diff --git a/.github/workflows/benchmark-tmpl.yml b/.github/workflows/benchmark-tmpl.yml
index 0e32953e8a..fe614f7f7e 100644
--- a/.github/workflows/benchmark-tmpl.yml
+++ b/.github/workflows/benchmark-tmpl.yml
@@ -235,6 +235,13 @@ jobs:
             done
           fi
 
+          # The direct-host RTX runner mounts this checkout into a rootful
+          # container. Repair files left by an interrupted job before
+          # actions/checkout attempts its clean reset.
+          if [ "${{ runner.name }}" = "rtx6000pro-lat_00" ]; then
+            sudo -n chown -R "$(id -u):$(id -g)" "$GITHUB_WORKSPACE"
+          fi
+
           # Cleanup SLURM resources
           if command -v squeue >/dev/null 2>&1; then
             echo "[Slurm] Cleaning up jobs with name: ${{ runner.name }} ..."
diff --git a/benchmarks/single_node/agentic/glm5.2_fp4_rtx6000pro_sglang.sh b/benchmarks/single_node/agentic/glm5.2_fp4_rtx6000pro_sglang.sh
new file mode 100755
index 0000000000..c10643fcfb
--- /dev/null
+++ b/benchmarks/single_node/agentic/glm5.2_fp4_rtx6000pro_sglang.sh
@@ -0,0 +1,236 @@
+#!/usr/bin/env bash
+set -euo pipefail
+set -x
+
+# AgentX trace replay for GLM-5.2 NVFP4 on the 8x RTX PRO 6000 Blackwell
+# (SM120) node using SGLang.
+#
+# Flags start from the merged B300 NVFP4 cookbook recipe
+# (benchmarks/single_node/agentic/glm5.2_fp4_b300_sglang.sh, STP only) and
+# then apply the SM120 deltas this GPU family needs:
+#
+#   * TP8 only. The checkpoint is 433 GB, so pure TP across all eight 96 GB
+#     GPUs is the only layout that leaves room for the KV pool; TP4 does not
+#     fit the weights at all.
+#   * Shared-experts fusion is force-disabled. SGLang enables it for this
+#     config (n_routed_experts=256, n_shared_experts=1, EP off), but
+#     nvidia/GLM-5.2-NVFP4 stores the shared expert loose and unquantized
+#     (BF16 [2048, 6144]) while the routed experts are packed NVFP4
+#     ([2048, 3072]), so the fused loader aborts during weight load with
+#     "The size of tensor a (3072) must match the size of tensor b (6144)".
+#     This is the same trap the in-tree comment records for the
+#     compressed-tensors Kimi-K2.5 checkpoint.
+#   * Attention falls back to Triton MLA. SGLang would default this DSA model
+#     to --attention-backend dsa, whose indexer metadata comes only from
+#     DeepGEMM, and DeepGEMM has no SM120 kernel. Sparse attention is
+#     therefore not exercised on this GPU family, and prefill cost grows
+#     superlinearly with context.
+#   * MoE runner backend is left at SGLang's own SM120 choice for
+#     modelopt_fp4 (flashinfer_cutlass; trtllm-gen MoE is SM100-only) and is
+#     overridable through MOE_RUNNER_BACKEND.
+#   * HiCache is not supported on RTX PRO 6000, so this recipe is
+#     GPU-resident KV only.
+#
+# Required env vars:
+#   MODEL, TP, CONC, KV_OFFLOADING, TOTAL_CPU_DRAM_GB, RESULT_DIR, DURATION,
+#   EP_SIZE, DP_ATTENTION
+
+source "$(dirname "$0")/../../benchmark_lib.sh"
+
+check_env_vars \
+    MODEL \
+    TP \
+    CONC \
+    KV_OFFLOADING \
+    TOTAL_CPU_DRAM_GB \
+    RESULT_DIR \
+    DURATION \
+    EP_SIZE \
+    DP_ATTENTION
+
+if [[ "$TP" != "8" ]]; then
+    echo "GLM-5.2 SGLang on RTX PRO 6000 requires TP8: the 433 GB NVFP4 checkpoint does not fit in fewer than eight 96 GB GPUs" >&2
+    exit 1
+fi
+if [[ "$KV_OFFLOADING" != "none" ]]; then
+    echo "GLM-5.2 SGLang on RTX PRO 6000 supports GPU-resident KV cache only (HiCache is unsupported on this GPU family)" >&2
+    exit 1
+fi
+if [[ "$DP_ATTENTION" == "true" ]]; then
+    echo "GLM-5.2 SGLang on RTX PRO 6000 does not support DP attention: attention-DP replicates the KV pool per rank, which the 96 GB GPUs cannot hold alongside 54 GB of weights" >&2
+    exit 1
+fi
+if [[ "$EP_SIZE" != "1" ]]; then
+    echo "GLM-5.2 SGLang on RTX PRO 6000 supports EP1 only" >&2
+    exit 1
+fi
+
+# `hf download` creates the target dir if missing and is itself idempotent.
