SemiAnalysisAI · Qiaolin-Yu · Apr 26, 2026 · Apr 25, 2026 · Apr 25, 2026 · Apr 26, 2026
@@ -1670,7 +1670,7 @@ dsr1-fp4-b200-sglang:
     - { tp: 8, ep: 8, conc-start: 4, conc-end: 16 }
 
 dsv4-fp4-b200-sglang:
-  image: lmsysorg/sglang:deepseek-v4-blackwell
+  image: lmsysorg/sglang:deepseek-v4-blackwell@sha256:df18bfc4aa9ecf59451002b49ba00cae58042de9e2a96378bbd21b404dd62c7b
   model: deepseek-ai/DeepSeek-V4-Pro
   model-prefix: dsv4
   runner: b200-dsv4
@@ -1690,7 +1690,7 @@ dsv4-fp4-b200-sglang:
     osl: 1024
     search-space:
     # low-latency
-    - { tp: 8, ep: 1, conc-start: 4, conc-end: 32 }
+    - { tp: 8, ep: 1, conc-start: 1, conc-end: 32 }
     # balanced
     - { tp: 8, ep: 8, dp-attn: true, conc-start: 64, conc-end: 128 }
     # max-throughput
@@ -1699,7 +1699,7 @@ dsv4-fp4-b200-sglang:
     osl: 1024
     search-space:
     # low-latency
-    - { tp: 8, ep: 1, conc-start: 4, conc-end: 32 }
+    - { tp: 8, ep: 1, conc-start: 1, conc-end: 32 }
     # balanced
     - { tp: 8, ep: 8, dp-attn: true, conc-start: 64, conc-end: 128 }
     # max-throughput
@@ -1838,7 +1838,7 @@ dsr1-fp8-b300-sglang:
 # until a B300-specific recipe ships. Prefix caching is disabled.
 # Parallelisms and concurrency ranges mirror dsv4-fp4-b200-vllm.
 dsv4-fp4-b300-sglang:
-  image: lmsysorg/sglang:deepseek-v4-b300
+  image: lmsysorg/sglang:deepseek-v4-b300@sha256:26e116bd211e300dbb76924d56c5cbe6cc3ee5ee2fe314859cb8774f5bc070f3
   model: deepseek-ai/DeepSeek-V4-Pro
   model-prefix: dsv4
   runner: b300
@@ -1857,21 +1857,15 @@ dsv4-fp4-b300-sglang:
   - isl: 1024
     osl: 1024
     search-space:
-    # low-latency
-    - { tp: 8, ep: 1, conc-start: 4, conc-end: 32 }
-    # balanced
-    - { tp: 8, ep: 8, dp-attn: true, conc-start: 64, conc-end: 128 }
-    # max-throughput
-    - { tp: 8, ep: 8, dp-attn: true, conc-start: 256, conc-end: 1024 }
+    - { tp: 8, ep: 1, conc-start: 1, conc-end: 1 }
+    - { tp: 4, ep: 1, conc-start: 32, conc-end: 32 }
+    - { tp: 4, ep: 4, dp-attn: true, conc-start: 512, conc-end: 512 }
   - isl: 8192
     osl: 1024
     search-space:
-    # low-latency
-    - { tp: 8, ep: 1, conc-start: 4, conc-end: 32 }
-    # balanced
-    - { tp: 8, ep: 8, dp-attn: true, conc-start: 64, conc-end: 128 }
-    # max-throughput
-    - { tp: 8, ep: 8, dp-attn: true, conc-start: 256, conc-end: 512 }
+    - { tp: 8, ep: 1, conc-start: 1, conc-end: 1 }
+    - { tp: 4, ep: 1, conc-start: 32, conc-end: 32 }
+    - { tp: 4, ep: 4, dp-attn: true, conc-start: 512, conc-end: 512 }
 
 qwen3.5-bf16-b200-sglang:
   image: lmsysorg/sglang:nightly-dev-20260216-d3bae71e

diff --git a/benchmarks/single_node/dsv4_fp4_b200.sh b/benchmarks/single_node/dsv4_fp4_b200.sh
@@ -5,6 +5,7 @@ source "$(dirname "$0")/../benchmark_lib.sh"
 check_env_vars \
     MODEL \
     TP \
+    DP_ATTENTION \
     CONC \
     ISL \
     OSL \
@@ -19,7 +20,13 @@ hf download "$MODEL"
 
 nvidia-smi
 
+# Common SGLANG env vars (apply to every config).
 export SGLANG_JIT_DEEPGEMM_PRECOMPILE=0
+export SGLANG_OPT_SWA_SPLIT_LEAF_ON_INSERT=1
+export SGLANG_OPT_USE_JIT_NORM=1
+export SGLANG_OPT_USE_JIT_INDEXER_METADATA=1
+export SGLANG_OPT_USE_TOPK_V2=1
+export SGLANG_OPT_USE_CUSTOM_ALL_REDUCE_V2=1
 
