DeepSeek-V3.2

1 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.

2 Supported Features

Refer to Supported Features List to get the model's supported feature matrix.

Refer to Feature Guide to get the feature's configuration.

3 Prerequisites

3.1 Model Weight

• DeepSeek-V3.2-Exp-W8A8 (Quantized version): requires 1 Atlas 800 A3 (64GB × 16) node or 2 Atlas 800 A2 (64GB × 8) nodes. Download model weight
• DeepSeek-V3.2-w8a8 (Quantized version): requires 1 Atlas 800 A3 (64GB × 16) node or 2 Atlas 800 A2 (64GB × 8) nodes. Download model weight

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

3.2 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.

4 Installation

4.1 Docker Image Installation

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

A3 seriesA2 series

Start the docker image on each node.

export IMAGE=quay.io/ascend/vllm-ascend:v0.23.0-a3
docker run --rm \
    --name vllm-ascend \
    --privileged=true \
    --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

Start the docker image on each node.

export IMAGE=quay.io/ascend/vllm-ascend:v0.23.0
docker run --rm \
    --name vllm-ascend \
    --privileged=true \
    --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

4.2 Source Code Installation

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

• Install vllm-ascend from source, refer to installation.

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

5 Online Service Deployment

Note

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

5.1 Single-node Deployment

• Quantized model DeepSeek-V3.2-w8a8 can be deployed on 1 Atlas 800 A3 (64GB × 16).

Run the following script to execute online inference.

export HCCL_OP_EXPANSION_MODE="AIV"
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V3.2-W8A8 --additional-config '{"enable_mlapo":true}' \
--host 0.0.0.0 \
--port 8000 \
--data-parallel-size 2 \
--tensor-parallel-size 8 \
--quantization ascend \
--seed 1024 \
--served-model-name deepseek_v3_2 \
--enable-expert-parallel \
--max-num-seqs 16 \
--max-model-len 8192 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.92 \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}'

5.2 Multi-node Deployment

• DeepSeek-V3.2-w8a8: require at least 2 Atlas 800 A2 (64GB × 8).

Run the following scripts on two nodes respectively.

A3 seriesA2 series

Node0

# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="xxx"

# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxxx"

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 VLLM_USE_V1=1
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V3.2-W8A8 --additional-config '{"enable_mlapo":true}' \
--host 0.0.0.0 \
--port 8077 \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 12890 \
--tensor-parallel-size 16 \
--quantization ascend \
--seed 1024 \
--served-model-name deepseek_v3_2 \
--enable-expert-parallel \
--max-num-seqs 16 \
--max-model-len 8192 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.92 \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}'

Node1

# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="xxx"

# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxxx"

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 VLLM_USE_V1=1
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V3.2-W8A8 --additional-config '{"enable_mlapo":true}' \
--host 0.0.0.0 \
--port 8077 \
--headless \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-start-rank 1 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 12890 \
--tensor-parallel-size 16 \
--quantization ascend \
--seed 1024 \
--served-model-name deepseek_v3_2 \
--enable-expert-parallel \
--max-num-seqs 16 \
--max-model-len 8192 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.92 \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}'

Node0

# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="xxx"

# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxxx"

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=100
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export HCCL_CONNECT_TIMEOUT=120
export HCCL_INTRA_PCIE_ENABLE=1
export HCCL_INTRA_ROCE_ENABLE=0

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V3.2-W8A8 --additional-config '{"enable_mlapo":true}' \
--host 0.0.0.0 \
--port 8077 \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 13389 \
--tensor-parallel-size 8 \
--quantization ascend \
--seed 1024 \
--served-model-name deepseek_v3_2 \
--enable-expert-parallel \
--max-num-seqs 16 \
--max-model-len 8192 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.92 \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY", "cudagraph_capture_sizes":[8, 16, 24, 32, 40, 48]}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}'

Node1

# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="xxx"

# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxxx"

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=100
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export HCCL_CONNECT_TIMEOUT=120
export HCCL_INTRA_PCIE_ENABLE=1
export HCCL_INTRA_ROCE_ENABLE=0

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V3.2-W8A8 --additional-config '{"enable_mlapo":true}' \
--host 0.0.0.0 \
--port 8077 \
--headless \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-start-rank 1 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 13389 \
--tensor-parallel-size 8 \
--quantization ascend \
--seed 1024 \
--served-model-name deepseek_v3_2 \
--enable-expert-parallel \
--max-num-seqs 16 \
--max-model-len 8192 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.92 \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY", "cudagraph_capture_sizes":[8, 16, 24, 32, 40, 48]}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}'

5.3 Prefill-Decode Disaggregation

We recommend using Mooncake for deployment: Mooncake.

In the standard single-node deployment mode, Prefill (prompt processing) and Decode (token generation) tasks run on the same set of NPUs. PD (Prefill-Decode) separation addresses this by running Prefill and Decode on dedicated node groups, each configured independently:

• Prefill nodes focus on high-throughput prompt processing, optimized for compute and communication.
• Decode nodes focus on low-latency token generation, optimized for memory bandwidth.

This architecture is recommended for production deployments with concurrent multi-user workloads, where stable latency and high throughput are both required.

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

To run the vllm-ascend Prefill-Decode Disaggregation service, you need to deploy a launch_online_dp.py script and a run_dp_template.sh script on each node and deploy a proxy.sh script on prefill master node to forward requests.

launch_online_dp.py

Parameter descriptions:

Parameter	Type	Required	Default	Description
--dp-size	int	Yes	-	Data parallel size (total number of DP ranks across all nodes).
--tp-size	int	No	1	Tensor parallel size within each DP rank.
--dp-size-local	int	No	(same as --dp-size)	Number of DP ranks on the current node. If not set, defaults to --dp-size.
--dp-rank-start	int	No	0	Starting rank offset for data parallel ranks on this node.
--dp-address	str	Yes	-	IP address of the data parallel master node (node 0).
--dp-rpc-port	str	No	12345	RPC port for data parallel master communication.
--vllm-start-port	int	No	9000	Starting port for each vLLM engine instance on this node. Each DP rank's engine port = vllm_start_port + local rank index.

1. run_dp_template.sh script

Node 0(Prefill)Node 1(Prefill)Node 0(Decode)Node 1(Decode)

nic_name="enp48s3u1u1" # change to your own nic name
local_ip=192.xx.xx.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 ASCEND_AGGREGATE_ENABLE=1
export ASCEND_TRANSPORT_PRINT=1
export ACL_OP_INIT_MODE=1
export ASCEND_A3_ENABLE=1
# Timeout (in seconds) for automatically releasing the prefiller’s KV cache for a particular request.
export VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT=480

export ASCEND_RT_VISIBLE_DEVICES=$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": 1, "method":"deepseek_mtp"}' \
    --profiler-config \
    '{"profiler": "torch",
    "torch_profiler_dir": "./vllm_profile",
    "torch_profiler_with_stack": false}' \
    --seed 1024 \
    --served-model-name deepseek_v3.2 \
    --max-model-len 68000 \
    --max-num-batched-tokens 32560 \
    --trust-remote-code \
    --max-num-seqs 64 \
    --gpu-memory-utilization 0.82 \
    --quantization ascend \
    --enforce-eager \
    --no-enable-prefix-caching \
    --additional-config '{"enable_dsa_cp": true}' \
    --kv-transfer-config \
    '{"kv_connector": "MooncakeConnectorV1",
    "kv_role": "kv_producer",
    "kv_port": "30000",
    "kv_connector_extra_config": {
            "prefill": {
                    "dp_size": 2,
                    "tp_size": 16
            },
            "decode": {
                    "dp_size": 8,
                    "tp_size": 4
            }
        }
    }'

nic_name="enp48s3u1u1" # change to your own nic name
local_ip=192.xx.xx.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 ASCEND_AGGREGATE_ENABLE=1
export ASCEND_TRANSPORT_PRINT=1
export ACL_OP_INIT_MODE=1
export ASCEND_A3_ENABLE=1
# Timeout (in seconds) for automatically releasing the prefiller's KV cache for a particular request.
export VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT=480

export ASCEND_RT_VISIBLE_DEVICES=$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": 1, "method":"deepseek_mtp"}' \
    --profiler-config \
    '{"profiler": "torch",
    "torch_profiler_dir": "./vllm_profile",
    "torch_profiler_with_stack": false}' \
    --seed 1024 \
    --served-model-name deepseek_v3.2 \
    --max-model-len 68000 \
    --max-num-batched-tokens 32560 \
    --trust-remote-code \
    --max-num-seqs 64 \
    --gpu-memory-utilization 0.82 \
    --quantization ascend \
    --enforce-eager \
    --no-enable-prefix-caching \
    --additional-config '{"enable_dsa_cp": true}' \
    --kv-transfer-config \
    '{"kv_connector": "MooncakeConnectorV1",
    "kv_role": "kv_producer",
    "kv_port": "30000",
    "kv_connector_extra_config": {
            "prefill": {
                    "dp_size": 2,
                    "tp_size": 16
            },
            "decode": {
                    "dp_size": 8,
                    "tp_size": 4
            }
        }
    }'

nic_name="enp48s3u1u1" # change to your own nic name
local_ip=192.xx.xx.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 ASCEND_AGGREGATE_ENABLE=1
export ASCEND_TRANSPORT_PRINT=1
export ACL_OP_INIT_MODE=1
export ASCEND_A3_ENABLE=1
# Timeout (in seconds) for automatically releasing the prefiller’s KV cache for a particular request.
export VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT=480

export TASK_QUEUE_ENABLE=1

export ASCEND_RT_VISIBLE_DEVICES=$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"}' \
    --profiler-config \
    '{"profiler": "torch",
    "torch_profiler_dir": "./vllm_profile",
    "torch_profiler_with_stack": false}' \
    --seed 1024 \
    --served-model-name deepseek_v3.2 \
    --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 \
    --quantization ascend \
    --kv-transfer-config \
    '{"kv_connector": "MooncakeConnectorV1",
    "kv_role": "kv_consumer",
    "kv_port": "30100",
    "kv_connector_extra_config": {
            "prefill": {
                    "dp_size": 2,
                    "tp_size": 16
            },
            "decode": {
                    "dp_size": 8,
                    "tp_size": 4
            }
        }
    }' \
    --additional-config '{"recompute_scheduler_enable" : true}'

nic_name="enp48s3u1u1" # change to your own nic name
local_ip=192.xx.xx.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 ASCEND_AGGREGATE_ENABLE=1
export ASCEND_TRANSPORT_PRINT=1
export ACL_OP_INIT_MODE=1
export ASCEND_A3_ENABLE=1
# Timeout (in seconds) for automatically releasing the prefiller’s KV cache for a particular request.
export VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT=480

export TASK_QUEUE_ENABLE=1

export ASCEND_RT_VISIBLE_DEVICES=$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"}' \
    --profiler-config \
    '{"profiler": "torch",
    "torch_profiler_dir": "./vllm_profile",
    "torch_profiler_with_stack": false}' \
    --seed 1024 \
    --served-model-name deepseek_v3.2 \
    --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 \
    --quantization ascend \
    --kv-transfer-config \
    '{"kv_connector": "MooncakeConnectorV1",
    "kv_role": "kv_consumer",
    "kv_port": "30100",
    "kv_connector_extra_config": {
            "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: Refer to Distributed DP Server With Large-Scale Expert Parallelism to get the detailed boot method.

1. run server for each node:

   # 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 192.xx.xx.105 --dp-rpc-port 12890 --vllm-start-port 9100
   # 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 192.xx.xx.105 --dp-rpc-port 12890 --vllm-start-port 9100
   # 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 192.xx.xx.117 --dp-rpc-port 12777 --vllm-start-port 9100
   # 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 192.xx.xx.117 --dp-rpc-port 12777 --vllm-start-port 9100
   

2. Run the proxy.sh script on the prefill master node

   Run a proxy server on the same node with the prefiller service instance. You can get the proxy program in the repository's examples: load_balance_proxy_server_example.py

   unset http_proxy
   unset https_proxy
   
   python load_balance_proxy_server_example.py \
       --port 8000 \
       --host 192.xx.xx.105 \
       --prefiller-hosts \
       192.xx.xx.105 \
       192.xx.xx.113 \
       --prefiller-ports \
       9100 \
       9100 \
       --decoder-hosts \
       192.xx.xx.117 \
       192.xx.xx.117 \
       192.xx.xx.117 \
       192.xx.xx.117 \
       192.xx.xx.181 \
       192.xx.xx.181 \
       192.xx.xx.181 \
       192.xx.xx.181 \
       --decoder-ports \
       9100 9101 9102 9103 \
       9100 9101 9102 9103
   

   cd vllm-ascend/examples/disaggregated_prefill_v1/
   bash proxy.sh
   

Common Issues Tip: If you encounter issues with PD separation deployment, please refer to the Public FAQs for troubleshooting.

6 Functional Verification

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

Note

• <node0_ip>: The IP address of the node where the server is running (e.g., localhost). For PD-separated deployment, use the host IP of the node where the proxy script resides.
• <port>: The port number specified in the server startup command (e.g., 8000). For PD-separated deployment, use the port configured in the proxy script.

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

Expected Result:

{"id":"019eab54ead036b23e53f3a709e09289","object":"chat.completion","created":1780990929,"model":"deepseek_v3.2","choices":[{"index":0,"message":{"role":"assistant","content":"The future of AI is **not a single destination, but a complex, multi-faceted trajectory** that will reshape nearly every aspect of human society, technology, and our understanding of intelligence itself. It can be understood through several interconnected lenses:\n\n### "},"finish_reason":"length"}],"usage":{"prompt_tokens":9,"completion_tokens":50,"total_tokens":59,"completion_tokens_details":{"reasoning_tokens":0},"prompt_tokens_details":{"cached_tokens":0},"prompt_cache_hit_tokens":0,"prompt_cache_miss_tokens":9},"system_fingerprint":""}

7 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 ./
   

3. After execution, you can get the result.

8 Performance Evaluation

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 subcommands:

• 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-prompts 200 --request-rate 1 --save-result --result-dir ./

9 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.

10 FAQ

For common environment, installation, and general parameter issues, please refer to the Public FAQs.