+# When MODEL_PATH is unset (stand-alone runs), fall back to the HF_HUB_CACHE.
+# Either way, MODEL_PATH is what the server is launched with.
+MODEL_REVISION="${GLM52_MODEL_REVISION:-aec724e8c7b8ee9db3b48c01c320f63f9cdaf8aa}"
+if [[ -n "${MODEL_PATH:-}" ]]; then
+    if [[ ! -d "$MODEL_PATH" || -z "$(ls -A "$MODEL_PATH" 2>/dev/null)" ]]; then
+        hf download "$MODEL" --revision "$MODEL_REVISION" --local-dir "$MODEL_PATH"
+    fi
+else
+    hf download "$MODEL" --revision "$MODEL_REVISION"
+    export MODEL_PATH="$MODEL"
+fi
+
+if [[ -n "${SLURM_JOB_ID:-}" ]]; then
+    echo "JOB $SLURM_JOB_ID running on ${SLURMD_NODENAME:-unknown}"
+fi
+nvidia-smi
+nvidia-smi topo -m || true
+
+# The FP8 KV pool holds 464,768 tokens per rank (~51 KB/token across 78
+# layers) at mem-fraction-static 0.85, so the model's 1M context does not fit
+# and the server is capped at 256k. Replay the matching 256k-capped corpus
+# instead of the 1M default this model prefix would otherwise select.
+export WEKA_LOADER_OVERRIDE="${WEKA_LOADER_OVERRIDE:-semianalysis_cc_traces_weka_062126_256k}"
+resolve_trace_source
+install_agentic_deps
+
+SERVER_LOG="$RESULT_DIR/server.log"
+mkdir -p "$RESULT_DIR"
+
+export PYTHONNOUSERSITE=1
+export TORCH_CUDA_ARCH_LIST=12.0a
+# All eight GPUs are SYS-connected (PCIe only, no NVLink) on this node, and
+# NCCL 2.28.9 segfaults probing its bnxt_re devices; the runner already sets
+# NCCL_IB_DISABLE=1. Keep collectives on the local PCIe/SHM transports.
+export NCCL_P2P_LEVEL="${NCCL_P2P_LEVEL:-SYS}"
+export NCCL_PROTO="${NCCL_PROTO:-LL,LL128,Simple}"
+export GLOO_SOCKET_IFNAME="${GLOO_SOCKET_IFNAME:-lo}"
+export NCCL_SOCKET_IFNAME="${NCCL_SOCKET_IFNAME:-lo}"
+export OMP_NUM_THREADS="${OMP_NUM_THREADS:-16}"
+
+# NOTE for whoever revisits the DSA path: SGLang carries a set of SM120
+# kernel fixups (SGLANG_OPT_FP8_WO_A_GEMM / SGLANG_OPT_USE_TOPK_V2 /
+# SGLANG_OPT_USE_TILELANG_MHC_PRE / SGLANG_OPT_DEEPGEMM_HC_PRENORM off,
+# SGLANG_FP8_PAGED_MQA_LOGITS_TORCH on) that it applies only inside its
+# DeepseekV4ForCausalLM branch, and GLM-5.2 (GlmMoeDsaForCausalLM) misses
+# them. They were tested on-node and are NOT needed on this Triton MLA
+# fallback path — boot and generation are identical with and without them —
+# so they are deliberately not exported here. They become relevant again if
+# the sparse DSA backend ever works on SM120.
+#
+# Do not simply switch --attention-backend back to dsa: enabling the sparse
+# path on SM120 was attempted on-node (2026-07-26) and needs kernel work, not
+# a flag. Patching SGLang to route the indexer's paged-MQA logits through its
+# TileLang/torch implementations (both already used by the dsv4 indexer) and
+# to stop building the DeepGEMM schedule plan does clear the "Unsupported
+# architecture" abort, and the server then boots and serves — but four more
+# walls follow:
+#   1. TileLang's CUDA sparse-MLA kernel takes bf16 KV only (its fp8 variants
+#      are ROCm-only), which halves the KV pool to 256,256 tokens/rank.
+#   2. That kernel asks for 170,048 B of dynamic shared memory; SM120 allows
+#      ~100 KB. Its tile size is not a knob — the barrier arrive_counts
+#      (384/256/128) encode the BI=64 thread mapping, so block_I=32 compiles
+#      and then reads out of bounds.
+#   3. The plain (v1) kernel does fit at block_I=32 / num_stages=1 / 128
+#      threads, but CUDA-graph capture fails because TileLang JIT-compiles
+#      inside the capture (cudaErrorStreamCaptureUnsupported).
+#   4. With graphs disabled it runs and is still numerically wrong: degenerate
+#      repetition on a 26-token prompt and an empty answer on the 45k-token
+#      needle, identically with the TileLang and the torch reference logits
+#      kernels — so the fault is the sparse-MLA kernel itself, not the indexer.
+# Fixing this means an SM120-shaped sparse-MLA kernel (split the d_v=512
+# accumulation so KV tiles stay under ~32 KB, re-derive the barrier counts,
+# validate against the dense path), not a config change. Full logs from the
+# attempt: rtx6000pro-lat:/home/ubuntu/glm52-sglang-patch/out/server.dsa*.log.
+
+# Agentic warmup dispatches hundreds of large prompts at once; allow up to
+# 15 minutes of TCP progress before AIPerf declares a connection dead.
+export AIPERF_HTTP_TCP_USER_TIMEOUT=900000
+# AIPerf pins one pooled keep-alive connection per session (client-side
+# keep-alive 300s) while uvicorn's default SGLANG_TIMEOUT_KEEP_ALIVE is 5s;
+# inter-turn idle gaps can reuse a socket exactly as the server closes it ->
+# ECONNRESET -> terminal warmup failure. Outlast the client pool.
+export SGLANG_TIMEOUT_KEEP_ALIVE=900
+# Dense-attention prefill on this node measured 872 tok/s at 4.4k context
+# falling to 418 tok/s at 244k (single stream), so a warmup snapshot of
+# 100k-token histories needs several minutes per trajectory. Double the
+# shared 1800s warmup grace so warmup drains instead of being declared
+# failed; grace is a maximum wait, not a fixed sleep.
+export AGENTIC_WARMUP_GRACE_PERIOD="${AGENTIC_WARMUP_GRACE_PERIOD:-3600}"
+
+# AgentX concurrency counts live session trees, not individual requests.
+# Allow subagent fan-out to exceed CONC without clipping request bursts.
+MAX_RUNNING_REQUESTS=$((2 * CONC))
+CUDA_GRAPH_MAX_BS=$MAX_RUNNING_REQUESTS
+[ "$CUDA_GRAPH_MAX_BS" -gt 64 ] && CUDA_GRAPH_MAX_BS=64
+
+MEM_FRACTION_STATIC="${MEM_FRACTION_STATIC:-0.85}"
+CONTEXT_LENGTH="${CONTEXT_LENGTH:-262144}"
+ATTENTION_BACKEND="${ATTENTION_BACKEND:-triton}"
+
+MOE_ARGS=()
+if [[ -n "${MOE_RUNNER_BACKEND:-}" ]]; then
+    MOE_ARGS=(--moe-runner-backend "$MOE_RUNNER_BACKEND")
+fi
+
+SGLANG_CMD=(
+    python3 -m sglang.launch_server
+    --model-path "$MODEL_PATH"
+    --served-model-name "$MODEL"
+    --host 0.0.0.0
+    --port "$PORT"
+    --trust-remote-code
+    --tp "$TP"
+    --ep-size "$EP_SIZE"
+    --quantization modelopt_fp4
+    # nvidia/GLM-5.2-NVFP4 keeps the shared expert loose and in BF16, so the
+    # fused-shared-expert loader cannot consume it (see the header note).
