +1810,11 @@ build_replay_cmd() {
# aiperf's conv-aware routing emits nvext.session_control, a removed POC field
# (dynamo #9920 / v1.3.0-dev) that current dynamo builds reject with a 400
# (they moved to router/routing_constraints/agent_context). Default stays on.
if [[ "${FRAMEWORK:-}" == dynamo-* && "${AIPERF_USE_DYNAMO_CONV_AWARE_ROUTING:-1}" != "0" ]]; then
# New recipes instead set AIPERF_HTTP_X_DYNAMO_SESSION_ID_FROM_CORRELATION_ID=true
# to route by X-Dynamo-Session-ID header, which needs no routing CLI flag.
if [[ "${FRAMEWORK:-}" == dynamo-* \
&& "${AIPERF_USE_DYNAMO_CONV_AWARE_ROUTING:-1}" != "0" \
&& "${AIPERF_HTTP_X_DYNAMO_SESSION_ID_FROM_CORRELATION_ID:-false}" != "true" ]]; then
REPLAY_CMD+=" --use-dynamo-conv-aware-routing"
# The upstream 300s affinity TTL is shorter than an overloaded
# high-concurrency agentic request. Keep bindings alive across long
Expand Down
Original file line number Diff line number Diff line change
@@ -0,0 +1,114 @@
name: "agg-gb300-tp4-c1-mtp-hicache-jid2191933"

# Agentic-coding SGLang aggregated recipe for Qwen3.5-397B-A17B-NVFP4 on GB300
# (single aggregated worker, TP4, NEXTN MTP + hierarchical cache).
#
# Ported from the manually-run srtctl base:/zip_override_conc: starter to the
# flat single-variant agentic schema. Pure sglang (no dynamo): frontend.type
# sglang serves directly. Concurrency comes from the GHA matrix (exported as
# CONC into agentic_srt.sh), not the recipe. Serving tuning preserved verbatim
# from the source run; only CI scaffolding is added.

model:
path: "qwen3.5-fp4"
container: "dynamo-sglang"
precision: "fp4"

slurm:
time_limit: "8:00:00"

health_check:
max_attempts: 1440
interval_seconds: 10

resources:
gpu_type: "gb300"
gpus_per_node: 4
agg_nodes: 1
agg_workers: 1
gpus_per_agg: 4

infra:
nats_max_payload_mb: 8

frontend:
type: sglang

backend:
type: sglang
aggregated_environment:
SGLANG_SIMULATE_ACC_LEN: '3.39'
SGLANG_SIMULATE_ACC_METHOD: match-expected
SGLANG_SIMULATE_ACC_TOKEN_MODE: "real-draft-token"
TORCH_CUDA_ARCH_LIST: '10.0'
PYTHONNOUSERSITE: '1'
NCCL_NVLS_ENABLE: '1'
SGL_ENABLE_JIT_DEEPGEMM: 'false'
SGLANG_ENABLE_FLASHINFER_GEMM: 'true'
sglang_config:
aggregated:
served-model-name: nvidia/Qwen3.5-397B-A17B-NVFP4
model-path: /model/
trust-remote-code: true
tensor-parallel-size: 4
data-parallel-size: 1
expert-parallel-size: 1
enable-symm-mem: true
quantization: modelopt_fp4
kv-cache-dtype: fp8_e4m3
mamba-ssm-dtype: bfloat16
mamba-scheduler-strategy: extra_buffer
mamba-track-interval: 8192
attention-backend: trtllm_mha
moe-runner-backend: flashinfer_trtllm
speculative-algorithm: NEXTN
speculative-num-steps: 3
speculative-eagle-topk: 1
speculative-num-draft-tokens: 4

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started about discussion about AgentX MTP +viz @kedarpotdar-nv

cuda-graph-max-bs: 256
max-running-requests: 1024
max-prefill-tokens: 16384
chunked-prefill-size: 16384
mem-fraction-static: 0.8
max-mamba-cache-size: 1024
allow-auto-truncate: true
stream-interval: 50
scheduler-recv-interval: 10
tokenizer-worker-num: 6
page-size: 64
enable-hierarchical-cache: true
hicache-ratio: 1.01
hicache-io-backend: kernel
hicache-mem-layout: page_first_direct
hicache-write-policy: write_through

