MiniMax-M2

1 Introduction

MiniMax-M2 is MiniMax's flagship large language model series, including MiniMax-M2.5 and MiniMax-M2.7. It is reinforced for high-value scenarios such as code generation, agentic tool calling/search, and complex office workflows, with an emphasis on reasoning efficiency and end-to-end speed on challenging tasks.

This document will show the main verification steps for both MiniMax-M2.5 and MiniMax-M2.7, including supported features, feature configuration, environment preparation, single-node and multi-node deployment, accuracy and performance evaluation.

This document is written based on the latest vLLM-Ascend version. Both MiniMax-M2.5 and MiniMax-M2.7 are fully supported. To use the latest features (e.g., PD separation, EAGLE3 speculative decoding), it is recommended to use the latest version.

2 Supported Features

Refer to supported features to get the model's supported feature matrix.

Refer to feature guide to get the feature's configuration.

3 Prerequisites

3.1 Model Weight

The following model weights and EAGLE3 weights are available on ModelScope. Search for the corresponding model name on ModelScope to obtain the latest weight files.

Model	Description	Recommended Hardware	Source
MiniMax-M2.7-w8a8-QuaRot	M2.7 W8A8 quantized version	1× Atlas 800 A3 (64G × 16) or 1× Atlas 800I A2 (64G × 8)	MiniMax-M2.7-w8a8-QuaRot
MiniMax-M2.5-w8a8-QuaRot	M2.5 W8A8 quantized version	1× Atlas 800 A3 (64G × 16) or 1× Atlas 800I A2 (64G × 8)	MiniMax-M2.5-w8a8-QuaRot
MiniMax-M2.7-w8a8c8-QuaRot	M2.7 W8A8C8 quantized version	1× Atlas 800 A3 (64G × 16) or 1× Atlas 800I A2 (64G × 8)	MiniMax-M2.7-w8a8c8-QuaRot
Eagle3 (M2.7)	M2.7 speculative decoding head model	Matches the base model node count	MiniMax-M2.7-eagle-model
Eagle3 (M2.5)	M2.5 speculative decoding head model	Matches the base model node count	MiniMax-M2.5-eagle-model

It is recommended to download the model weights to a shared directory, such as /root/.cache/.

3.2 Verify Multi-node Communication (Optional)

If you need to deploy a multi-node environment, verify the multi-node communication according to Verify Multi-node Communication Environment.

4 Installation

4.1 Docker Image Installation

Select an image based on your machine type and start the container on your node. For the available image tags and published versions, refer to Using Docker.

A3 series

# Update the vllm-ascend image
export IMAGE=m.daocloud.io/quay.io/ascend/vllm-ascend:v0.21.0rc1
export NAME=vllm-ascend

# Run the container using the defined variables
# Note: If you are running bridge network with docker, please expose available ports for multiple nodes communication in advance
docker run --rm \
--name $NAME \
--net=host \
--shm-size=1g \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--device /dev/davinci4 \
--device /dev/davinci5 \
--device /dev/davinci6 \
--device /dev/davinci7 \
--device /dev/davinci8 \
--device /dev/davinci9 \
--device /dev/davinci10 \
--device /dev/davinci11 \
--device /dev/davinci12 \
--device /dev/davinci13 \
--device /dev/davinci14 \
--device /dev/davinci15 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-it $IMAGE bash

A2 series

Map your model weight directory into the container (the example maps it to /root/.cache/).

#!/bin/sh
NAME=minimax
IMAGE=m.daocloud.io/quay.io/ascend/vllm-ascend:|vllm_ascend_version|

docker run -itd -u 0 --ipc=host \
  -e VLLM_USE_MODELSCOPE=True \
  -e PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256 \
  --name $NAME \
  --net=host \
  --device /dev/davinci_manager \
  --device /dev/devmm_svm \
  --device /dev/hisi_hdc \
  --device /dev/davinci0 \
  --device /dev/davinci1 \
  --device /dev/davinci2 \
  --device /dev/davinci3 \
  --device /dev/davinci4 \
  --device /dev/davinci5 \
  --device /dev/davinci6 \
  --device /dev/davinci7 \
  --shm-size=1200g \
  -v /usr/local/dcmi:/usr/local/dcmi \
  -v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
  -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
  -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
  -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
  -v /etc/ascend_install.info:/etc/ascend_install.info \
  -v /root/.cache:/root/.cache \
  -it $IMAGE bash

Save the script as minimax-docker-run.sh, then start and enter the container:

bash minimax-docker-run.sh
docker exec -it minimax bash

Verification:

After starting the container, verify the installation with:

# Check that the container is running
docker ps | grep $NAME

# Verify that NPU devices are visible inside the container
docker exec $NAME npu-smi info

Expected result: docker ps shows the container with status "Up", and npu-smi info lists the expected number of NPU devices.

4.2 Source Code Installation

If you prefer to build from source instead of using the Docker image, install vLLM-Ascend following the Installation Guide.

To verify the source installation:

python -c "import vllm_ascend; print(vllm_ascend.__version__)"

5 Online Service Deployment

备注

In this tutorial, we assume you have downloaded the model weights. Replace /path/to/weight/ with your actual model weight path.

5.1 Single-Node Online Deployment

Single-node deployment completes both Prefill and Decode within the same node, suitable for development, testing, and low-to-medium throughput production scenarios.

Common Issues Tip: If you encounter OOM, HCCL port conflicts, or other startup issues, please refer to the Public FAQ for troubleshooting. For MiniMax-specific issues, refer to Chapter 10 FAQ.

A3 (single node)

Below is a recommended startup configuration for short-context conditions (e.g., 3.5k input / 1.5k output) to achieve good performance.

Notes:

• If you only care about short-context low latency, you can set --max-model-len 32768, --tensor-parallel-size 4, and --data-parallel-size 4.

export HCCL_OP_EXPANSION_MODE="AIV"
export HCCL_BUFFSIZE=1024
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export OMP_NUM_THREADS=1
echo performance | tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
sysctl -w vm.swappiness=0
sysctl -w kernel.numa_balancing=0
sysctl kernel.sched_migration_cost_ns=50000
export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
export TASK_QUEUE_ENABLE=1

export VLLM_ASCEND_BALANCE_SCHEDULING=0

vllm serve /path/to/weight/MiniMax-M2.7-w8a8-QuaRot \
    --served-model-name "MiniMax-M2.7" \
    --host 0.0.0.0 \
    --port 8000 \
    --trust-remote-code \
    --quantization ascend \
    --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
    --async-scheduling \
    --additional-config '{"enable_cpu_binding":true,
                          "enable_fused_mc2":true,
                          "enable_flashcomm1":true,
                          "weight_nz_mode":true}' \
    --enable-expert-parallel \
    --tensor-parallel-size 4 \
    --data-parallel-size 4 \
    --max-num-seqs 48 \
    --max-model-len 40690 \
    --max-num-batched-tokens 16384 \
    --gpu-memory-utilization 0.85 \
    --speculative_config '{"enforce_eager": true, "method": "eagle3", "model": "/path/to/weight/Eagle3/", "num_speculative_tokens": 3}'

Remarks:

• minimax_m2_append_think keeps <think>...</think> inside content.

• If you mainly rely on the reasoning semantics of /v1/responses, it is recommended to use --reasoning-parser minimax_m2 instead.

• To achieve better performance on long-context scenarios (e.g., 128k or 64k), we recommend the following adjustments:

    --tensor-parallel-size 8 \
    --data-parallel-size 1 \
    --decode-context-parallel-size 1 \
    --prefill-context-parallel-size 2 \
    --cp-kv-cache-interleave-size 128 \
    --max-num-seqs 16 \
    --max-model-len 138000 \
    --max-num-batched-tokens 65536 \
    --gpu-memory-utilization 0.85 \
    --speculative_config '{"enforce_eager": true, "method": "eagle3", "model": "/path/to/weight/Eagle3/", "num_speculative_tokens": 1}'

Note: The above parameters are validated in a specific test environment for reference only. Please adjust --max-model-len, --max-num-seqs, --max-num-batched-tokens, and --gpu-memory-utilization based on your actual input/output length, concurrency, and hardware configuration.