+    --disable-shared-experts-fusion
+    # SGLang would default this DSA model to --attention-backend dsa, whose
+    # indexer metadata is built by deep_gemm.get_paged_mqa_logits_metadata
+    # (the only CUDA option; 'cutedsl' is gated to SM100). DeepGEMM aborts
+    # with "Assertion error (attention.hpp:227): Unsupported architecture" on
+    # SM120 during warmup, so fall back to Triton MLA. Sparse attention is
+    # consequently not exercised on this GPU family.
+    --attention-backend "$ATTENTION_BACKEND"
+    "${MOE_ARGS[@]}"
+    # GLM-5.2 emits the GLM-4.7-style <tool_call>/<arg_key>/<arg_value>
+    # format; glm47 is required for structured message.tool_calls, and the
+    # reasoning parser keeps hybrid-thinking output in reasoning_content.
+    --tool-call-parser glm47
+    --reasoning-parser glm45
+    --context-length "$CONTEXT_LENGTH"
+    --chunked-prefill-size 8192
+    --mem-fraction-static "$MEM_FRACTION_STATIC"
+    --max-running-requests "$MAX_RUNNING_REQUESTS"
+    --cuda-graph-max-bs "$CUDA_GRAPH_MAX_BS"
+    --watchdog-timeout 1800
+    --enable-metrics
+)
+
+printf '%q ' "${SGLANG_CMD[@]}" | tee "$RESULT_DIR/sglang_command.txt"
+printf '\n' | tee -a "$RESULT_DIR/sglang_command.txt"
+
+echo "Starting SGLang server for RTX PRO 6000..."
+"${SGLANG_CMD[@]}" > "$SERVER_LOG" 2>&1 &
+SERVER_PID=$!
+echo "Server PID: $SERVER_PID"
+
+cleanup_agentic_server() {
+    local exit_code=$?
+    trap - EXIT INT TERM
+    set +e
+    stop_background_process_tree "$SERVER_PID" "SGLang server" 60
+    exit "$exit_code"
+}
+trap cleanup_agentic_server EXIT
+trap 'exit 130' INT
+trap 'exit 143' TERM
+
+wait_for_server_ready --port "$PORT" --server-log "$SERVER_LOG" --server-pid "$SERVER_PID"
+
+if [ "${EVAL_ONLY:-false}" = "true" ]; then
+    run_eval --port "$PORT"
+else
+    build_replay_cmd "$RESULT_DIR"
+    REPLAY_CMD+=" --server-metrics http://localhost:$PORT/metrics"
+    run_agentic_replay_and_write_outputs "$RESULT_DIR"
+fi
diff --git a/configs/nvidia-master.yaml b/configs/nvidia-master.yaml
index a388cad95d..e99835f57a 100644
--- a/configs/nvidia-master.yaml
+++ b/configs/nvidia-master.yaml
@@ -8097,3 +8097,35 @@ glm5.2-fp4-b300-sglang-agentic:
       # radix cache (GPU hit 0.93->0.57 at 48->64) and thrashes on re-prefill, so it is
       # strictly dominated by conc 48 on both throughput and interactivity.
       - { tp: 8, ep: 8, dp-attn: true, kv-offloading: dram, kv-offload-backend: { name: hicache }, conc-list: [48], router: { name: sglang-router, version: "0.3.2" } }
+
+# GLM-5.2 NVFP4 AgentX on the 8x RTX PRO 6000 Blackwell (SM120) Latitude node
+# with SGLang. Three SM120 facts shape this entry:
+#
+#   * TP8 only. The checkpoint is 433 GB (56 GB/GPU loaded), so pure TP across
+#     all eight 96 GB GPUs is the only layout that leaves room for a KV pool;
+#     TP4 cannot hold the weights, and attention-DP would replicate the pool
+#     per rank.
