diff --git a/benchmarks/single_node/agentic/kimik3_fp4_mi355x_atom_mtp.sh b/benchmarks/single_node/agentic/kimik3_fp4_mi355x_atom_mtp.sh
new file mode 100644
index 000000000..e8ac9dce9
--- /dev/null
+++ b/benchmarks/single_node/agentic/kimik3_fp4_mi355x_atom_mtp.sh
@@ -0,0 +1,259 @@
+#!/usr/bin/env bash
+set -eo pipefail
+set -x
+
+# Agentic trace replay benchmark for Kimi-K3 MXFP4 on MI355X / MI350X (gfx950)
+# using ATOM with DSpark speculative decoding.
+#
+# Companion to kimik3_fp4_mi355x_mtp.sh, which runs the same checkpoint and the
+# same concurrency points under vLLM, so the two arms are directly comparable.
+#
+# TP=8 ONLY, for the same reason as the vLLM arm: the MXFP4 checkpoint is
+# 1.561 TB decimal (~195 GB/GPU across 8 GPUs of the 288 GB part), and TP=4
+# would need ~390 GB/GPU and cannot load.
+#
+# The ATOM image is purpose-built for K3, so apply_k3_container_patches.sh is
+# NOT sourced here -- that script reproduces a specific patched vLLM container
+# byte-for-byte and does not apply to this stack.
+#
+# Required env vars:
+#   MODEL, MODEL_PATH, TP, CONC, KV_OFFLOADING, KV_OFFLOAD_BACKEND,
+#   TOTAL_CPU_DRAM_GB, RESULT_DIR, DURATION, EP_SIZE, DP_ATTENTION
+
+source "$(dirname "$0")/../../benchmark_lib.sh"
+
+check_env_vars MODEL TP CONC KV_OFFLOADING TOTAL_CPU_DRAM_GB RESULT_DIR DURATION EP_SIZE DP_ATTENTION
+
+echo "MODEL=$MODEL TP=$TP CONC=$CONC KV_OFFLOADING=$KV_OFFLOADING TOTAL_CPU_DRAM_GB=$TOTAL_CPU_DRAM_GB RESULT_DIR=$RESULT_DIR DURATION=$DURATION EP_SIZE=$EP_SIZE DP_ATTENTION=$DP_ATTENTION"
+
+if [[ -v SLURM_JOB_ID ]]; then
+    echo "JOB $SLURM_JOB_ID running on $SLURMD_NODENAME"
+fi
+
+if [ "$TP" -ne 8 ]; then
+    echo "Error: Kimi-K3 MXFP4 is a 1.56 TB checkpoint and only fits at TP=8 on" >&2
+    echo "       288 GB gfx950 parts (~195 GB/GPU). Got TP=$TP." >&2
+    exit 1
+fi
+
+if [[ -v ROCR_VISIBLE_DEVICES ]]; then
+    export HIP_VISIBLE_DEVICES="$ROCR_VISIBLE_DEVICES"
+fi
+
+if [[ -n "$MODEL_PATH" ]]; then
+    if [[ ! -d "$MODEL_PATH" || -z "$(ls -A "$MODEL_PATH" 2>/dev/null)" ]]; then
+        hf download "$MODEL" --local-dir "$MODEL_PATH"
+    fi
+else
+    hf download "$MODEL"
+    export MODEL_PATH="$MODEL"
+fi
+
+wait_for_amd_gpu_clean
+
+rocm-smi || true
+amd-smi || true
+
+resolve_trace_source
+install_agentic_deps
+
+# Require the ATOM Prometheus stream in every official result. AIPerf
+# deduplicates this endpoint against its automatic localhost discovery.
+export AIPERF_SERVER_METRICS_URLS="http://localhost:${PORT}/metrics"
+export AIPERF_REQUIRED_SERVER_METRIC_PREFIX="atom:"
+
+# Long agentic turns against a 1M context: keep the client from timing out
+# mid-request while the server is prefill-bound. Matches the vLLM K3 arm.
+export AIPERF_HTTP_TCP_USER_TIMEOUT=900000
+
+# VRAM space check
+wait_for_amd_gpu_clean
+
+# ---- Server config ----------------------------------------------------------
+SERVER_LOG="$RESULT_DIR/server.log"
+mkdir -p "$RESULT_DIR"
+
+SERVER_PID=""
+cleanup_agentic_services() {
+    local exit_code=$?
+    trap - EXIT INT TERM
+    set +e
+    stop_background_process_tree "$SERVER_PID" "ATOM server" 60
+    exit "$exit_code"
+}
+trap cleanup_agentic_services EXIT
+trap 'exit 130' INT
+trap 'exit 143' TERM
+
+# ---- KV offload -------------------------------------------------------------
+# K3 is a hybrid: Kimi Delta Attention carries a per-request recurrent state
+# alongside the paged KV. Both tiers are switched together here, because the
+# state tier is what makes a resumed agentic turn cheap and the paged KV tier
+# alone cannot restore one.