• If you need to test with curl and tool calling, add the following to the startup command:

    --enable-auto-tool-choice \
    --tool-call-parser minimax_m2 \
    --reasoning-parser minimax_m2_append_think \

A2 (single node)

export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
export HCCL_OP_EXPANSION_MODE="AIV"
export HCCL_BUFFSIZE=512
sysctl -w vm.swappiness=0
sysctl -w kernel.numa_balancing=0
sysctl kernel.sched_migration_cost_ns=50000
export TASK_QUEUE_ENABLE=1
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export HCCL_INTRA_PCIE_ENABLE=1
export HCCL_INTRA_ROCE_ENABLE=0
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=1

vllm serve /path/to/weight/MiniMax-M2.7-w8a8-QuaRot \
    --served-model-name MiniMax-M2.7 \
    --host 0.0.0.0 \
    --port 8000 \
    --trust-remote-code \
    --tensor-parallel-size 8 \
    --quantization ascend \
    --enable-expert-parallel \
    --max-num-seqs 32 \
    --seed 1024 \
    --max-num-batched-tokens 32768 \
    --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
    --gpu-memory-utilization 0.85 \
    --additional-config '{"enable_cpu_binding":true,
                          "enable_flashcomm1":true}' \
    --model-loader-extra-config '{"enable_multithread_load":true,"num_threads":16}' \
    --speculative_config '{"method": "eagle3", "model": "/path/to/weight/Eagle3/",  "num_speculative_tokens":3}'

Note: The above parameters are validated in a specific test environment for reference only. Please adjust --max-model-len, --max-num-seqs, --max-num-batched-tokens, and --gpu-memory-utilization based on your actual input/output length, concurrency, and hardware configuration.

• If you need to test with curl and tool calling, add the following to the startup command:

    --enable-auto-tool-choice \
    --tool-call-parser minimax_m2 \
    --reasoning-parser minimax_m2_append_think \

5.2 Multi-Node PD Separation Deployment

PD (Prefill-Decode) separation splits the Prefill and Decode phases across different nodes for better throughput. The following 1P1D configuration is validated for 128k input/output scenarios with MiniMax-M2.7-W8A8.

Hardware: 2× Atlas 800 A3 (64G × 16), one for Prefill, one for Decode.

Common Issues Tip: For PD separation specific issues such as KV transfer timeouts or Mooncake connection errors, please refer to the Public FAQ. For MiniMax-specific PD separation issues, refer to Chapter 10 FAQ.

First, prepare launch_online_dp.py on each node:

import argparse
import multiprocessing
import os
import subprocess
import sys

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--dp-size", type=int, required=True)
    parser.add_argument("--tp-size", type=int, default=1)
    parser.add_argument("--dp-size-local", type=int, default=-1)
    parser.add_argument("--dp-rank-start", type=int, default=0)
    parser.add_argument("--dp-address", type=str, required=True)
    parser.add_argument("--dp-rpc-port", type=str, default=12345)
    parser.add_argument("--vllm-start-port", type=int, default=9000)
    return parser.parse_args()

args = parse_args()
dp_size, tp_size = args.dp_size, args.tp_size
dp_size_local = args.dp_size_local if args.dp_size_local != -1 else dp_size

def run_command(visible_devices, dp_rank, vllm_engine_port):
    subprocess.run([
        "bash", "./run_dp_template.sh",
        visible_devices, str(vllm_engine_port),
        str(dp_size), str(dp_rank), args.dp_address,
        args.dp_rpc_port, str(tp_size),
    ], check=True)

if __name__ == "__main__":
    for i in range(dp_size_local):
        dp_rank = args.dp_rank_start + i
        vllm_port = args.vllm_start_port + i
        visible_devices = ",".join(str(x) for x in range(i * tp_size, (i + 1) * tp_size))
        p = multiprocessing.Process(target=run_command, args=(visible_devices, dp_rank, vllm_port))
        p.start()
        p.join()

Then prepare run_dp_template.sh on each node.

Prefill node (set nic_name and local_ip to your own):

unset http_proxy https_proxy ftp_proxy

nic_name="<your_nic_name>"
local_ip="<your_ip>"

export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name

export HCCL_BUFFSIZE=1024
export HCCL_OP_EXPANSION_MODE="AIV"
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export OMP_NUM_THREADS=1
echo performance | tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
sysctl -w vm.swappiness=0
sysctl -w kernel.numa_balancing=0
sysctl kernel.sched_migration_cost_ns=50000
export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages/mooncake:$LD_LIBRARY_PATH

export TASK_QUEUE_ENABLE=1
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1
export VLLM_ASCEND_ENABLE_FUSED_MC2=1
export PYTHONHASHSEED=0