+#   * Sparse DSA attention is unavailable. SGLang's DSA backend builds its
+#     indexer paged-MQA-logits metadata through DeepGEMM (the only CUDA
+#     option; 'cutedsl' is gated to SM100), and DeepGEMM asserts "Unsupported
+#     architecture" on SM120, so the recipe runs the Triton MLA fallback with
+#     the sparse indexer bypassed. Prefill therefore scales superlinearly with
+#     context (measured on-node: 17.6k tokens 5.2 s -> 70.5k tokens 38.9 s).
+#   * Context is capped at 256k, not the model's 1M. The FP8 KV pool is
+#     464,768 tokens per rank at mem-fraction-static 0.85 (~51 KB/token across
+#     78 layers), so the recipe replays the 256k-capped trace corpus and stops
+#     at conc 4, where four full-length sessions already exceed the pool and
+#     start re-prefilling.
+glm5.2-fp4-rtx6000pro-sglang-agentic:
+  image: lmsysorg/sglang:v0.5.15.post1-cu130
+  model: nvidia/GLM-5.2-NVFP4
+  model-prefix: glm5.2
+  runner: cluster:rtx6000pro-lat
+  precision: fp4
+  framework: sglang
+  multinode: false
+  scenarios:
+    agentic-coding:
+    - dram-utilization: 0.80
+      search-space:
+      - { tp: 8, ep: 1, dp-attn: false, kv-offloading: none, conc-list: [1, 2, 4] }
diff --git a/configs/runners.yaml b/configs/runners.yaml
index 851b821ba2..5008248a1b 100644
--- a/configs/runners.yaml
+++ b/configs/runners.yaml
@@ -164,6 +164,10 @@ labels:
     - gb300-nv_0
     - gb300-nv_1
     - gb300-nv_2
+  rtx6000pro:
+    - rtx6000pro-lat_00
+  rtx6000pro-lat:
+    - rtx6000pro-lat_00
   cluster:h100-cw:
     - h100-cw_00
     - h100-cw_01
@@ -251,6 +255,8 @@ labels:
     - gb300-nv_0
     - gb300-nv_1
     - gb300-nv_2
+  cluster:rtx6000pro-lat:
+    - rtx6000pro-lat_00
   cluster:mi300x-amds:
     - mi300x-amds_00
     - mi300x-amds_01
diff --git a/perf-changelog.yaml b/perf-changelog.yaml
index 975c917743..19c05b8007 100644
--- a/perf-changelog.yaml
+++ b/perf-changelog.yaml
@@ -5066,3 +5066,19 @@
   description:
     - "Bump SGLang container image from lmsysorg/sglang:v0.5.12-cu130 to lmsysorg/sglang:v0.5.15.post1-cu130 (https://github.com/sgl-project/sglang/releases/tag/v0.5.15.post1)"
   pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/2313
+
+- config-keys:
+    - glm5.2-fp4-rtx6000pro-sglang-agentic
+  scenario-type:
+    - agentic-coding
+  description:
+    - "Add a GLM-5.2 NVFP4 SGLang AgentX sweep on the 8x RTX PRO 6000 Blackwell (SM120) Latitude runner, TP8/EP1 with GPU-resident FP8 KV cache at concurrency 1, 2, and 4"
+    - "Pin lmsysorg/sglang:v0.5.15.post1-cu130, the same image as the merged B300 GLM-5.2 SGLang recipe and the first release whose GlmMoeDsaForCausalLM support is present"
+    - "Force --disable-shared-experts-fusion: SGLang enables fusion for this config, but nvidia/GLM-5.2-NVFP4 stores the shared expert loose in BF16 while the routed experts are packed NVFP4, so the fused loader aborts weight load with a 3072-vs-6144 shape mismatch"
+    - "Run the Triton MLA attention fallback instead of the sparse DSA backend: SGLang builds DSA indexer paged-MQA-logits metadata through DeepGEMM only ('cutedsl' is gated to SM100), and DeepGEMM asserts 'Unsupported architecture' on SM120, so GLM-5.2's sparse-attention advantage is not realized on this GPU family"