 # TODO(Cam): the lmsysorg/sglang:deepseek-v4-blackwell image installs sglang
 # editable at /workspace/sglang/python; prior sglang tags used /sgl-workspace/sglang.
@@ -30,7 +37,7 @@ export SGLANG_JIT_DEEPGEMM_PRECOMPILE=0
 SERVER_LOG="$PWD/server.log"
 PORT=${PORT:-8888}
 
-echo "TP: $TP, CONC: $CONC, ISL: $ISL, OSL: $OSL"
+echo "TP: $TP, DP_ATTENTION: $DP_ATTENTION, CONC: $CONC, ISL: $ISL, OSL: $OSL"
 
 EVAL_CONTEXT_ARGS=""
 if [ "${EVAL_ONLY}" = "true" ]; then
@@ -40,47 +47,41 @@ fi
 
 start_gpu_monitor --output "$PWD/gpu_metrics.csv"
 
-# Three recipes from https://docs.sglang.io/cookbook/autoregressive/DeepSeek/DeepSeek-V4
-# (spec-decoding / MTP and prefix-caching flags dropped for the baseline):
-#   - low-latency    (CONC <= 32):        TP-only, chunked-prefill, disable autotune
-#   - balanced       (32 < CONC <= 128):  + DP-attn, max-running-requests=128
-#   - max-throughput (CONC > 128):        + DP-attn, max-running-requests=256
+# Pick the parallelism + MoE backend based on DP_ATTENTION (mirrors the vllm
+# script's pattern). DP-attention turns on EP-MoE (deepep) and the related
+# mega_moe optimizations; single-instance uses flashinfer_mxfp4.
 DEEPEP_CONFIG='{"normal_dispatch":{"num_sms":96},"normal_combine":{"num_sms":96}}'
 
-if [[ $CONC -le 32 ]]; then
-    RECIPE=low-latency
-    RECIPE_FLAGS=(
-        --moe-runner-backend flashinfer_mxfp4
-        --chunked-prefill-size 4096
-        --disable-flashinfer-autotune
-        --mem-fraction-static 0.82
-    )
-elif [[ $CONC -le 128 ]]; then
-    RECIPE=balanced
-    export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=256
-    RECIPE_FLAGS=(
+if [ "${DP_ATTENTION}" = "true" ]; then
+    export SGLANG_OPT_USE_DEEPGEMM_MEGA_MOE=1
+    export SGLANG_OPT_FIX_HASH_MEGA_MOE=1
+    export SGLANG_OPT_USE_FAST_MASK_EP=1
+    export SGLANG_OPT_FIX_MEGA_MOE_MEMORY=1
+    export SGLANG_OPT_DEEPGEMM_MEGA_MOE_NUM_MAX_TOKENS_PER_RANK=4096
+    export SGLANG_OPT_FIX_NEXTN_MEGA_MOE=1
+    export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=0
+    PARALLEL_ARGS=(
         --dp-size "$TP"
         --enable-dp-attention
         --moe-a2a-backend deepep
         --deepep-config "$DEEPEP_CONFIG"
-        --mem-fraction-static 0.82
-        --cuda-graph-max-bs 64
-        --max-running-requests 128
+        --chunked-prefill-size 32768
     )
 else
-    RECIPE=max-throughput
-    export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=256
-    RECIPE_FLAGS=(
-        --dp-size "$TP"
-        --enable-dp-attention
-        --moe-a2a-backend deepep
-        --deepep-config "$DEEPEP_CONFIG"
-        --mem-fraction-static 0.82
-        --cuda-graph-max-bs 64
-        --max-running-requests 256
+    PARALLEL_ARGS=(
+        --moe-runner-backend flashinfer_mxfp4
+        --chunked-prefill-size 8192
+        --disable-flashinfer-autotune
     )
 fi
-echo "Recipe: $RECIPE (CONC=$CONC)"
+
+# Print all SGLANG_* env vars to both the CI step log and server.log so the
+# launch config is auditable from the result artifact alone.
+{
+    echo "=== SGLANG_* env vars at launch ==="
+    env | grep -E '^SGLANG_' | sort
+    echo "==================================="
+} | tee "$SERVER_LOG"
 
 set -x
 PYTHONNOUSERSITE=1 sglang serve \
@@ -89,8 +90,10 @@ PYTHONNOUSERSITE=1 sglang serve \
     --port $PORT \
     --trust-remote-code \
     --tp $TP \
-    --disable-radix-cache \
-    "${RECIPE_FLAGS[@]}" $EVAL_CONTEXT_ARGS > $SERVER_LOG 2>&1 &
+    --max-running-requests "$((CONC * 3 / 2))" \
+    --mem-fraction-static 0.90 \
+    --swa-full-tokens-ratio 0.1 \
+    "${PARALLEL_ARGS[@]}" $EVAL_CONTEXT_ARGS >> $SERVER_LOG 2>&1 &
 
 SERVER_PID=$!
 

diff --git a/benchmarks/single_node/dsv4_fp4_b300_sglang.sh b/benchmarks/single_node/dsv4_fp4_b300_sglang.sh
@@ -5,6 +5,7 @@ source "$(dirname "$0")/../benchmark_lib.sh"
 check_env_vars \
     MODEL \
     TP \
+    DP_ATTENTION \
     CONC \
     ISL \
     OSL \
@@ -23,12 +24,13 @@ fi
 
 nvidia-smi
 
+# Common SGLANG env vars (apply to every config).
 export SGLANG_JIT_DEEPGEMM_PRECOMPILE=0
-
-# The deepseek-v4 sglang images (lmsysorg/sglang:deepseek-v4-blackwell and its
-# B300 forks) bake CUDA_VISIBLE_DEVICES=4,5,6,7 into their ENV, which masks half
-# of the 8 GPUs Slurm allocates us. Clear it so TP=8 can bind to all ranks.
-unset CUDA_VISIBLE_DEVICES
+export SGLANG_OPT_SWA_SPLIT_LEAF_ON_INSERT=1
+export SGLANG_OPT_USE_JIT_NORM=1
+export SGLANG_OPT_USE_JIT_INDEXER_METADATA=1
+export SGLANG_OPT_USE_TOPK_V2=1
+export SGLANG_OPT_USE_CUSTOM_ALL_REDUCE_V2=1
 
 # TODO(Cam): the deepseek-v4 sglang images install sglang editable at
 # /workspace/sglang/python; prior sglang tags used /sgl-workspace/sglang.
@@ -40,7 +42,7 @@ unset CUDA_VISIBLE_DEVICES
 SERVER_LOG="$PWD/server.log"
 PORT=${PORT:-8888}
 
-echo "TP: $TP, CONC: $CONC, ISL: $ISL, OSL: $OSL"
+echo "TP: $TP, DP_ATTENTION: $DP_ATTENTION, CONC: $CONC, ISL: $ISL, OSL: $OSL"
 
 EVAL_CONTEXT_ARGS=""
 if [ "${EVAL_ONLY}" = "true" ]; then
@@ -50,47 +52,56 @@ fi
 
 start_gpu_monitor --output "$PWD/gpu_metrics.csv"
 
-# Three recipes from https://docs.sglang.io/cookbook/autoregressive/DeepSeek/DeepSeek-V4
-# (spec-decoding / MTP and prefix-caching flags dropped for the baseline):
-#   - low-latency    (CONC <= 32):        TP-only, chunked-prefill, disable autotune
-#   - balanced       (32 < CONC <= 128):  + DP-attn, max-running-requests=128
-#   - max-throughput (CONC > 128):        + DP-attn, max-running-requests=256
+# 1k inputs need more SWA cache headroom on B300 than 8k inputs do; 0.5 was
+# tuned empirically for the 1k1k recipe, while 0.1 is the cookbook default.
+if [[ "$ISL" == "1024" ]]; then
+    SWA_FULL_TOKENS_RATIO=0.5
+else
+    SWA_FULL_TOKENS_RATIO=0.1
+fi
+
+# Pick the parallelism + MoE backend based on DP_ATTENTION (mirrors the vllm
+# script's pattern). DP-attention runs the empirically-tuned high-concurrency
+# recipe (flashinfer_mxfp4 runner + halved prefill chunks + prefill-delayer);
+# single-instance uses flashinfer_mxfp4 with the cookbook defaults.
 DEEPEP_CONFIG='{"normal_dispatch":{"num_sms":96},"normal_combine":{"num_sms":96}}'
 
