diff --git a/.github/configs/amd-master.yaml b/.github/configs/amd-master.yaml
index 1321b2337..1b1d2e85b 100644
--- a/.github/configs/amd-master.yaml
+++ b/.github/configs/amd-master.yaml
@@ -2810,3 +2810,39 @@ minimaxm3-fp8-mi355x-vllm:
       - { tp: 8, ep: 8, conc-start: 1, conc-end: 512 }
       - { tp: 4, conc-start: 1, conc-end: 128 }
       - { tp: 8, ep: 8, dp-attn: true, conc-start: 128, conc-end: 512 }
+
+# EAGLE3 speculative-decoding (spec-decoding: mtp) variant of
+# minimaxm3-fp8-mi355x-vllm, pairing MiniMaxAI/MiniMax-M3-MXFP8 with the
+# Inferact/MiniMax-M3-EAGLE3 draft head (3 speculative tokens). No
+# attention_backend override is needed — the server runs on TRITON_ATTN, so
+# the FlashInfer page-128/MHA limitation that forced FLASH_ATTN on Blackwell
+# does not apply here. Search space mirrors the non-MTP entry trimmed at the
+# extreme-concurrency end, identical to the minimaxm3-fp8-b300-vllm-mtp /
+# b200-vllm-mtp precedent: spec decode pays off at low/mid concurrency while
+# acceptance dilutes in big batches, and the draft weights + draft KV shave
+# headroom — tp2-ep2 is dropped since its KV headroom was already thin.
+minimaxm3-fp8-mi355x-vllm-mtp:
+  image: vllm/vllm-openai-rocm:minimax-m3
+  model: MiniMaxAI/MiniMax-M3-MXFP8
+  model-prefix: minimaxm3
+  runner: mi355x
+  precision: fp8
+  framework: vllm
+  multinode: false
+  scenarios:
+    fixed-seq-len:
+    - isl: 1024
+      osl: 1024
+      search-space:
+      - { tp: 8, conc-start: 1, conc-end: 64, spec-decoding: mtp }
+      - { tp: 8, ep: 8, conc-start: 1, conc-end: 256, spec-decoding: mtp }
+      - { tp: 4, conc-start: 1, conc-end: 64, spec-decoding: mtp }
+      - { tp: 4, ep: 4, conc-start: 64, conc-end: 256, spec-decoding: mtp }
+      - { tp: 8, ep: 8, dp-attn: true, conc-start: 256, conc-end: 512, spec-decoding: mtp }
+    - isl: 8192
+      osl: 1024
+      search-space:
+      - { tp: 8, conc-start: 1, conc-end: 64, spec-decoding: mtp }
+      - { tp: 8, ep: 8, conc-start: 1, conc-end: 256, spec-decoding: mtp }
+      - { tp: 4, conc-start: 1, conc-end: 64, spec-decoding: mtp }
+      - { tp: 8, ep: 8, dp-attn: true, conc-start: 128, conc-end: 256, spec-decoding: mtp }
diff --git a/benchmarks/single_node/fixed_seq_len/minimaxm3_fp8_mi355x_mtp.sh b/benchmarks/single_node/fixed_seq_len/minimaxm3_fp8_mi355x_mtp.sh
new file mode 100644
index 000000000..b7335c9fc
--- /dev/null
+++ b/benchmarks/single_node/fixed_seq_len/minimaxm3_fp8_mi355x_mtp.sh
@@ -0,0 +1,124 @@
+#!/usr/bin/env bash
+
+# MiniMax-M3 MXFP8 MI355X (gfx950) single-node vLLM recipe with EAGLE3
+# speculative decoding — the spec-decoding=mtp variant of
+# minimaxm3_fp8_mi355x.sh. Adds the Inferact/MiniMax-M3-EAGLE3 draft head via
+# --speculative-config with 3 speculative tokens. Everything else mirrors the
+# non-MTP recipe: MXFP8 from TP=4 on gfx950, mandatory --block-size 128,
+# --language-model-only for the text-only benchmark, FP8 KV cache,
+# --attention-backend TRITON_ATTN, and --enforce-eager.
+#
+# Unlike the CUDA recipes, the drafter needs no attention_backend override:
+# the FlashInfer "page size 128 requires GQA/MQA" limitation that forced
+# FLASH_ATTN for the EAGLE3 MHA head on Blackwell is FlashInfer/CUDA-specific.
+# Here the whole server runs on TRITON_ATTN (set globally below), which serves
+# the MHA draft fine.
+#
+# KNOWN BLOCKER (2026-06-13): this recipe does NOT yet run on the current
+# vllm/vllm-openai-rocm:minimax-m3 image. Engine init fails with
+# "RuntimeError: Model does not support EAGLE3 interface but
+# aux_hidden_state_outputs was requested" — the ROCm build's
+# MiniMaxM3SparseForConditionalGeneration class does not implement vLLM's
+# SupportsEagle3 aux-hidden-state hook. The CUDA minimax-m3 image (a newer
+# vLLM commit) does, which is why the B300/B200/H100/H200 EAGLE3 recipes pass.
+# Confirmed independent of --trust-remote-code (sweeps 27472217773 /
+# 27472704212). The recipe is otherwise correct and should pass once the ROCm
+# image is rebuilt with MiniMax-M3 EAGLE3 target support.