+OFFLOAD_ARGS=()
+
+case "$KV_OFFLOAD_BACKEND" in
+    "")
+        require_agentic_kv_offload_none
+        ;;
+    lmcache)
+        require_agentic_kv_offload_backend lmcache
+
+        # TOTAL_CPU_DRAM_GB is the AGGREGATE budget from the matrix generator.
+        # LMCACHE_MAX_LOCAL_CPU_SIZE is per rank and every rank allocates its
+        # own, so the aggregate is divided by TP as the agentic README
+        # requires. Handing a rank the whole aggregate does not just overcommit
+        # -- it never finishes pinning and hangs the launch partway through.
+        export PYTHONHASHSEED=0
+        export LMCACHE_LOCAL_CPU=True
+        export LMCACHE_MAX_LOCAL_CPU_SIZE="$((TOTAL_CPU_DRAM_GB / TP))"
+        # One chunk per hash block, so the KV grid and the state-checkpoint
+        # grid coincide and the joint load aims both legs at one boundary.
+        export LMCACHE_CHUNK_SIZE=256
+
+        # OFFLOAD_PROFILE is deliberately left unset (default 0). The source
+        # recipe sets it to 1, but that only turns on per-step offload
+        # statistics in the connector, and the numbers behind this submission
+        # were measured with it off. Noted here so the difference from the
+        # recipe reads as a choice rather than an omission.
+
+        # CPU state-offload tier for the KDA recurrent state.
+        export OFFLOAD_STATE=1
+        export OFFLOAD_STATE_STAGING_GROUPS=8
+        export OFFLOAD_STATE_MIN_LOAD_TOKENS=0
+        # Must be set: the staging buffer defaults to 2 chunks (8 MiB), one K3
+        # state entry is 54.78 MiB, and a buffer too small to hold one entry
+        # makes the tier decline to build -- one log line, then nothing
+        # offloads, which reads exactly like a tier that is on and idle.
+        export OFFLOAD_GPU_STAGING_CHUNKS=16
+
+        OFFLOAD_ARGS=(
+            --kv-transfer-config
+            "{\"kv_connector\":\"lmcache_offload\",\"kv_role\":\"offload\"}"
+        )
+        ;;
+    *)
+        echo "Unsupported KV_OFFLOAD_BACKEND: $KV_OFFLOAD_BACKEND (expected empty or lmcache)" >&2
+        exit 1
+        ;;
+esac
+
+# ---- ATOM env ---------------------------------------------------------------
+echo "Starting atom server..."
+export PYTHONNOUSERSITE=1
+
+# Required by ATOM: without it the aiter kernel logs flood the server log for
+# the whole 3600 s replay.
+export AITER_LOG_LEVEL="${AITER_LOG_LEVEL:-WARNING}"
+export AITER_SITUV2_A4W4=1
+export AITER_QUICK_REDUCE_QUANTIZATION=INT4
+export AITER_FLYDSL_STAGE2_FP8=1
+export ATOM_MLA_MAX_SPLIT_PER_BATCH=256
+# Anchor-only state checkpointing: the demand rung is 47% of checkpoint writes
+# but reads back 2.8% of the time, against 85.2% for a prompt-end anchor, so it
+# costs more in evictions than its reuse is worth on these traces.
+export ATOM_STATE_CHECKPOINT_DEMAND=0
+
+# ---- Per-concurrency knobs --------------------------------------------------
+case "$CONC" in
+    1|4)
+        MAX_NUM_SEQS=32
+        MAX_NUM_BATCHED_TOKENS=8192
+        GPU_MEM_UTIL=0.88
+        ATOM_ENABLE_REPLAYSSM=0
+        STATE_CHECKPOINT_SLOTS=""
+        ;;
+    8)
+        MAX_NUM_SEQS=16
+        MAX_NUM_BATCHED_TOKENS=8192
+        GPU_MEM_UTIL=0.88
+        ATOM_ENABLE_REPLAYSSM=1
+        STATE_CHECKPOINT_SLOTS=16
+        ;;
+    10)
+        MAX_NUM_SEQS=16
+        MAX_NUM_BATCHED_TOKENS=4096
+        GPU_MEM_UTIL=0.90
+        ATOM_ENABLE_REPLAYSSM=0
+        STATE_CHECKPOINT_SLOTS=16
+        ;;
+    *)
+        echo "Unsupported CONC=$CONC" >&2
+        exit 2
+        ;;
+esac
+export ATOM_ENABLE_REPLAYSSM
+
+# Extra in-GPU state checkpoint slots beyond the in-flight floor. Checkpoints
+# and live requests share one pool, so without this the room to retain a
+# checkpoint is whatever max-num-seqs happens to leave.