+    - "Do not carry SGLang's DeepseekV4-only SM120 kernel fixups (FP8 weight-only GEMM, topk_v2, tilelang/DeepGEMM hidden-compression prenorm, torch paged-MQA logits): tested on-node, boot and generation are identical with and without them on this Triton fallback path"
+    - "Cap context at 256k and replay the 256k-capped trace corpus: the FP8 KV pool holds 464,768 tokens per rank at mem-fraction-static 0.85 (~51 KB/token across 78 layers), so the model's 1M context does not fit and conc >4 re-prefills continuously"
+    - "Raise the agentic warmup grace period to 3600s: dense-attention prefill measured 872 tok/s at 4.4k context falling to 418 tok/s at 244k, so long-history warmup snapshots need more than the shared 1800s default"
+    - "Validated over SSH: TP8 boot on the node (56.04 GB weights/GPU, 464,768-token KV pool/rank, CUDA graphs captured, /health 200), short-prompt correctness through the glm45/glm47 parsers, and exact needle retrieval at 45,394 / 155,552 / 220,029-token prompts with no NaN from the SM120 flashinfer_cutlass NVFP4 MoE path"
+    - "Record in the recipe why the sparse DSA path cannot be re-enabled by flag: patching SGLang to use its TileLang/torch paged-MQA-logits kernels clears the DeepGEMM abort and boots, but the TileLang sparse-MLA kernel needs bf16 KV, asks for 170,048 B of shared memory against SM120's ~100 KB, cannot be CUDA-graph captured, and is numerically wrong at the only tile size that fits"
+  pr-link: XXX
diff --git a/runners/launch_rtx6000pro-lat.sh b/runners/launch_rtx6000pro-lat.sh
new file mode 100755
index 0000000000..4b7b0e7548
--- /dev/null
+++ b/runners/launch_rtx6000pro-lat.sh
@@ -0,0 +1,144 @@
+#!/usr/bin/bash
+set -euo pipefail
+
+# This runner executes directly on the single RTX PRO 6000 GPU node. Docker
+# therefore owns a separate image cache from the node's RKE2/containerd cache.
+HF_HUB_CACHE_MOUNT="${HF_HUB_CACHE_MOUNT:-/var/lib/inferencex/hf-hub-cache}"
+export HF_HUB_CACHE="${HF_HUB_CACHE:-/mnt/hf_hub_cache/}"
+PORT="${PORT:-8888}"
+
+# NCCL 2.28.9 segfaults while probing this node's bnxt_re RDMA devices.
+# Disable that RDMA path by default while preserving local CUDA P2P/SHM and
+# allowing an explicit caller override.
+export NCCL_IB_DISABLE="${NCCL_IB_DISABLE:-1}"
+
+: "${GITHUB_WORKSPACE:?GITHUB_WORKSPACE must be set}"
+: "${IMAGE:?IMAGE must be set}"
+: "${EXP_NAME:?EXP_NAME must be set}"
+: "${PRECISION:?PRECISION must be set}"
+
+mkdir -p "$HF_HUB_CACHE_MOUNT"
+
+export GPU_COUNT="${GPU_COUNT:-${TP:?TP must be set}}"
+if [[ ! "$GPU_COUNT" =~ ^[1-9][0-9]*$ ]]; then
+    echo "GPU_COUNT must be a positive integer, got: $GPU_COUNT" >&2
+    exit 1
+fi
+
+export CUDA_VISIBLE_DEVICES
+CUDA_VISIBLE_DEVICES="$(seq -s, 0 "$((GPU_COUNT - 1))")"
+
+# Some Slurm/enroot configs spell registry paths as nvcr.io#namespace/image.
+# Docker requires the normal slash form.
+DOCKER_IMAGE="${IMAGE//#//}"
+
+SPEC_SUFFIX=""
+if [[ "${SPEC_DECODING:-}" == "mtp" ]]; then
+    SPEC_SUFFIX="_mtp"
+fi
+
+export SCENARIO_SUBDIR="${SCENARIO_SUBDIR:-fixed_seq_len/}"
+SCENARIO_SUBDIR="${SCENARIO_SUBDIR#/}"
+SCENARIO_SUBDIR="${SCENARIO_SUBDIR%/}/"
+BENCH_BASE="benchmarks/single_node/${SCENARIO_SUBDIR}${EXP_NAME%%_*}_${PRECISION}_rtx6000pro"
+
+# Prefer the framework-tagged name so several engines can serve the same model
+# on this runner, and fall back to the untagged name that predates it.