export ASCEND_RT_VISIBLE_DEVICES=$1

vllm serve /path/to/weight/MiniMax-M2.7-w8a8-QuaRot \
    --host 0.0.0.0 \
    --port $2 \
    --data-parallel-size $3 \
    --data-parallel-rank $4 \
    --data-parallel-address $5 \
    --data-parallel-rpc-port $6 \
    --tensor-parallel-size $7 \
    --enable-expert-parallel \
    --served-model-name minimax \
    --max-model-len 200000 \
    --max-num-batched-tokens 16384 \
    --max-num-seqs 64 \
    --trust-remote-code \
    --gpu-memory-utilization 0.85 \
    --quantization ascend \
    --enforce-eager \
    --speculative_config '{"method": "eagle3", "model": "/path/to/weight/Eagle3/", "num_speculative_tokens": 3}' \
    --additional-config '{"enable_cpu_binding":true}' \
    --kv-transfer-config \
        '{"kv_connector": "MooncakeConnectorV1",
        "kv_role": "kv_producer",
        "kv_port": "35880",
        "engine_id": "0",
        "kv_connector_extra_config": {
             "use_ascend_direct": true,
             "prefill": {"dp_size": 2, "tp_size": 8},
             "decode":  {"dp_size": 2, "tp_size": 8}
        }}'

Decode node (set nic_name and local_ip to your own):

unset http_proxy https_proxy ftp_proxy

nic_name="<your_nic_name>"
local_ip="<your_ip>"

export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name

export HCCL_BUFFSIZE=2048
export HCCL_OP_EXPANSION_MODE="AIV"
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export OMP_NUM_THREADS=1
echo performance | tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
sysctl -w vm.swappiness=0
sysctl -w kernel.numa_balancing=0
sysctl kernel.sched_migration_cost_ns=50000
export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages/mooncake:$LD_LIBRARY_PATH

export TASK_QUEUE_ENABLE=1
export VLLM_ASCEND_ENABLE_FLASHCOMM1=0
export VLLM_ASCEND_ENABLE_FUSED_MC2=1
export PYTHONHASHSEED=0

export ASCEND_RT_VISIBLE_DEVICES=$1

vllm serve /path/to/weight/MiniMax-M2.7-w8a8-QuaRot \
    --host 0.0.0.0 \
    --port $2 \
    --data-parallel-size $3 \
    --data-parallel-rank $4 \
    --data-parallel-address $5 \
    --data-parallel-rpc-port $6 \
    --tensor-parallel-size $7 \
    --enable-expert-parallel \
    --served-model-name minimax \
    --max-model-len 200000 \
    --max-num-batched-tokens 16384 \
    --max-num-seqs 16 \
    --trust-remote-code \
    --no-enable-prefix-caching \
    --gpu-memory-utilization 0.85 \
    --quantization ascend \
    --async-scheduling \
    --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY"}' \
    --speculative_config '{"method": "eagle3", "model": "/path/to/weight/Eagle3/", "num_speculative_tokens": 3}' \
    --additional-config '{"enable_cpu_binding":true}' \
    --kv-transfer-config \
        '{"kv_connector": "MooncakeConnectorV1",
        "kv_role": "kv_consumer",
        "kv_port": "56900",
        "engine_id": "1",
        "kv_connector_extra_config": {
             "use_ascend_direct": true,
             "prefill": {"dp_size": 2, "tp_size": 8},
             "decode":  {"dp_size": 2, "tp_size": 8}
        }}'

Once the scripts are ready, start the servers on each node.

Prefill node:

python launch_online_dp.py \
    --dp-size 2 --tp-size 8 \
    --dp-size-local 2 --dp-rank-start 0 \
    --dp-address <prefill_ip> --dp-rpc-port 12321 \
    --vllm-start-port 7000

Decode node:

python launch_online_dp.py \
    --dp-size 2 --tp-size 8 \
    --dp-size-local 2 --dp-rank-start 0 \
    --dp-address <decode_ip> --dp-rpc-port 12321 \
    --vllm-start-port 7100

Request Forwarding

Run the proxy on any machine that can reach both nodes. You can get the proxy script from the repository: load_balance_proxy_server_example.py.

unset http_proxy https_proxy

python load_balance_proxy_server_example.py \
    --port 8009 \
    --host <prefill_ip> \
    --prefiller-hosts \
       <prefill_ip> <prefill_ip> \
    --prefiller-ports \
       7000 7001 \
    --decoder-hosts \
       <decode_ip> <decode_ip> \
    --decoder-ports \
       7100 7101

The service is then accessible at http://<proxy_ip>:8009.

6 Functional Verification

Once your server is started, you can query the model with input prompts.

Note:

• <node_ip>: The IP address of the node where the server is running (e.g., localhost for single-node).

• <port>: The port number specified in the server startup command (e.g., 8000).

Using curl

curl http://<node_ip>:<port>/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "MiniMax-M2.7",
    "messages": [{"role": "user", "content": "Hello, who are you?"}],
    "stream": false,
    "temperature": 0.8,
    "max_tokens": 200
  }'

Expected result: HTTP 200 with a JSON response containing a choices field with the model's reply text.

Using OpenAI Python Client

from openai import OpenAI

client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="na")

resp = client.chat.completions.create(
    model="MiniMax-M2.7",
    messages=[{"role": "user", "content": "你好，请介绍一下你自己，并展示一次工具调用的参数格式。"}],
    max_tokens=256,
)
print(resp.choices[0].message.content)

Expected result: The response should contain a coherent self-introduction and tool call parameter format in the content field.

Tool Calling Verification

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "MiniMax-M2.7",
    "messages": [{"role": "user", "content": "请查询上海的天气。"}],
    "tools": [{
      "type": "function",
      "function": {
        "name": "get_current_weather",
        "description": "Get weather by city",
        "parameters": {
          "type": "object",
          "properties": {
            "city": {"type": "string"},
            "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
          },
          "required": ["city"]
        }
      }
    }],
    "tool_choice": "auto",
    "temperature": 0,
    "max_tokens": 512
  }'

Expected result: HTTP 200 with a JSON response containing a tool_calls field with the function name and arguments.

7 Accuracy Evaluation

Note: Post-processing parameters (e.g., max_tokens, temperature, stop tokens) should match those defined in the model weight's generation_config.json. The recommended maximum output length for GPQA-diamond and AIME2025 is 64k (65536 tokens).

Here are two accuracy evaluation methods.

7.1 Using AISBench

For details, please refer to Using AISBench.

7.2 Using Language Model Evaluation Harness

Using the gsm8k dataset as an example test dataset, run the accuracy evaluation for MiniMax-M2.7-W8A8 in online mode.

1. For lm_eval installation, please refer to Using lm_eval.

2. Run lm_eval to execute the accuracy evaluation:

lm_eval \
  --model local-completions \
  --model_args model=/path/to/weight/MiniMax-M2.7-w8a8-QuaRot,base_url=http://127.0.0.1:8000/v1/completions,tokenized_requests=False,trust_remote_code=True \
  --tasks gsm8k \
  --output_path ./

8 Performance Evaluation

8.1 Using AISBench

Refer to Using AISBench for performance evaluation for details.

8.2 Using vLLM Benchmark

Run performance evaluation for MiniMax-M2.7-W8A8 as an example.

Refer to vllm benchmark for more details.

Take the serve subcommand as an example:

export VLLM_USE_MODELSCOPE=True
vllm bench serve \
  --model /path/to/weight/MiniMax-M2.7-w8a8-QuaRot \
  --dataset-name random \
  --random-input 200 \
  --num-prompts 200 \
  --request-rate 1 \
  --save-result \
  --result-dir ./

9 Performance Tuning

Note: The following configurations are validated in specific test environments and are for reference only. The optimal configuration depends on factors such as maximum input/output length, prefix cache hit rate, precision requirements, and deployment machine ratios. It is recommended to refer to Section 9.2 for tuning based on actual conditions.

9.1 Recommended Configurations

The following configurations are validated on the self-test report (AR20260326132822) and are categorized by use case.

Scenario	Input/Output	Deployment	NPUs	P Config	D Config	Max Batched Tokens	Max Num Seqs (P/D)	Max Model Len	EAGLE3	FUSED_MC2	FlashComm1	Async Scheduling
Short Seq High Throughput	3.5K → 1.5K	1P2D PD separation	24 (A3)	DP8TP2EP16	DP32TP1EP32	16384	128 / 128	32k	3	On	On	On
Short Seq Low Latency	3.5K → 1.5K	1P2D PD separation	24 (A3)	DP4TP4EP16	DP8TP4EP32	16384	128 / 128	32k	3	On	On	On
Long Seq High Throughput	128K → 1K （90% cache hit）	1P1D PD separation	16 (A3)	DP2TP8EP16	DP2TP8EP16	16384	64 / 16	200k	3	On	On	On
Long Seq Low Latency	128K → 1K （90% cache hit）	1P2D PD separation	24 (A3)	DP2TP8EP16	DP4TP8EP32	16384	64 / 16	200k	3	On	On	On

Note: The prefix cache hit rate for short-sequence tests is 0%; for long-sequence tests it is 90%. Adjust max-num-seqs, max-model-len, and max-num-batched-tokens based on your actual workload.

9.2 Tuning Guidelines

9.2.1 General Tuning Reference

Please refer to the Public Performance Tuning Documentation for general tuning methods.

Please refer to the Feature Guide for detailed feature descriptions.

9.2.2 Model-Specific Optimizations

Optimizations Enabled by Default

The following optimizations are enabled by default and require no additional configuration:

Optimization Technique	Technical Principle	Performance Benefit
FullGraph Optimization	Captures and replays the entire decoding graph at once using compilation_config={"cudagraph_mode":"FULL_DECODE_ONLY"}	Significantly reduces scheduling latency, stabilizes multi-device performance
CPU Binding	Uses --additional-config '{"enable_cpu_binding":true}' to bind CPU cores	Reduces cross-core scheduling overhead, improving decode latency stability
Multi-thread Weight Loading	Uses --model-loader-extra-config '{"enable_multithread_load":true}' for parallel weight loading	Reduces model loading time

Optimizations That Require Explicit Enabling

Optimization Technique	Applicable Scenarios	Enablement Method	Technical Principle	Precautions
FlashComm v1	High-concurrency, TP scenarios	--additional-config '{"enable_flashcomm1": true}'	Decomposes traditional Allreduce into Reduce-Scatter and All-Gather	Threshold protection: only takes effect when the actual number of tokens exceeds the threshold
Fused MC2	TP ≥ 4 scenarios	--additional-config '{"enable_fused_mc2": true}'	Fuses multiple communication and computation operations	Recommended for A3; not applicable for A2
Balanced Scheduling	High DP scenarios	export VLLM_ASCEND_BALANCE_SCHEDULING=1	Enhances scheduling capacity between prefill and decode	Currently disabled by default (0). Set to 1 only when concurrency ≈ DP × max-num-seqs. Disable for long-context scenarios
EAGLE3 Speculative Decoding	All scenarios	--speculative_config '{"method": "eagle3", "model": "/path/to/Eagle3/", "num_speculative_tokens": 3}'	Uses a draft model to predict future tokens	1–3 tokens for long context; 3 tokens for short context
jemalloc Preload	All scenarios	export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2	Replaces default memory allocator to reduce fragmentation	Ensure jemalloc is installed in the container

10 FAQ

For common environment, installation, and general parameter issues, please refer to the Public FAQ. This chapter only covers MiniMax-M2 (M2.5/M2.7) model-specific issues.

• Q: Does C8 quantization support EAGLE3 speculative decoding?

  A: Not yet. C8 quantization with EAGLE3 is currently unsupported.

• Q: Which --reasoning-parser is recommended for tool calling tasks?

  A: For tool calling tasks, it is recommended to use --reasoning-parser minimax_m2_append_think.

• Q: Why is the reasoning field often empty after using minimax_m2_append_think?

  A: This is expected. The parser keeps <think>...</think> inside content. If you mainly rely on the reasoning semantics of /v1/responses, use --reasoning-parser minimax_m2 instead.

• Q: Startup fails with HCCL port conflicts (address already bound). What should I do?

  A: Check whether another process is already occupying the port (e.g., lsof -i :<port> or ss -tlnp | grep <port>). If a port conflict is found, switch to a different port with --port, or terminate the specific process occupying that port.

• Q: How to handle OOM or unstable startup?

  A: Refer to the upstream vLLM guide on out-of-memory troubleshooting. In short: reduce --max-num-seqs and --max-num-batched-tokens first, lower --gpu-memory-utilization (e.g., from 0.9 to 0.85), or decrease the number of concurrent requests.

• Q: How should I choose --reasoning-parser?

  A: This guide uses minimax_m2_append_think so that <think>...</think> is kept in content. If you mainly rely on the reasoning semantics of /v1/responses, consider using --reasoning-parser minimax_m2.

• Q: Which ports must be accessible?

  A: At minimum, expose the serving port (e.g., 8000). For multi-node deployment, also ensure HCCL communication ports and DP RPC ports are accessible.