-if [[ $CONC -le 32 ]]; then
-    RECIPE=low-latency
-    RECIPE_FLAGS=(
-        --moe-runner-backend flashinfer_mxfp4
-        --chunked-prefill-size 4096
-        --disable-flashinfer-autotune
-        --mem-fraction-static 0.82
-    )
-elif [[ $CONC -le 128 ]]; then
-    RECIPE=balanced
-    export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=256
-    RECIPE_FLAGS=(
+# Default; the DP-attn branch below overrides to 0.94.
+MEM_FRACTION_STATIC=0.90
+
+if [ "${DP_ATTENTION}" = "true" ]; then
+    export SGLANG_OPT_USE_DEEPGEMM_MEGA_MOE=0
+    export SGLANG_OPT_FIX_HASH_MEGA_MOE=0
+    export SGLANG_OPT_USE_FAST_MASK_EP=1
+    export SGLANG_OPT_FIX_MEGA_MOE_MEMORY=1
+    export SGLANG_OPT_DEEPGEMM_MEGA_MOE_NUM_MAX_TOKENS_PER_RANK=4096
+    export SGLANG_OPT_FIX_NEXTN_MEGA_MOE=1
+    export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=0
+    PARALLEL_ARGS=(
         --dp-size "$TP"
         --enable-dp-attention
-        --moe-a2a-backend deepep
+        --moe-runner-backend flashinfer_mxfp4
+        --disable-flashinfer-autotune
         --deepep-config "$DEEPEP_CONFIG"
-        --mem-fraction-static 0.82
-        --cuda-graph-max-bs 64
-        --max-running-requests 128
+        --chunked-prefill-size 16384
+        --enable-prefill-delayer
     )
+    MEM_FRACTION_STATIC=0.94
 else
-    RECIPE=max-throughput
-    export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=256
-    RECIPE_FLAGS=(
-        --dp-size "$TP"
-        --enable-dp-attention
-        --moe-a2a-backend deepep
-        --deepep-config "$DEEPEP_CONFIG"
-        --mem-fraction-static 0.82
-        --cuda-graph-max-bs 64
-        --max-running-requests 256
+    PARALLEL_ARGS=(
+        --moe-runner-backend flashinfer_mxfp4
+        --chunked-prefill-size 8192
+        --disable-flashinfer-autotune
     )
 fi
-echo "Recipe: $RECIPE (CONC=$CONC)"
+
+# Print all SGLANG_* env vars to both the CI step log and server.log so the
+# launch config is auditable from the result artifact alone.
+{
+    echo "=== SGLANG_* env vars at launch ==="
+    env | grep -E '^SGLANG_' | sort
+    echo "==================================="
+} | tee "$SERVER_LOG"
 
 set -x
 PYTHONNOUSERSITE=1 sglang serve \
@@ -99,8 +110,10 @@ PYTHONNOUSERSITE=1 sglang serve \
     --port $PORT \
     --trust-remote-code \
     --tp $TP \
-    --disable-radix-cache \
-    "${RECIPE_FLAGS[@]}" $EVAL_CONTEXT_ARGS > $SERVER_LOG 2>&1 &
+    --max-running-requests "$((CONC * 3 / 2))" \
+    --mem-fraction-static "$MEM_FRACTION_STATIC" \
+    --swa-full-tokens-ratio "$SWA_FULL_TOKENS_RATIO" \
+    "${PARALLEL_ARGS[@]}" $EVAL_CONTEXT_ARGS >> $SERVER_LOG 2>&1 &
 
 SERVER_PID=$!
 

diff --git a/perf-changelog.yaml b/perf-changelog.yaml
@@ -1784,7 +1784,7 @@
   description:
     - "Restore the recipe-per-CONC split (low-latency / balanced / max-throughput) on top of the low-latency-only fallback from #1143; the DeepEP FP8 weight-postprocess path is fixed, so the high-throughput scenario runs again"
     - "Recipes from https://docs.sglang.io/cookbook/autoregressive/DeepSeek/DeepSeek-V4"
-  pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/1132
+  pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/1158
 
 - config-keys:
     - dsv4-fp8-mi355x-sglang