DeepSeek-V4-Flash

1 Introduction

DeepSeek-V4 introduces several key upgrades over DeepSeek-V3:

• The Manifold-Constrained Hyper-Connections (mHC) to strengthen conventional residual connections.
• A hybrid attention architecture, which greatly improves long-context efficiency through Compress-4-Attention and Compress-128-Attention. For the Mixture-of-Experts (MoE) components, it still adopts the DeepSeekMoE architecture, with only minor adjustments.

DeepSeek-V4-Flash is the lightweight variant of the DeepSeek-V4 family, suitable for high-throughput and low-latency serving 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.

Note: Please replace the version placeholder above with your actual validation 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

• DeepSeek-V4-Flash-w8a8-mtp (Quantized version): requires 1 Atlas 800 A3 (128GB × 8) node or 1 Atlas 800 A2 (64GB × 8) node. Download model weight

• DeepSeek released new DeepSeek-V4-Flash-DSpark weights on July 31, 2026. Download the quantized DeepSeek-V4-Flash-0731-w8a8 weight from ModelScope.

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 a multi-node environment, you need to verify 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 docker image on your node, refer to using docker.

Attention: DSpark is supported on both A2 and A3 in vLLM Ascend v0.25.0 and later. Use the image quay.io/ascend/vllm-ascend:DeepSeekV4-flash-0731 for A2 or the image quay.io/ascend/vllm-ascend:DeepSeekV4-flash-0731-a3 for A3.

A3 seriesA2 series

Start the docker image on each node.

export IMAGE=quay.io/ascend/vllm-ascend:v0.22.1rc1-a3
docker run --rm \
    --name vllm-ascend \
    --shm-size=512g \
    --net=host \
    --privileged=true \
    --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 /etc/hccn.conf:/etc/hccn.conf \
    -v /root/.cache:/root/.cache \
    -it $IMAGE bash

Start the docker image on each node.

# deepseek-v4-flash uses the following image
export IMAGE=quay.io/ascend/vllm-ascend:v0.22.1rc1

# deepseek-v4-flash-dspark uses the following image
export IMAGE=quay.io/ascend/vllm-ascend:nightly-main

docker run --rm \
    --name vllm-ascend \
    --shm-size=512g \
    --net=host \
    --privileged=true \
    --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 /etc/hccn.conf:/etc/hccn.conf \
    -v /root/.cache:/root/.cache \
    -it $IMAGE bash

After a successful docker run, you can verify the running container service by executing the docker ps command.

4.2 Source Code Installation

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 a multi-node environment, you need to set up the environment on each node.

5 Online Service Deployment

Note

In this tutorial, we suppose you downloaded the model weight to /root/.cache/modelscope/hub/models/vllm-ascend/. Feel free to change it to your own path.

It is recommended that the following service code be encapsulated in a .sh script file and executed in Bash mode.

5.1 Single-Node Online Deployment

Single-node deployment completes both Prefill and Decode within the same node. The quantized model DeepSeek-V4-Flash-w8a8-mtp can be deployed on 1 Atlas 800 A3 (128GB × 8) or 1 Atlas 800 A2 (64GB × 8).

A2 seriesA2 series with dsparkA3 seriesA3 series with DSpark

Run the following script to execute online inference.

export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
export HCCL_BUFFSIZE=1024
export TASK_QUEUE_ENABLE=1
export HCCL_OP_EXPANSION_MODE="AIV"

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V4-Flash-w8a8-mtp \
    --max-model-len 133120 \
    --max-num-batched-tokens 8192 \
    --served-model-name dsv4 \
    --gpu-memory-utilization 0.9 \
    --max-num-seqs 32 \
    --data-parallel-size 1 \
    --tensor-parallel-size 8 \
    --enable-expert-parallel \
    --tokenizer-mode deepseek_v4 \
    --tool-call-parser deepseek_v4 \
    --enable-auto-tool-choice \
    --reasoning-parser deepseek_v4 \
    --no-enable-prefix-caching \
    --model-loader-extra-config='{"enable_multithread_load": true, "num_threads": 128}' \
    --quantization ascend \
    --port 8900 \
    --block-size 128 \
    --speculative-config '{"num_speculative_tokens": 1,"method": "mtp","enforce_eager": true}' \
    --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
    --additional-config '
    {"ascend_compilation_config":{
        "enable_npugraph_ex":true,
        "enable_static_kernel":false
        },
    "enable_cpu_binding": true,
    "enable_dsa_cp": true,
    "enable_flashcomm1": true,
    "multistream_overlap_shared_expert": true}'

Run the following script to execute online inference.