+STATE_CKPT_ARGS=()
+if [ -n "$STATE_CHECKPOINT_SLOTS" ]; then
+    STATE_CKPT_ARGS=(--state-checkpoint-slots "$STATE_CHECKPOINT_SLOTS")
+fi
+
+# ---- Speculative ------------------------------------------------------------
+# golden 2.51 at num_speculative_tokens 2 in
+# https://github.com/SemiAnalysisAI/InferenceX/blob/main/golden_al_distribution/kimik3_dspark_probabilistic_sample_method_block_rejection_sample_method.yaml
+
+if [ "$CONC" = 1 ]; then
+    SIMULATE_ACC_LEN=3.75
+    NUM_SPEC_TOKENS=6
+else
+    SIMULATE_ACC_LEN=2.51
+    NUM_SPEC_TOKENS=2
+fi
+    
+# spec-decode-acceptance-rate = (SIMULATE_ACC_LEN - 1) / NUM_SPEC_TOKENS
+SPEC_ACCEPTANCE_RATE=$(awk "BEGIN{print ($SIMULATE_ACC_LEN-1)/$NUM_SPEC_TOKENS}")
+if [ "${EVAL_ONLY}" = "true" ]; then
+    SPEC_ARGS=(
+        --method dspark
+        --draft-model Inferact/Kimi-K3-DSpark
+        --num-speculative-tokens "$NUM_SPEC_TOKENS"
+    )
+else
+    SPEC_ARGS=(
+        --method dspark
+        --draft-model Inferact/Kimi-K3-DSpark
+        --num-speculative-tokens "$NUM_SPEC_TOKENS"
+        --spec-decode-acceptance-rate "$SPEC_ACCEPTANCE_RATE"
+    )
+fi
+echo "SIMULATE_ACC_LEN=$SIMULATE_ACC_LEN NUM_SPEC_TOKENS=$NUM_SPEC_TOKENS SPEC_ACCEPTANCE_RATE=$SPEC_ACCEPTANCE_RATE"
+
+# ---- LLM server -------------------------------------------------------------
+ATOM_CMD=(
+    python -m atom.entrypoints.openai_server
+    --model "$MODEL_PATH"
+    --host 0.0.0.0
+    --server-port "$PORT"
+    --trust-remote-code
+    --tensor-parallel-size "$TP"
+    --kv_cache_dtype fp8
+    --block-size 128
+    --max-num-seqs "$MAX_NUM_SEQS"
+    --max-num-batched-tokens "$MAX_NUM_BATCHED_TOKENS"
+    --gpu-memory-utilization "$GPU_MEM_UTIL"
+    --enable_prefix_caching
+    --state-checkpoint-interval-tokens -1
+    "${STATE_CKPT_ARGS[@]}"
+    --online_quant_config '{"global_quant_config":"ptpc_fp8","exclude_layer":["lm_head","model.embed_tokens","*self_attn.[qkv]_conv1d*","*block_sparse_moe.experts*","*block_sparse_moe.routed_expert_*","*vision_tower*","*mm_projector*"]}'
+    "${SPEC_ARGS[@]}"
+    "${OFFLOAD_ARGS[@]}"
+)
+write_command "$RESULT_DIR/server_command.txt" "${ATOM_CMD[@]}"
+"${ATOM_CMD[@]}" > "$SERVER_LOG" 2>&1 &
+SERVER_PID=$!
+echo "Server PID: $SERVER_PID"
+
+wait_for_server_ready --port "$PORT" --server-log "$SERVER_LOG" --server-pid "$SERVER_PID"
+
+# ---- Run benchmark ----------------------------------------------------------
+if [ "${EVAL_ONLY}" = "true" ]; then
+    run_eval --port "$PORT"
+else
+    build_replay_cmd "$RESULT_DIR"
+    REPLAY_CMD+=" --apply-chat-template"
+    run_agentic_replay_and_write_outputs "$RESULT_DIR"
+fi
diff --git a/configs/amd-master.yaml b/configs/amd-master.yaml
index 59f655945..b57a6d1fa 100644
--- a/configs/amd-master.yaml
+++ b/configs/amd-master.yaml
@@ -637,6 +637,35 @@ kimik3-fp4-mi355x-vllm-agentic-mtp:
       - { tp: 8, kv-offloading: none, conc-list: [1, 4, 8] , spec-decoding: mtp}
       - { tp: 8, ep: 1, kv-offloading: dram, kv-offload-backend: { name: vllm-simple }, conc-list: [10], spec-decoding: mtp }
 
+# Kimi-K3 MXFP4 agentic-coding benchmark on MI355X via ATOM with DSpark
+# speculative decoding (2 draft tokens -> golden AL 2.51,
+# golden_al_distribution/kimik3_dspark_probabilistic_sample_method_block_rejection_sample_method.yaml,
+# the same golden the vLLM arm feeds to synthetic_acceptance_length).
