DeepSeek-V3/3.1

DeepSeek-V3/3.1#

Introduction#

DeepSeek-V3.1 is a hybrid model that supports both thinking mode and non-thinking mode. Compared to the previous version, this upgrade brings improvements in multiple aspects:

Hybrid thinking mode: One model supports both thinking mode and non-thinking mode by changing the chat template.
Smarter tool calling: Through post-training optimization, the model’s performance in tool usage and agent tasks has significantly improved.
Higher thinking efficiency: DeepSeek-V3.1-Think achieves comparable answer quality to DeepSeek-R1-0528, while responding more quickly.

The DeepSeek-V3.1 model is first supported in vllm-ascend:v0.9.1rc3.

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-V3.1(BF16 version): Download model weight.
DeepSeek-V3.1-w8a8(Quantized version without mtp): Download model weight.
DeepSeek-V3.1_w8a8mix_mtp(Quantized version with mix mtp): Download model weight. Please modify torch_dtype from float16 to bfloat16 in config.json.
DeepSeek-V3.1-Terminus-w4a8-mtp-QuaRot(Quantized version with mix mtp): Download model weight.
Method of Quantify: msmodelslim. You can use these methods to quantify the model.

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 use our official docker image to run DeepSeek-V3.1 directly.

Select an image based on your machine type and start the docker image on your node, refer to using docker.

# Update --device according to your device (Atlas A2: /dev/davinci[0-7] Atlas A3:/dev/davinci[0-15]).
# Update the vllm-ascend image according to your environment.
# Note you should download the weight to /root/.cache in advance.
# Update the vllm-ascend image
export IMAGE=m.daocloud.io/quay.io/ascend/vllm-ascend:v0.15.0rc1
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/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

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

Deployment#

Single-node Deployment#

Quantized model DeepSeek-V3.1-w8a8-mtp-QuaRot can be deployed on 1 Atlas 800 A3 (64G × 16).

Run the following script to execute online inference.

#!/bin/sh
# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxxx"
local_ip="xxxx"

# [Optional] jemalloc
# jemalloc is for better performance, if `libjemalloc.so` is installed on your machine, you can turn it on.
# export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD

# AIV
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 VLLM_ASCEND_BALANCE_SCHEDULING=1
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True

vllm serve /weights/DeepSeek-V3.1-w8a8-mtp-QuaRot \
--host 0.0.0.0 \
--port 8015 \
--data-parallel-size 4 \
--tensor-parallel-size 4 \
--quantization ascend \
--seed 1024 \
--served-model-name deepseek_v3 \
--enable-expert-parallel \
--async-scheduling \
--max-num-seqs 16 \
--max-model-len 16384 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.92 \
--speculative-config '{"num_speculative_tokens": 3, "method": "mtp"}' \
--compilation-config '{"cudagraph_capture_sizes":[4,16,32,48,64], "cudagraph_mode": "FULL_DECODE_ONLY"}'

Notice: The parameters are explained as follows:

Setting the environment variable VLLM_ASCEND_BALANCE_SCHEDULING=1 enables balance scheduling. This may help increase output throughput and reduce TPOT in v1 scheduler. However, TTFT may degrade in some scenarios. Furthermore, enabling this feature is not recommended in scenarios where PD is separated.
For single-node deployment, we recommend using dp4tp4 instead of dp2tp8.
--max-model-len specifies the maximum context length - that is, the sum of input and output tokens for a single request. For performance testing with an input length of 3.5K and output length of 1.5K, a value of 16384 is sufficient, however, for precision testing, please set it at least 35000.
--no-enable-prefix-caching indicates that prefix caching is disabled. To enable it, remove this option.
If you use the w4a8 weight, more memory will be allocated to kvcache, and you can try to increase system throughput to achieve greater throughput.

Multi-node Deployment#

DeepSeek-V3.1-w8a8-mtp-QuaRot: require at least 2 Atlas 800 A2 (64G × 8).

Run the following scripts on two nodes respectively.

Node 0

#!/bin/sh

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

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

# [Optional] jemalloc
# jemalloc is for better performance, if `libjemalloc.so` is installed on your machine, you can turn it on.
# export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD

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=1
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_BALANCE_SCHEDULING=1
export HCCL_INTRA_PCIE_ENABLE=1
export HCCL_INTRA_ROCE_ENABLE=0

vllm serve /weights/DeepSeek-V3.1-w8a8-mtp-QuaRot \
--host 0.0.0.0 \
--port 8004 \
--data-parallel-size 4 \
--data-parallel-size-local 2 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 13389 \
--tensor-parallel-size 4 \
--quantization ascend \
--seed 1024 \
--served-model-name deepseek_v3 \
--enable-expert-parallel \
--async-scheduling \
--max-num-seqs 16 \
--max-model-len 16384 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.92 \
--speculative-config '{"num_speculative_tokens": 3, "method": "mtp"}' \
--compilation-config '{"cudagraph_capture_sizes":[4,16,32,48,64], "cudagraph_mode": "FULL_DECODE_ONLY"}'

Node 1

#!/bin/sh

# 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"

# [Optional] jemalloc
# jemalloc is for better performance, if `libjemalloc.so` is installed on your machine, you can turn it on.
# export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD

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=1
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_BALANCE_SCHEDULING=1
export HCCL_INTRA_PCIE_ENABLE=1
export HCCL_INTRA_ROCE_ENABLE=0

vllm serve /weights/DeepSeek-V3.1-w8a8-mtp-QuaRot \
--host 0.0.0.0 \
--port 8004 \
--headless \
--data-parallel-size 4 \
--data-parallel-size-local 2 \
--data-parallel-start-rank 2 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 13389 \
--tensor-parallel-size 4 \
--quantization ascend \
--seed 1024 \
--served-model-name deepseek_v3 \
--enable-expert-parallel \
--async-scheduling \
--max-num-seqs 16 \
--max-model-len 16384 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.92 \
--speculative-config '{"num_speculative_tokens": 3, "method": "mtp"}' \
--compilation-config '{"cudagraph_capture_sizes":[4,16,32,48,64], "cudagraph_mode": "FULL_DECODE_ONLY"}'

Prefill-Decode Disaggregation#

We recommend using Mooncake for deployment: Mooncake.

Take Atlas 800 A3 (64G × 16) for example, we recommend to deploy 2P1D (4 nodes) rather than 1P1D (2 nodes), because there is no enough NPU memory to serve high concurrency in 1P1D case.

DeepSeek-V3.1-w8a8-mtp-QuaRot 2P1D Layerwise require 4 Atlas 800 A3 (64G × 16).

To run the vllm-ascend Prefill-Decode Disaggregation service, you need to deploy a launch_dp_program.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 to launch external dp vllm servers. launch_online_dp.py
Prefill Node 0 run_dp_template.sh script

# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="141.xx.xx.1"

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

# [Optional] jemalloc
# jemalloc is for better performance, if `libjemalloc.so` is installed on your machine, you can turn it on.
# export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD

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 VLLM_RPC_TIMEOUT=3600000
export VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=30000
export HCCL_EXEC_TIMEOUT=204
export HCCL_CONNECT_TIMEOUT=120

export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export HCCL_BUFFSIZE=256
export TASK_QUEUE_ENABLE=1
export HCCL_OP_EXPANSION_MODE="AIV"
export VLLM_USE_V1=1
export ASCEND_RT_VISIBLE_DEVICES=$1
export ASCEND_BUFFER_POOL=4:8
export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages/mooncake:$LD_LIBRARY_PATH

