diff --git a/.github/configs/nvidia-master.yaml b/.github/configs/nvidia-master.yaml
index a64803497..86fade6c1 100644
--- a/.github/configs/nvidia-master.yaml
+++ b/.github/configs/nvidia-master.yaml
@@ -1728,6 +1728,30 @@ dsr1-fp4-b300-sglang:
     - { tp: 4, ep: 4, conc-start: 4, conc-end: 128 }
     - { tp: 8, ep: 8, conc-start: 4, conc-end: 16 }
 
+# DeepSeek-V4-Pro recipe from https://vllm.ai/blog/deepseek-v4
+# The Pro recipe lists 8xB200 and 8xB300 with identical flags. Runs with
+# DP=8 + expert parallelism (no --tensor-parallel-size flag), FP8 KV cache,
+# block size 256, and an FP4 indexer cache.
+# precision is tagged fp4fp8 because the model runs fp4 weights with an
+# fp8 KV cache.
+dsv4-fp4fp8-b300-vllm:
+  image: vllm/vllm-openai:deepseekv4-cu130
+  model: deepseek-ai/DeepSeek-V4-Pro
+  model-prefix: dsv4
+  runner: b300
+  precision: fp4fp8
+  framework: vllm
+  multinode: false
+  seq-len-configs:
+  - isl: 1024
+    osl: 1024
+    search-space:
+    - { tp: 8, ep: 8, dp-attn: true, conc-start: 4, conc-end: 64 }
+  - isl: 8192
+    osl: 1024
+    search-space:
+    - { tp: 8, ep: 8, dp-attn: true, conc-start: 4, conc-end: 64 }
+
 dsr1-fp4-b200-trt:
   image: nvcr.io#nvidia/tensorrt-llm/release:1.2.0rc6.post2
   model: nvidia/DeepSeek-R1-0528-FP4-V2
diff --git a/benchmarks/single_node/dsv4_fp4fp8_b300.sh b/benchmarks/single_node/dsv4_fp4fp8_b300.sh
new file mode 100644
index 000000000..e4f8507c8
--- /dev/null
+++ b/benchmarks/single_node/dsv4_fp4fp8_b300.sh
@@ -0,0 +1,89 @@
+#!/usr/bin/env bash
+
+# Per https://vllm.ai/blog/deepseek-v4 the DeepSeek-V4-Pro recipe lists
+# 8xB200 and 8xB300 with identical flags, so this script mirrors
+# dsv4_fp4_b200.sh.
+
+source "$(dirname "$0")/../benchmark_lib.sh"
+
+check_env_vars \
+    MODEL \
+    TP \
+    CONC \
+    ISL \
+    OSL \
+    MAX_MODEL_LEN \
+    RANDOM_RANGE_RATIO \
+    RESULT_FILENAME
+
+if [[ -n "$SLURM_JOB_ID" ]]; then
+  echo "JOB $SLURM_JOB_ID running on $SLURMD_NODENAME"
+fi
+
+nvidia-smi
+
+hf download "$MODEL"
+
+SERVER_LOG=/workspace/server.log
+PORT=${PORT:-8888}
+
+# DeepSeek-V4-Pro weights are large and engine startup on B300 can exceed
+# the default 600s. Give it an hour to load.
+export VLLM_ENGINE_READY_TIMEOUT_S=3600
+
+if [ "${EVAL_ONLY}" = "true" ]; then
+    setup_eval_context
+    MAX_MODEL_LEN="$EVAL_MAX_MODEL_LEN"
+fi
+
+# Start GPU monitoring (power, temperature, clocks every second)
+start_gpu_monitor
+
+# Per the recipe, run with EP + DP=8 (no --tensor-parallel-size flag). TP
+# from the search space is used only for GPU allocation by the runner and
+# as the DP size.
+set -x
+vllm serve $MODEL --host 0.0.0.0 --port $PORT \
+--trust-remote-code \
+--kv-cache-dtype fp8 \
+--block-size 256 \
+--no-enable-prefix-caching \
+--enable-expert-parallel \
+--data-parallel-size $TP \
+--max-model-len $MAX_MODEL_LEN \
+--compilation-config '{"cudagraph_mode":"FULL_AND_PIECEWISE","custom_ops":["all"]}' \
+--attention_config.use_fp4_indexer_cache=True \
+--tokenizer-mode deepseek_v4 \
+--tool-call-parser deepseek_v4 \
+--enable-auto-tool-choice \
+--reasoning-parser deepseek_v4 > $SERVER_LOG 2>&1 &
+
+SERVER_PID=$!
+
+# Wait for server to be ready
+wait_for_server_ready --port "$PORT" --server-log "$SERVER_LOG" --server-pid "$SERVER_PID"
+
+pip install -q datasets pandas
+
+run_benchmark_serving \
+    --model "$MODEL" \
+    --port "$PORT" \
+    --backend vllm \
+    --input-len "$ISL" \
+    --output-len "$OSL" \
+    --random-range-ratio "$RANDOM_RANGE_RATIO" \
+    --num-prompts "$((CONC * 10))" \
+    --max-concurrency "$CONC" \
+    --result-filename "$RESULT_FILENAME" \
+    --result-dir /workspace/ \
+    --trust-remote-code
+
+# After throughput, run evaluation only if RUN_EVAL is true
+if [ "${RUN_EVAL}" = "true" ]; then
+    run_eval --framework lm-eval --port "$PORT"
+    append_lm_eval_summary
+fi
+
+# Stop GPU monitoring
+stop_gpu_monitor
+set +x
diff --git a/perf-changelog.yaml b/perf-changelog.yaml
index 528a12d02..196297d4b 100644
--- a/perf-changelog.yaml
+++ b/perf-changelog.yaml
@@ -1775,3 +1775,21 @@
     - "Model: sgl-project/DeepSeek-V4-Pro-FP8"
     - "https://github.com/sgl-project/sglang/pull/23608#issuecomment-4311952977"
   pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/1134
+
+- config-keys:
+    - dsv4-fp4fp8-b300-vllm
+  description:
+    - "Add DeepSeek-V4-Pro vLLM B300 benchmark per https://vllm.ai/blog/deepseek-v4"
+    - "Image: vllm/vllm-openai:deepseekv4-cu130"
+    - "Model: deepseek-ai/DeepSeek-V4-Pro"
+    - "Recipe lists 8xB200 and 8xB300 with identical flags; this mirrors the B200 config"
+    - "EP + DP=8 (no --tensor-parallel-size), FP4 weights + FP8 KV cache, block size 256, FP4 indexer cache"
+    - "Precision tag fp4fp8 reflects fp4 weights with fp8 KV cache"
+    - "VLLM_ENGINE_READY_TIMEOUT_S=3600 to accommodate large weight loading"
+    - "Flags: --trust-remote-code, --kv-cache-dtype fp8, --block-size 256, --no-enable-prefix-caching,"
+    - "  --enable-expert-parallel, --data-parallel-size=$TP,"
+    - "  --compilation-config cudagraph_mode=FULL_AND_PIECEWISE custom_ops=all,"
+    - "  --attention_config.use_fp4_indexer_cache=True, --tokenizer-mode deepseek_v4,"
+    - "  --tool-call-parser deepseek_v4, --enable-auto-tool-choice, --reasoning-parser deepseek_v4"
+    - "Configs: 1k1k conc 4-64, 8k1k conc 4-64"
+  pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/1128