+
+source "$(dirname "$0")/../../benchmark_lib.sh"
+
+check_env_vars \
+    MODEL \
+    TP \
+    EP_SIZE \
+    DP_ATTENTION \
+    CONC \
+    ISL \
+    OSL \
+    MAX_MODEL_LEN \
+    RANDOM_RANGE_RATIO \
+    RESULT_FILENAME
+
+DRAFT_MODEL="Inferact/MiniMax-M3-EAGLE3"
+
+if [[ -n "$SLURM_JOB_ID" ]]; then
+  echo "JOB $SLURM_JOB_ID running on $SLURMD_NODENAME"
+fi
+
+# MODEL stays a bare HF id on the mi355x single-node runner (weights are
+# pre-staged in the mounted NFS HF cache, so this is a fast cache hit). The
+# EAGLE3 draft is not staged; fetch it into the same cache.
+if [[ "$MODEL" != /* ]]; then
+  hf download "$MODEL"
+  hf download "$DRAFT_MODEL"
+fi
+
+if [ -n "$ROCR_VISIBLE_DEVICES" ]; then
+    export HIP_VISIBLE_DEVICES="$ROCR_VISIBLE_DEVICES"
+fi
+
+SERVER_LOG=/workspace/server.log
+export VLLM_ENGINE_READY_TIMEOUT_S=3600
+
+if [ "${EVAL_ONLY}" = "true" ]; then
+    setup_eval_context
+fi
+
+PARALLEL_ARGS=(--tensor-parallel-size "$TP")
+if [ "${DP_ATTENTION}" = "true" ]; then
+    PARALLEL_ARGS=(
+        --tensor-parallel-size 1
+        --data-parallel-size "$TP"
+        --enable-expert-parallel
+    )
+elif [ "$EP_SIZE" -gt 1 ]; then
+    PARALLEL_ARGS+=(--enable-expert-parallel)
+fi
+
+# use 3 speculative tokens for all configs for now
+NUM_SPEC_TOKENS=3
+
+start_gpu_monitor
+
+set -x
+vllm serve "$MODEL" --port "$PORT" \
+    "${PARALLEL_ARGS[@]}" \
+    --block-size 128 \
+    --language-model-only \
+    --max-model-len "$MAX_MODEL_LEN" \
+    --kv-cache-dtype fp8 \
+    --attention-backend TRITON_ATTN \
+    --enforce-eager \
+    --speculative-config "{\"method\": \"eagle3\", \"model\": \"$DRAFT_MODEL\", \"num_speculative_tokens\": $NUM_SPEC_TOKENS}" \
+    --tool-call-parser minimax_m3 \
+    --reasoning-parser minimax_m3 \
+    --enable-auto-tool-choice > "$SERVER_LOG" 2>&1 &
+
+SERVER_PID=$!
+wait_for_server_ready --port "$PORT" --server-log "$SERVER_LOG" --server-pid "$SERVER_PID"
+
+pip install -q datasets pandas
+
+# Spec-decode acceptance rate degrades on raw random tokens; route prompts
+# through the chat template as the other MTP recipes do.
+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 \
+    --use-chat-template
+
+if [ "${RUN_EVAL}" = "true" ]; then
+    run_eval --framework lm-eval --port "$PORT"
+    append_lm_eval_summary
+fi
+
+stop_gpu_monitor
+set +x
diff --git a/perf-changelog.yaml b/perf-changelog.yaml
index 155da5483..23bea2235 100644
--- a/perf-changelog.yaml
+++ b/perf-changelog.yaml
@@ -3717,3 +3717,13 @@
     - "B300-parity layouts and concurrency ranges: TP8, TP8+EP8, TP4, TP4+EP4, TP2+EP2, and TP8+EP8 dp-attn (DEP) across 1k1k and 8k1k"
     - "launch_mi355x-amds.sh routes M3 weights to NFS /it-share/hf-hub-cache instead of node-local /var/lib NVMe"
   pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/1725
+
+- config-keys:
+    - minimaxm3-fp8-mi355x-vllm-mtp
+  description:
+    - "Initial submission: MiniMax-M3 MXFP8 MI355X (gfx950) vLLM benchmark with EAGLE3 speculative decoding (target: MiniMaxAI/MiniMax-M3-MXFP8, draft: Inferact/MiniMax-M3-EAGLE3, 3 speculative tokens) — spec-decoding=mtp variant of the MI355X day-zero recipe"
+    - "Image: vllm/vllm-openai-rocm:minimax-m3 (same day-zero ROCm build as the non-MTP entry)"
+    - "Serve shape follows minimaxm3-fp8-mi355x-vllm (--block-size 128, --language-model-only, --kv-cache-dtype fp8, --attention-backend TRITON_ATTN, --enforce-eager, minimax_m3 parsers); prompts routed through the chat template for realistic acceptance"
+    - "No attention_backend override on the drafter: the server runs on TRITON_ATTN, so the FlashInfer page-128/MHA limitation that forced FLASH_ATTN on the CUDA recipes does not apply on ROCm"
+    - "Layouts: TP8 / TP4 (latency), TP8+EP8 / TP4+EP4 (TEP), TP8+EP8 dp-attn (DEP) across 1k1k and 8k1k — non-MTP search space trimmed at the extreme-concurrency end, tp2-ep2 dropped, mirroring the minimaxm3-fp8-b300-vllm-mtp search space"
+  pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/1742