export VLLM_ASCEND_ENABLE_FLASHCOMM1=1

vllm serve /weights/DeepSeek-V3.1-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 \
  --seed 1024 \
  --served-model-name deepseek_v3 \
  --max-model-len 65536 \
  --max-num-batched-tokens 16384 \
  --max-num-seqs 8 \
  --enforce-eager \
  --trust-remote-code \
  --gpu-memory-utilization 0.9 \
  --quantization ascend \
  --no-enable-prefix-caching \
  --speculative-config '{"num_speculative_tokens": 1, "method": "mtp"}' \
  --additional-config '{"recompute_scheduler_enable":true}' \
  --kv-transfer-config \
  '{"kv_connector": "MooncakeConnectorV1",
  "kv_role": "kv_producer",
  "kv_port": "30000",
  "engine_id": "0",
  "kv_connector_extra_config": {
            "prefill": {
                    "dp_size": 2,
                    "tp_size": 8
             },
             "decode": {
                    "dp_size": 32,
                    "tp_size": 1
             }
      }
  }'

Prefill Node 1 run_dp_template.sh script

# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="141.xx.xx.2"

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

# [Optional] jemalloc
# jemalloc is for better performance, if `libjemalloc.so` is installed on your machine, you can turn it on.
# export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD

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 VLLM_RPC_TIMEOUT=3600000
export VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=30000
export HCCL_EXEC_TIMEOUT=204
export HCCL_CONNECT_TIMEOUT=120

export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export HCCL_BUFFSIZE=256
export TASK_QUEUE_ENABLE=1
export HCCL_OP_EXPANSION_MODE="AIV"
export VLLM_USE_V1=1
export ASCEND_RT_VISIBLE_DEVICES=$1
export ASCEND_BUFFER_POOL=4:8
export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages/mooncake:$LD_LIBRARY_PATH

export VLLM_ASCEND_ENABLE_FLASHCOMM1=1

vllm serve /weights/DeepSeek-V3.1-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 \
  --seed 1024 \
  --served-model-name deepseek_v3 \
  --max-model-len 65536 \
  --max-num-batched-tokens 16384 \
  --max-num-seqs 8 \
  --enforce-eager \
  --trust-remote-code \
  --gpu-memory-utilization 0.9 \
  --quantization ascend \
  --no-enable-prefix-caching \
  --speculative-config '{"num_speculative_tokens": 1, "method": "mtp"}' \
  --additional-config '{"recompute_scheduler_enable":true}' \
  --kv-transfer-config \
  '{"kv_connector": "MooncakeConnectorV1",
  "kv_role": "kv_producer",
  "kv_port": "30100",
  "engine_id": "1",
  "kv_connector_extra_config": {
            "prefill": {
                    "dp_size": 2,
                    "tp_size": 8
             },
             "decode": {
                    "dp_size": 32,
                    "tp_size": 1
             }
      }
  }'

Decode Node 0 run_dp_template.sh script

# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="141.xx.xx.3"

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

# [Optional] jemalloc
# jemalloc is for better performance, if `libjemalloc.so` is installed on your machine, you can turn it on.
# export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD

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 VLLM_RPC_TIMEOUT=3600000
export VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=30000
export HCCL_EXEC_TIMEOUT=204
export HCCL_CONNECT_TIMEOUT=120

export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export HCCL_BUFFSIZE=1100
export TASK_QUEUE_ENABLE=1
export HCCL_OP_EXPANSION_MODE="AIV"
export VLLM_USE_V1=1
export ASCEND_RT_VISIBLE_DEVICES=$1
export ASCEND_BUFFER_POOL=4:8
export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages/mooncake:$LD_LIBRARY_PATH

vllm serve /weights/DeepSeek-V3.1-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 \
  --seed 1024 \
  --served-model-name deepseek_v3 \
  --max-model-len 65536 \
  --max-num-batched-tokens 256 \
  --max-num-seqs 28 \
  --trust-remote-code \
  --gpu-memory-utilization 0.92 \
  --quantization ascend \
  --no-enable-prefix-caching \
  --async-scheduling \
  --speculative-config '{"num_speculative_tokens": 1, "method": "mtp"}' \
  --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY", "cudagraph_capture_sizes":[2, 4, 8, 16, 24, 32, 48, 56]}' \
  --additional-config '{"recompute_scheduler_enable":true,"multistream_overlap_shared_expert": true,"finegrained_tp_config": {"lmhead_tensor_parallel_size":16}}' \
  --kv-transfer-config \
  '{"kv_connector": "MooncakeConnectorV1",
  "kv_role": "kv_consumer",
  "kv_port": "30200",
  "engine_id": "2",
  "kv_connector_extra_config": {
            "prefill": {
                    "dp_size": 2,
                    "tp_size": 8
             },
             "decode": {
                    "dp_size": 32,
                    "tp_size": 1
             }
      }
  }'

Decode Node 1 run_dp_template.sh script

# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="141.xx.xx.4"

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

# [Optional] jemalloc
# jemalloc is for better performance, if `libjemalloc.so` is installed on your machine, you can turn it on.
# export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD

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 VLLM_RPC_TIMEOUT=3600000
export VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=30000
export HCCL_EXEC_TIMEOUT=204
export HCCL_CONNECT_TIMEOUT=120

export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export HCCL_BUFFSIZE=1100
export TASK_QUEUE_ENABLE=1
export HCCL_OP_EXPANSION_MODE="AIV"
export VLLM_USE_V1=1
export ASCEND_RT_VISIBLE_DEVICES=$1
export ASCEND_BUFFER_POOL=4:8
export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages/mooncake:$LD_LIBRARY_PATH

vllm serve /weights/DeepSeek-V3.1-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 \
  --seed 1024 \
  --served-model-name deepseek_v3 \
  --max-model-len 65536 \
  --max-num-batched-tokens 256 \
  --max-num-seqs 28 \
  --trust-remote-code \
  --gpu-memory-utilization 0.92 \
  --quantization ascend \
  --no-enable-prefix-caching \
  --async-scheduling \
  --speculative-config '{"num_speculative_tokens": 1, "method": "mtp"}' \
  --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY", "cudagraph_capture_sizes":[2, 4, 8, 16, 24, 32, 48, 56]}' \
  --additional-config '{"recompute_scheduler_enable":true,"multistream_overlap_shared_expert": true,"finegrained_tp_config": {"lmhead_tensor_parallel_size":16}}' \
  --kv-transfer-config \
  '{"kv_connector": "MooncakeConnectorV1",
  "kv_role": "kv_consumer",
  "kv_port": "30300",
  "engine_id": "3",
  "kv_connector_extra_config": {
            "prefill": {
                    "dp_size": 2,
                    "tp_size": 8
             },
             "decode": {
                    "dp_size": 32,
                    "tp_size": 1
             }
      }
  }'

Notice: The parameters are explained as follows:

VLLM_ASCEND_ENABLE_FLASHCOMM1=1: enables the communication optimization function on the prefill nodes.
VLLM_ASCEND_ENABLE_MLAPO=1: enables the fusion operator, which can significantly improve performance but consumes more NPU memory. In the Prefill-Decode (PD) separation scenario, enable MLAPO only on decode nodes.
--async-scheduling: enables the asynchronous scheduling function. When Multi-Token Prediction (MTP) is enabled, asynchronous scheduling of operator delivery can be implemented to overlap the operator delivery latency.
cudagraph_capture_sizes: The recommended value is n x (mtp + 1). And the min is n = 1 and the max is n = max-num-seqs. For other values, it is recommended to set them to the number of frequently occurring requests on the Decode (D) node.
recompute_scheduler_enable: true: enables the recomputation scheduler. When the Key-Value Cache (KV Cache) of the decode node is insufficient, requests will be sent to the prefill node to recompute the KV Cache. In the PD separation scenario, it is recommended to enable this configuration on both prefill and decode nodes simultaneously.
multistream_overlap_shared_expert: true: When the Tensor Parallelism (TP) size is 1 or enable_shared_expert_dp: true, an additional stream is enabled to overlap the computation process of shared experts for improved efficiency.
lmhead_tensor_parallel_size: 16: When the Tensor Parallelism (TP) size of the decode node is 1, this parameter allows the TP size of the LMHead embedding layer to be greater than 1, which is used to reduce the computational load of each card on the LMHead embedding layer.

run server for each node:

# p0
python launch_online_dp.py --dp-size 2 --tp-size 8 --dp-size-local 2 --dp-rank-start 0 --dp-address 141.xx.xx.1 --dp-rpc-port 12321 --vllm-start-port 7100
# p1
python launch_online_dp.py --dp-size 2 --tp-size 8 --dp-size-local 2 --dp-rank-start 0 --dp-address 141.xx.xx.2 --dp-rpc-port 12321 --vllm-start-port 7100
# d0
python launch_online_dp.py --dp-size 32 --tp-size 1 --dp-size-local 16 --dp-rank-start 0 --dp-address 141.xx.xx.3 --dp-rpc-port 12321 --vllm-start-port 7100
# d1
python launch_online_dp.py --dp-size 32 --tp-size 1 --dp-size-local 16 --dp-rank-start 16 --dp-address 141.xx.xx.3 --dp-rpc-port 12321 --vllm-start-port 7100

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

python load_balance_proxy_server_example.py \
  --port 1999 \
  --host 141.xx.xx.1 \
  --prefiller-hosts \
xx.xx.1 \
xx.xx.1 \
xx.xx.2 \
xx.xx.2 \
  --prefiller-ports \
7101 7100 7101 \
  --decoder-hosts \
xx.xx.3 \
xx.xx.3 \
xx.xx.3 \
xx.xx.3 \
xx.xx.3 \
xx.xx.3 \
xx.xx.3 \
xx.xx.3 \
xx.xx.3 \
xx.xx.3 \
xx.xx.3 \
xx.xx.3 \
xx.xx.3 \
xx.xx.3 \
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  --decoder-ports \
7101 7102 7103 7104 7105 7106 7107 7108 7109 7110 7111 7112 7113 7114 7115 \
7101 7102 7103 7104 7105 7106 7107 7108 7109 7110 7111 7112 7113 7114 7115 \

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

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",
        "prompt": "The future of AI is",
        "max_completion_tokens": 50,
        "temperature": 0
    }'

Accuracy Evaluation#

Here are two accuracy evaluation methods.

Using AISBench#

Refer to Using AISBench for details.
After execution, you can get the result, here is the result of DeepSeek-V3.1-w8a8-mtp-QuaRot in vllm-ascend:0.11.0rc1 for reference only.

dataset	version	metric	mode	vllm-api-general-chat	note
ceval	-	accuracy	gen	90.94	1 Atlas 800 A3 (64G × 16)
gsm8k	-	accuracy	gen	96.28	1 Atlas 800 A3 (64G × 16)

Using Language Model Evaluation Harness#

Not test yet.

Performance#

Using AISBench#

Refer to Using AISBench for performance evaluation for details.

The performance result is:

Hardware: A3-752T, 4 node

Deployment: 2P1D, Prefill node: DP2+TP8, Decode Node: DP32+TP1

Input/Output: 3.5k/1.5k

Performance: TTFT = 6.16s, TPOT = 48.82ms, Average performance of each card is 478 TPS (Token Per Second).

Using vLLM Benchmark#

Run performance evaluation of DeepSeek-V3.1-w8a8-mtp-QuaRot 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.

vllm bench serve --model /weights/DeepSeek-V3.1-w8a8-mtp-QuaRot  --dataset-name random --random-input 1024 --num-prompts 200 --request-rate 1 --save-result --result-dir ./

After about several minutes, you can get the performance evaluation result.

DeepSeek-V3/3.1

Contents

DeepSeek-V3/3.1#

Introduction#

Supported Features#

Environment Preparation#

Model Weight#

Verify Multi-node Communication(Optional)#

Installation#

Deployment#

Single-node Deployment#

Multi-node Deployment#

Prefill-Decode Disaggregation#

Functional Verification#

Accuracy Evaluation#

Using AISBench#

Using Language Model Evaluation Harness#

Performance#

Using AISBench#

Using vLLM Benchmark#