+BENCH_SCRIPT="${BENCH_BASE}_${FRAMEWORK:-vllm}${SPEC_SUFFIX}.sh"
+if [[ ! -f "$GITHUB_WORKSPACE/$BENCH_SCRIPT" ]]; then
+    BENCH_SCRIPT="${BENCH_BASE}${SPEC_SUFFIX}.sh"
+fi
+
+if [[ ! -f "$GITHUB_WORKSPACE/$BENCH_SCRIPT" ]]; then
+    echo "Benchmark script not found: $GITHUB_WORKSPACE/$BENCH_SCRIPT" >&2
+    exit 1
+fi
+
+server_name="bmk-server-${RUNNER_NAME:-rtx6000pro-lat}"
+server_name="${server_name//[^a-zA-Z0-9_.-]/-}"
+
+cleanup() {
+    docker rm -f "$server_name" >/dev/null 2>&1 || true
+}
+trap cleanup EXIT
+
+# Clear a container left behind by a cancelled or interrupted workflow.
+cleanup
+
+docker run \
+    --rm \
+    --pull=missing \
+    --name="$server_name" \
+    --runtime=nvidia \
+    --gpus="$GPU_COUNT" \
+    --network=host \
+    --ipc=host \
+    --privileged \
+    --shm-size=32g \
+    --ulimit memlock=-1 \
+    --ulimit stack=67108864 \
+    --security-opt seccomp=unconfined \
+    --cap-add=SYS_PTRACE \
+    --volume "$HF_HUB_CACHE_MOUNT:$HF_HUB_CACHE" \
+    --volume "$GITHUB_WORKSPACE:/workspace/" \
+    --workdir=/workspace/ \
+    --env HF_TOKEN \
+    --env HF_HUB_CACHE \
+    --env MODEL \
+    --env MODEL_PREFIX \
+    --env MODEL_PATH \
+    --env TP \
+    --env PP_SIZE \
+    --env DCP_SIZE \
+    --env PCP_SIZE \
+    --env EP_SIZE \
+    --env DP_SIZE \
+    --env DP_ATTENTION \
+    --env GPU_COUNT \
+    --env CONC \
+    --env MAX_MODEL_LEN \
+    --env ISL \
+    --env OSL \
+    --env FRAMEWORK \
+    --env PRECISION \
+    --env DISAGG \
+    --env SPEC_DECODING \
+    --env NUM_SPEC_TOKENS \
+    --env RUN_EVAL \
+    --env EVAL_ONLY \
+    --env EVAL_LIMIT \
+    --env EVAL_MAX_MODEL_LEN \
+    --env RUNNER_TYPE \
+    --env RUNNER_NAME \
+    --env RESULT_FILENAME \
+    --env RESULT_DIR \
+    --env RANDOM_RANGE_RATIO \
+    --env GPU_MEM_UTIL \
+    --env AIPERF_FAILED_REQUEST_THRESHOLD \
+    --env KV_OFFLOADING \
+    --env KV_OFFLOAD_BACKEND \
+    --env KV_OFFLOAD_BACKEND_METADATA \
+    --env ROUTER_METADATA \
+    --env KV_P2P_TRANSFER \
+    --env TOTAL_CPU_DRAM_GB \
+    --env DURATION \
+    --env SCENARIO_TYPE \
+    --env SCENARIO_SUBDIR \
+    --env IS_AGENTIC \
+    --env SWEBENCH_GEN_MODE \
+    --env SWEBENCH_USE_MODAL \
+    --env MODAL_TOKEN_ID \
+    --env MODAL_TOKEN_SECRET \
+    --env PROFILE \
+    --env SGLANG_TORCH_PROFILER_DIR \
+    --env VLLM_TORCH_PROFILER_DIR \
+    --env VLLM_RPC_TIMEOUT \
+    --env PYTHONDONTWRITEBYTECODE \
+    --env PYTHONPYCACHEPREFIX=/tmp/pycache/ \
+    --env PORT="$PORT" \
+    --env CUDA_DEVICE_ORDER=PCI_BUS_ID \
+    --env CUDA_VISIBLE_DEVICES \
+    --env NCCL_IB_DISABLE \
+    --entrypoint=/bin/bash \
+    "$DOCKER_IMAGE" \
+    "$BENCH_SCRIPT"