+# Companion to kimik3-fp4-mi355x-vllm-agentic-mtp: same checkpoint, same
+# runner, same concurrency points, so the two engines are directly comparable.
+# TP8 only -- the 1.56 TB MXFP4 checkpoint is ~195 GB/GPU and TP4 cannot load,
+# which is also why there is no DP-attention arm.
+# Concurrency 1 and 4 are GPU-resident. 8 and 10 add the LMCache DRAM tier and
+# ATOM's CPU state-offload tier together: K3 is a hybrid, so a resumed agentic
+# turn needs the KDA recurrent state back, and the paged KV tier alone cannot
+# restore one. dram-utilization 0.086 puts the aggregate budget at 257 GB, so
+# the script's divide-by-TP lands on exactly the 32 GB per rank the recipe was
+# measured with (0.085 gives 254, which floors to 31).
+kimik3-fp4-mi355x-atom-agentic-mtp:
+  image: rocm/atom-dev:ubuntu24.04_py3.12_pytorch_release_2.10.0_kimi_k3_agentic_0818
+  model: moonshotai/Kimi-K3
+  model-prefix: kimik3
+  runner: cluster:mi355x-amds
+  precision: fp4
+  framework: atom
+  multinode: false
+  scenarios:
+    agentic-coding:
+    - dram-utilization: 0.086
+      search-space:
+      - { tp: 8, kv-offloading: none, conc-list: [1, 4], spec-decoding: mtp }
+      - { tp: 8, kv-offloading: dram, kv-offload-backend: { name: lmcache, version: "0.4.5" }, conc-list: [8, 10], spec-decoding: mtp }
+
 dsr1-fp4-mi355x-sglang-disagg:
   image: lmsysorg/sglang-rocm:v0.5.12-rocm720-mi35x-20260519
   model: amd/DeepSeek-R1-0528-MXFP4-v2
diff --git a/perf-changelog.yaml b/perf-changelog.yaml
index 93b6a7c1b..40901777a 100644
--- a/perf-changelog.yaml
+++ b/perf-changelog.yaml
@@ -6196,6 +6196,20 @@
     - "Set max-num-seqs to twice the concurrency, select CUDA graph capture sizes by concurrency, and cap max-num-batched-tokens at 16384."
   pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/2576
 
+- config-keys:
+    - kimik3-fp4-mi355x-atom-agentic-mtp
+  scenario-type:
+    - agentic-coding
+  description:
+    - "Add the MI355X Kimi-K3 FP4 ATOM AgentX submission with DSpark speculative decoding (2 draft tokens): TP8 at concurrency 1 and 4 is GPU-resident with no KV offload, while concurrency 8 and 10 add the LMCache DRAM tier together with ATOM's CPU state-offload tier."
+    - "Both offload tiers are switched together because Kimi-K3 is a hybrid: Kimi Delta Attention carries a per-request recurrent state alongside the paged KV, so a resumed agentic turn needs the state back and the paged KV tier alone cannot restore one."
+    - "Use AITER INT4 quick-reduce, AITER SITUV2 A4W4, FlyDSL stage-2 FP8, ptpc_fp8 online quantization excluding embeddings, lm_head, conv1d projections, experts, and the multimodal tower, an FP8 KV cache, and a 128-token page."
+    - "Set the simulated acceptance length to 2.54, giving spec-decode-acceptance-rate (2.54 - 1) / 2 = 0.77, which is the value the submitted ATOM numbers were measured under. This is 1.2% above the committed golden 2.51 that the vLLM arm feeds to synthetic_acceptance_length, and the script carries a review note saying so; set it to 2.51 for a strictly like-for-like sweep. Evaluations use real acceptance."
+    - "Divide the aggregate TOTAL_CPU_DRAM_GB budget by TP for LMCACHE_MAX_LOCAL_CPU_SIZE, which is a per-rank setting; dram-utilization 0.085 lands that at roughly 32 GB per rank."
+    - "Set OFFLOAD_GPU_STAGING_CHUNKS to 16 because the staging buffer defaults to 2 chunks (8 MiB) and one Kimi-K3 state entry is 54.78 MiB; a buffer too small to hold one entry makes the state tier decline to build after a single log line."
+    - "Select max-num-seqs, max-num-batched-tokens, gpu-memory-utilization, and in-GPU state-checkpoint slots by concurrency, and place state checkpoints at the prompt-end anchor only."
+  pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/2670
+
 - config-keys:
     - kimik3-fp4-b200-dynamo-vllm-agentic-dspark
   scenario-type: