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meihanc c1dcddce3f [CI]update bisheng version (#5621) 
### What this PR does / why we need it?
update bisheng version in 20260105

- vLLM version: v0.13.0
- vLLM main:
8be6432bda

Signed-off-by: Meihan-chen <jcccx.cmh@gmail.com>
2026-01-06 15:22:22 +08:00

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DeepSeek-V3.2

Introduction

DeepSeek-V3.2 is a sparse attention model. The main architecture is similar to DeepSeek-V3.1, but with a sparse attention mechanism, which is designed to explore and validate optimizations for training and inference efficiency in long-context scenarios.

This document will show the main verification steps of the model, including supported features, feature configuration, environment preparation, single-node and multi-node deployment, accuracy and performance evaluation.

Supported Features

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

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

Environment Preparation

Model Weight

  • DeepSeek-V3.2-Exp(BF16 version): require 2 Atlas 800 A3 (64G × 16) nodes or 4 Atlas 800 A2 (64G × 8) nodes. Download model weight
  • DeepSeek-V3.2-Exp-w8a8(Quantized version): require 1 Atlas 800 A3 (64G × 16) node or 2 Atlas 800 A2 (64G × 8) nodes. Download model weight
  • DeepSeek-V3.2(BF16 version): require 2 Atlas 800 A3 (64G × 16) nodes or 4 Atlas 800 A2 (64G × 8) nodes. Model weight in BF16 not found now.
  • DeepSeek-V3.2-w8a8(Quantized version): require 1 Atlas 800 A3 (64G × 16) node or 2 Atlas 800 A2 (64G × 8) nodes. Download model weight

It is recommended to download the model weight to the shared directory of multiple nodes, such as /root/.cache/

Verify Multi-node Communication(Optional)

If you want to deploy multi-node environment, you need to verify multi-node communication according to verify multi-node communication environment.

Installation

You can using our official docker image to run DeepSeek-V3.2 directly..

:::{note} We strongly recommend you to install triton ascend package to speed up the inference.

The Triton Ascend is for better performance, please follow the instructions below to install it and its dependency.

Install the Ascend BiSheng toolkit, execute the command:

BISHENG_NAME="Ascend-BiSheng-toolkit_$(uname -i)_20260105.run"
BISHENG_URL="https://vllm-ascend.obs.cn-north-4.myhuaweicloud.com/vllm-ascend/${BISHENG_NAME}"
wget -O "${BISHENG_NAME}" "${BISHENG_URL}" && chmod a+x "${BISHENG_NAME}" && "./${BISHENG_NAME}" --install && rm "${BISHENG_NAME}"
export PATH=/usr/local/Ascend/tools/bishengir/bin:$PATH

Install Triton Ascend:

python3 -m pip install -i https://test.pypi.org/simple/ triton-ascend==3.2.0.dev20260105

:::

:::::{tab-set} :sync-group: install

::::{tab-item} A3 series :sync: A3

Start the docker image on your each node.

   :substitutions:

export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-a3
docker run --rm \
    --name vllm-ascend \
    --shm-size=1g \
    --net=host \
    --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

:::: ::::{tab-item} A2 series :sync: A2

Start the docker image on your each node.

   :substitutions:

export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
    --name vllm-ascend \
    --shm-size=1g \
    --net=host \
    --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/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

:::: :::::

In addition, if you don't want to use the docker image as above, you can also build all from source:

If you want to deploy multi-node environment, you need to set up environment on each node.

Deployment

:::{note} In this tutorial, we suppose you downloaded the model weight to /root/.cache/. Feel free to change it to your own path. :::

Prefill-Decode Disaggregation

We'd like to show the deployment guide of DeepSeek-V3.2 on multi-node environment with 1P1D for better performance.

Before you start, please

  1. prepare the script 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,
        help="Data parallel size."
    )
    parser.add_argument(
        "--tp-size",
        type=int,
        default=1,
        help="Tensor parallel size."
    )
    parser.add_argument(
        "--dp-size-local",
        type=int,
        default=-1,
        help="Local data parallel size."
    )
    parser.add_argument(
        "--dp-rank-start",
        type=int,
        default=0,
        help="Starting rank for data parallel."
    )
    parser.add_argument(
        "--dp-address",
        type=str,
        required=True,
        help="IP address for data parallel master node."
    )
    parser.add_argument(
        "--dp-rpc-port",
        type=str,
        default=12345,
        help="Port for data parallel master node."
    )
    parser.add_argument(
        "--vllm-start-port",
        type=int,
        default=9000,
        help="Starting port for the engine."
    )
    return parser.parse_args()

args = parse_args()
dp_size = args.dp_size
tp_size = args.tp_size
dp_size_local = args.dp_size_local
if dp_size_local == -1:
    dp_size_local = dp_size
dp_rank_start = args.dp_rank_start
dp_address = args.dp_address
dp_rpc_port = args.dp_rpc_port
vllm_start_port = args.vllm_start_port

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

if __name__ == "__main__":
    template_path = "./run_dp_template.sh"
    if not os.path.exists(template_path):
        print(f"Template file {template_path} does not exist.")
        sys.exit(1)

    processes = []
    num_cards = dp_size_local * tp_size
    for i in range(dp_size_local):
        dp_rank = dp_rank_start + i
        vllm_engine_port = vllm_start_port + i
        visiable_devices = ",".join(str(x) for x in range(i * tp_size, (i + 1) * tp_size))
        process = multiprocessing.Process(target=run_command,
                                        args=(visiable_devices, dp_rank,
                                                vllm_engine_port))
        processes.append(process)
        process.start()

    for process in processes:
        process.join()

  2. prepare the script run_dp_template.sh on each node.

    1. Prefill node 0

      nic_name="enp48s3u1u1" # change to your own nic name
local_ip=141.61.39.105 # change to your own ip

export HCCL_OP_EXPANSION_MODE="AIV"

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 OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=256

export VLLM_TORCH_PROFILER_DIR="./vllm_profile"
export VLLM_TORCH_PROFILER_WITH_STACK=0

export ASCEND_AGGREGATE_ENABLE=1
export ASCEND_TRANSPORT_PRINT=1
export ACL_OP_INIT_MODE=1
export ASCEND_A3_ENABLE=1
export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000

export ASCEND_RT_VISIBLE_DEVICES=$1

export VLLM_ASCEND_ENABLE_FLASHCOMM1=1


vllm serve /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-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 \
    --speculative-config '{"num_speculative_tokens": 2, "method":"deepseek_mtp"}' \
    --seed 1024 \
    --served-model-name dsv3 \
    --max-model-len 68000 \
    --max-num-batched-tokens 32550 \
    --trust-remote-code \
    --max-num-seqs 64 \
    --gpu-memory-utilization 0.82 \
    --quantization ascend \
    --enforce-eager \
    --no-enable-prefix-caching \
    --kv-transfer-config \
    '{"kv_connector": "MooncakeConnectorV1",
    "kv_role": "kv_producer",
    "kv_port": "30000",
    "engine_id": "0",
    "kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
    "kv_connector_extra_config": {
                "use_ascend_direct": true,
                "prefill": {
                        "dp_size": 2,
                        "tp_size": 16
                },
                "decode": {
                        "dp_size": 8,
                        "tp_size": 4
                }
        }
    }'

    2. Prefill node 1

      nic_name="enp48s3u1u1" # change to your own nic name
local_ip=141.61.39.113 # change to your own ip

export HCCL_OP_EXPANSION_MODE="AIV"

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 OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=256

export VLLM_TORCH_PROFILER_DIR="./vllm_profile"
export VLLM_TORCH_PROFILER_WITH_STACK=0

export ASCEND_AGGREGATE_ENABLE=1
export ASCEND_TRANSPORT_PRINT=1
export ACL_OP_INIT_MODE=1
export ASCEND_A3_ENABLE=1
export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000

export ASCEND_RT_VISIBLE_DEVICES=$1
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True

export VLLM_ASCEND_ENABLE_FLASHCOMM1=1


vllm serve /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-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 \
    --speculative-config '{"num_speculative_tokens": 2, "method":"deepseek_mtp"}' \
    --seed 1024 \
    --served-model-name dsv3 \
    --max-model-len 68000 \
    --max-num-batched-tokens 32550 \
    --trust-remote-code \
    --max-num-seqs 64 \
    --gpu-memory-utilization 0.82 \
    --quantization ascend \
    --enforce-eager \
    --no-enable-prefix-caching \
    --kv-transfer-config \
    '{"kv_connector": "MooncakeConnectorV1",
    "kv_role": "kv_producer",
    "kv_port": "30000",
    "engine_id": "0",
    "kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
    "kv_connector_extra_config": {
                "use_ascend_direct": true,
                "prefill": {
                        "dp_size": 2,
                        "tp_size": 16
                },
                "decode": {
                        "dp_size": 8,
                        "tp_size": 4
                }
        }
    }'

    3. Decode node 0

      nic_name="enp48s3u1u1" # change to your own nic name
local_ip=141.61.39.117 # change to your own ip

export HCCL_OP_EXPANSION_MODE="AIV"

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

#Mooncake
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10

export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=256

export VLLM_TORCH_PROFILER_DIR="./vllm_profile"
export VLLM_TORCH_PROFILER_WITH_STACK=0

export ASCEND_AGGREGATE_ENABLE=1
export ASCEND_TRANSPORT_PRINT=1
export ACL_OP_INIT_MODE=1
export ASCEND_A3_ENABLE=1
export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000

export TASK_QUEUE_ENABLE=1

export ASCEND_RT_VISIBLE_DEVICES=$1

export VLLM_ASCEND_ENABLE_MLAPO=1


vllm serve /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-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 \
    --speculative-config '{"num_speculative_tokens": 2, "method":"deepseek_mtp"}' \
    --seed 1024 \
    --served-model-name dsv3 \
    --max-model-len 68000 \
    --max-num-batched-tokens 12 \
    --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY", "cudagraph_capture_sizes":[3, 6, 9, 12]}' \
    --trust-remote-code \
    --max-num-seqs 4 \
    --gpu-memory-utilization 0.95 \
    --no-enable-prefix-caching \
    --async-scheduling \
    --quantization ascend \
    --kv-transfer-config \
    '{"kv_connector": "MooncakeConnectorV1",
    "kv_role": "kv_consumer",
    "kv_port": "30100",
    "engine_id": "1",
    "kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
    "kv_connector_extra_config": {
                "use_ascend_direct": true,
                "prefill": {
                        "dp_size": 2,
                        "tp_size": 16
                },
                "decode": {
                        "dp_size": 8,
                        "tp_size": 4
                }
        }
    }' \
    --additional-config '{"recompute_scheduler_enable" : true}'

    4. Decode node 1

      nic_name="enp48s3u1u1" # change to your own nic name
local_ip=141.61.39.181 # change to your own ip

export HCCL_OP_EXPANSION_MODE="AIV"

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

#Mooncake
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10

export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=256

export VLLM_TORCH_PROFILER_DIR="./vllm_profile"
export VLLM_TORCH_PROFILER_WITH_STACK=0

export ASCEND_AGGREGATE_ENABLE=1
export ASCEND_TRANSPORT_PRINT=1
export ACL_OP_INIT_MODE=1
export ASCEND_A3_ENABLE=1
export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000

export TASK_QUEUE_ENABLE=1

export ASCEND_RT_VISIBLE_DEVICES=$1

export VLLM_ASCEND_ENABLE_MLAPO=1


vllm serve /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-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 \
    --speculative-config '{"num_speculative_tokens": 2, "method":"deepseek_mtp"}' \
    --seed 1024 \
    --served-model-name dsv3 \
    --max-model-len 68000 \
    --max-num-batched-tokens 12 \
    --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY",  "cudagraph_capture_sizes":[3, 6, 9, 12]}' \
    --trust-remote-code \
    --async-scheduling \
    --max-num-seqs 4 \
    --gpu-memory-utilization 0.95 \
    --no-enable-prefix-caching \
    --quantization ascend \
    --kv-transfer-config \
    '{"kv_connector": "MooncakeConnectorV1",
    "kv_role": "kv_consumer",
    "kv_port": "30100",
    "engine_id": "1",
    "kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
    "kv_connector_extra_config": {
                "use_ascend_direct": true,
                "prefill": {
                        "dp_size": 2,
                        "tp_size": 16
                },
                "decode": {
                        "dp_size": 8,
                        "tp_size": 4
                }
        }
    }' \
    --additional-config '{"recompute_scheduler_enable" : true}'

Once the preparation is done, you can start the server with the following command on each node:

  1. Prefill node 0
# change ip to your own
python launch_online_dp.py --dp-size 2 --tp-size 16 --dp-size-local 1 --dp-rank-start 0 --dp-address 141.61.39.105 --dp-rpc-port 12890 --vllm-start-port 9100
  1. Prefill node 1
# change ip to your own
python launch_online_dp.py --dp-size 2 --tp-size 16 --dp-size-local 1 --dp-rank-start 1 --dp-address 141.61.39.105 --dp-rpc-port 12890 --vllm-start-port 9100
  1. Decode node 0
# change ip to your own
python launch_online_dp.py --dp-size 8 --tp-size 4 --dp-size-local 4 --dp-rank-start 0 --dp-address 141.61.39.117 --dp-rpc-port 12777 --vllm-start-port 9100
  1. Decode node 1
# change ip to your own
python launch_online_dp.py --dp-size 8 --tp-size 4 --dp-size-local 4 --dp-rank-start 4 --dp-address 141.61.39.117 --dp-rpc-port 12777 --vllm-start-port 9100

Functional Verification

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

curl http://<node0_ip>:<port>/v1/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "deepseek_v3.2",
        "prompt": "The future of AI is",
        "max_tokens": 50,
        "temperature": 0
    }'

Accuracy Evaluation

Here are two accuracy evaluation methods.

Using AISBench

  1. Refer to Using AISBench for details.

  2. After execution, you can get the result.

Using Language Model Evaluation Harness

As an example, take the gsm8k dataset as a test dataset, and run accuracy evaluation of DeepSeek-V3.2-W8A8 in online mode.

  1. Refer to Using lm_eval for lm_eval installation.

  2. Run lm_eval to execute the accuracy evaluation.

lm_eval \
  --model local-completions \
  --model_args model=/root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-QuaRot,base_url=http://127.0.0.1:8000/v1/completions,tokenized_requests=False,trust_remote_code=True \
  --tasks gsm8k \
  --output_path ./
  1. After execution, you can get the result.

Performance

Using AISBench

Refer to Using AISBench for performance evaluation for details.

The performance result is:

Hardware: A3-752T, 4 node

Deployment: 1P1D, Prefill node: DP2+TP16, Decode Node: DP8+TP4

Input/Output: 64k/3k

Performance: 533tps, TPOT 32ms

Using vLLM Benchmark

Run performance evaluation of DeepSeek-V3.2-W8A8 as an example.

Refer to vllm benchmark for more details.

There are three vllm bench subcommand:

  • latency: Benchmark the latency of a single batch of requests.
  • serve: Benchmark the online serving throughput.
  • throughput: Benchmark offline inference throughput.

Take the serve as an example. Run the code as follows.

export VLLM_USE_MODELSCOPE=true
vllm bench serve --model /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-QuaRot  --dataset-name random --random-input 200 --num-prompt 200 --request-rate 1 --save-result --result-dir ./

Function Call

The function call feature is supported from v0.13.0rc1 on. Please use the latest version.

Refer to DeepSeek-V3.2 Usage Guide for details.