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

srun_options:
mem: "0"
# gb300-nv: pyxis maps the calling user into the container as a non-root uid;
# agentic_srt.sh's apt-get install git step needs EUID 0. Remap to uid 0 inside
# the container. srt-slurm renders empty-string values as flag-only srun args.
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"
# Aggregated: one worker serves both prefill and decode, so GPU accounting is
# the single-worker form num_gpus = TP (not the disagg prefill+decode sum).
# Force the single-node post-proc path; TP must match sglang_config tp-size.
IS_MULTINODE: "false"
TP: "4"
# Match the source run's dataset (256k cc-traces-with-subagents variant).
WEKA_LOADER_OVERRIDE: semianalysis_cc_traces_weka_062126_256k
# Container-side path of the aiperf mmap dataset cache; host-side mount wired
# via launch_gb300-nv.sh srtslurm.yaml default_mounts.
AIPERF_DATASET_MMAP_CACHE_DIR: "/aiperf_mmap_cache"
# Persistent HF hub cache (also via default_mounts).
HF_HUB_CACHE: "/hf_hub_cache"
Original file line number Diff line number Diff line change
@@ -0,0 +1,114 @@
name: "agg-gb300-tp4-c96-mtp-hicache-jid2195211"

# Agentic-coding SGLang aggregated recipe for Qwen3.5-397B-A17B-NVFP4 on GB300
# (single aggregated worker, TP4, NEXTN MTP + hierarchical cache).
#
# Ported from the manually-run srtctl base:/zip_override_conc: starter to the
# flat single-variant agentic schema. Pure sglang (no dynamo): frontend.type
# sglang serves directly. Concurrency comes from the GHA matrix (exported as
# CONC into agentic_srt.sh), not the recipe. Serving tuning preserved verbatim
# from the source run; only CI scaffolding is added.

model:
path: "qwen3.5-fp4"
container: "dynamo-sglang"
precision: "fp4"

slurm:
time_limit: "8:00:00"

health_check:
max_attempts: 1440
interval_seconds: 10

resources:
gpu_type: "gb300"
gpus_per_node: 4
agg_nodes: 1
agg_workers: 1
gpus_per_agg: 4

infra:
nats_max_payload_mb: 8

frontend:
type: sglang

backend:
type: sglang
aggregated_environment:
SGLANG_SIMULATE_ACC_LEN: '3.39'
SGLANG_SIMULATE_ACC_METHOD: match-expected
SGLANG_SIMULATE_ACC_TOKEN_MODE: "real-draft-token"
TORCH_CUDA_ARCH_LIST: '10.0'
PYTHONNOUSERSITE: '1'
NCCL_NVLS_ENABLE: '1'
SGL_ENABLE_JIT_DEEPGEMM: 'false'
SGLANG_ENABLE_FLASHINFER_GEMM: 'true'
sglang_config:
aggregated:
served-model-name: nvidia/Qwen3.5-397B-A17B-NVFP4
model-path: /model/
trust-remote-code: true
tensor-parallel-size: 4
data-parallel-size: 1
expert-parallel-size: 1
enable-symm-mem: true
quantization: modelopt_fp4
kv-cache-dtype: fp8_e4m3
mamba-ssm-dtype: bfloat16
mamba-scheduler-strategy: extra_buffer
mamba-track-interval: 8192
attention-backend: trtllm_mha
moe-runner-backend: flashinfer_trtllm
speculative-algorithm: NEXTN
speculative-num-steps: 3
speculative-eagle-topk: 1
speculative-num-draft-tokens: 4
cuda-graph-max-bs: 256
max-running-requests: 1024
max-prefill-tokens: 16384
chunked-prefill-size: 16384
mem-fraction-static: 0.8
max-mamba-cache-size: 1024
allow-auto-truncate: true
stream-interval: 50
scheduler-recv-interval: 10
tokenizer-worker-num: 6
page-size: 64
enable-hierarchical-cache: true
hicache-ratio: 1.05
hicache-io-backend: kernel
hicache-mem-layout: page_first_direct
hicache-write-policy: write_through

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

srun_options:
mem: "0"
# gb300-nv: pyxis maps the calling user into the container as a non-root uid;
# agentic_srt.sh's apt-get install git step needs EUID 0. Remap to uid 0 inside
# the container. srt-slurm renders empty-string values as flag-only srun args.
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"
# Aggregated: one worker serves both prefill and decode, so GPU accounting is
# the single-worker form num_gpus = TP (not the disagg prefill+decode sum).
# Force the single-node post-proc path; TP must match sglang_config tp-size.
IS_MULTINODE: "false"
TP: "4"
# Match the source run's dataset (256k cc-traces-with-subagents variant).
WEKA_LOADER_OVERRIDE: semianalysis_cc_traces_weka_062126_256k
# Container-side path of the aiperf mmap dataset cache; host-side mount wired
# via launch_gb300-nv.sh srtslurm.yaml default_mounts.
AIPERF_DATASET_MMAP_CACHE_DIR: "/aiperf_mmap_cache"
# Persistent HF hub cache (also via default_mounts).
HF_HUB_CACHE: "/hf_hub_cache"
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