export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
export HCCL_BUFFSIZE=1024
export TASK_QUEUE_ENABLE=1
export HCCL_OP_EXPANSION_MODE=AIV

vllm serve /root/.cache/modelscope/hub/models/UploadWeight/DeepSeek-V4-Flash-DSpark-w4a8-test \
    --max-model-len 800000 \
    --max-num-batched-tokens 8192 \
    --served-model-name dsv4-dspark \
    --gpu-memory-utilization 0.9 \
    --max-num-seqs 32 \
    --data-parallel-size 1 \
    --tensor-parallel-size 8 \
    --enable-expert-parallel \
    --tokenizer-mode deepseek_v4 \
    --tool-call-parser deepseek_v4 \
    --enable-auto-tool-choice \
    --reasoning-parser deepseek_v4 \
    --no-disable-hybrid-kv-cache-manager \
    --model-loader-extra-config='{"enable_multithread_load": true, "num_threads": 128}' \
    --quantization ascend \
    --port 8000 \
    --block-size 128 \
    --speculative-config '{"method": "dspark", "num_speculative_tokens": 7, "enforce_eager": true}'  \
    --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}'

tps more than 50+ ,its reach 2X speed of dsv4f with mtp

Run the following script to execute online inference.

export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
export HCCL_BUFFSIZE=1024
export TASK_QUEUE_ENABLE=1
export HCCL_OP_EXPANSION_MODE="AIV"
export VLLM_PREFIX_CACHE_RETENTION_INTERVAL=4096

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V4-Flash-w8a8-mtp \
    --max-model-len 1048576 \
    --max-num-batched-tokens 10240 \
    --served-model-name dsv4 \
    --gpu-memory-utilization 0.9 \
    --api-server-count 1 \
    --max-num-seqs 64 \
    --data-parallel-size 4 \
    --tensor-parallel-size 4 \
    --enable-expert-parallel \
    --tokenizer-mode deepseek_v4 \
    --tool-call-parser deepseek_v4 \
    --enable-auto-tool-choice \
    --reasoning-parser deepseek_v4 \
    --model-loader-extra-config='{"enable_multithread_load": true, "num_threads": 128}' \
    --quantization ascend \
    --port 8900 \
    --block-size 32 \
    --speculative-config '{"num_speculative_tokens": 1,"method": "mtp","enforce_eager": true}' \
    --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
    --additional-config '
    {"ascend_compilation_config":{
        "enable_npugraph_ex": true,
        "enable_static_kernel": false
        },
    "enable_cpu_binding": true,
    "enable_flashcomm1": true,
    "multistream_overlap_shared_expert": true}'

Run the following script to execute online inference.

export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
export HCCL_BUFFSIZE=1024
export TASK_QUEUE_ENABLE=1
export HCCL_OP_EXPANSION_MODE="AIV"
export VLLM_PREFIX_CACHE_RETENTION_INTERVAL=4096

vllm serve /path/to/DeepSeek-V4-Flash-0731-w8a8 \
    --max-model-len 1048576 \
    --max-num-batched-tokens 10240 \
    --served-model-name dsv4 \
    --gpu-memory-utilization 0.9 \
    --max-num-seqs 64 \
    --data-parallel-size 4 \
    --tensor-parallel-size 4 \
    --enable-expert-parallel \
    --tokenizer-mode deepseek_v4 \
    --tool-call-parser deepseek_v4 \
    --enable-auto-tool-choice \
    --reasoning-parser deepseek_v4 \
    --model-loader-extra-config='{"enable_multithread_load": true, "num_threads": 128}' \
    --quantization ascend \
    --port 8900 \
    --block-size 32 \
    --speculative-config '{"method":"dspark","num_speculative_tokens":7,"enforce_eager":true}' \
    --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
    --additional-config '{
        "ascend_compilation_config": {
            "enable_npugraph_ex": true,
            "enable_static_kernel": false
        },
        "enable_cpu_binding": true,
        "enable_flashcomm1": true,
        "multistream_overlap_shared_expert": true
    }'

Key Parameter Descriptions:

• --max-model-len specifies the maximum context length - that is, the sum of input and output tokens for a single request. Adjust it according to your actual scenario.
• --max-num-seqs indicates the maximum number of requests that each DP group is allowed to process. If the number of requests sent to the service exceeds this limit, the excess requests will remain in a waiting state and will not be scheduled. Note that the time spent in the waiting state is also counted in metrics such as TTFT and TPOT. Therefore, when testing performance, it is generally recommended that --max-num-seqs * --data-parallel-size >= the actual total concurrency.
• --max-num-batched-tokens is the maximum number of tokens processed in one scheduler step. A larger value can improve prefill efficiency but consumes more activation memory.
• --data-parallel-size sets the global number of data parallel ranks, while --tensor-parallel-size sets the tensor parallel size within each DP rank. Configure them together according to the deployment topology and available NPUs.
• --enable-expert-parallel enables expert parallelism for MoE layers. Do not mix MoE tensor parallelism and expert parallelism in the same MoE layer.
• --no-enable-prefix-caching indicates that prefix caching is disabled. To enable it, remove this option.
• --block-size sets the KV cache block size. To enable the experimental 4K prefix cache hit support, change it from 128 to 32.
• --quantization ascend enables Ascend quantization for the W8A8 model.
• --speculative-config configures speculative decoding to accelerate inference. Use mtp for Multi-Token Prediction (MTP) and dspark for DSpark models. When using DSpark, num_speculative_tokens must be at least 5 (check the checkpoint's config.json).
• --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY"}' enables full ACL graph execution in the decode phase to reduce scheduling latency.
• --additional-config enables Ascend-specific optimizations. enable_npugraph_ex enables enhanced ACL graph execution, enable_static_kernel: false keeps static-kernel compilation disabled, enable_cpu_binding enables Ascend-native CPU binding, enable_dsa_cp enables DSA context parallelism, and multistream_overlap_shared_expert overlaps shared expert computation for better MoE throughput. DSA-CP depends on FlashComm1, and both options must be enabled explicitly.
• enable_flashcomm1 enables the FlashComm communication optimization.
• VLLM_PREFIX_CACHE_RETENTION_INTERVAL: Controls the retention interval, in tokens, for prefix-cache checkpoints of hybrid attention layers. It is applicable to DeepSeek-V4 and takes effect only when prefix caching is enabled. Under KV-cache pressure, it can improve the effective prefix-cache hit rate for reusable long prefixes. The value must be a non-negative multiple of --block-size; for DeepSeek-V4-Flash, 128 times --block-size is recommended. Set it to 4096 when --block-size is 32, or 16384 when --block-size is 128.

Common Issues Tip: If you encounter issues, please refer to the Public FAQ for troubleshooting.

Service Verification:

curl http://<node0_ip>:8900/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "dsv4",
        "messages": [
            {
                "role": "user",
                "content": "Who are you?"
            }
        ],
        "max_tokens": 256,
        "temperature": 0
    }'

Expected Result:

The service returns HTTP 200 OK with a JSON response containing the choices field.

5.2 Multi-Node PD Separation Deployment

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. This can lead to two issues:

1. Prefill preemption interrupts Decode: Prefill is a compute-intensive task that processes the entire input context at once, while Decode generates tokens one by one. When a new user request arrives, its Prefill phase can preempt and interrupt ongoing Decode tasks, causing jitter and higher time-per-output-token (TPOT) latency.
2. Inflexible resource allocation: Prefill and Decode have fundamentally different computational characteristics — Prefill is compute-bound and memory-bandwidth-intensive, while Decode is memory-bandwidth-bound. Running them on the same hardware forces a compromise that satisfies neither optimally.

PD (Prefill-Decode) separation addresses these issues by running Prefill and Decode on dedicated node groups, each configured independently. This architecture is recommended for production deployments with concurrent multi-user workloads, where stable latency and high throughput are both required.

The following sections describe PD separation deployment on both Atlas 800 A3 (128GB × 8) and Atlas 800 A2 (64GB × 8) multi-node environments.

5.2.1 A3 Series PD Separation Deployment

This section shows the deployment guide of DeepSeek-V4-Flash on Atlas 800 A3 (128GB × 8) 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(visible_devices, dp_rank, vllm_engine_port):
       command = [
           "bash",
           "./run_dp_template.sh",
           visible_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
           visible_devices = ",".join(str(x) for x in range(i * tp_size, (i + 1) * tp_size))
           process = multiprocessing.Process(target=run_command,
                                           args=(visible_devices, dp_rank,
                                                   vllm_engine_port))
           processes.append(process)
           process.start()
   
       for process in processes:
           process.join()
   

   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.
   --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.

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

A3 seriesA3 series with dspark

1. Prefill node

   nic_name="xxxx" # change to your own nic name
   local_ip=xx.xx.xx.1 # change to your own 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 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=2560
   export TASK_QUEUE_ENABLE=1
   export HCCL_OP_EXPANSION_MODE="AIV"
   export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
   export ASCEND_RT_VISIBLE_DEVICES=$1
   export VLLM_PREFIX_CACHE_RETENTION_INTERVAL=4096
   
   vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V4-Flash-w8a8-mtp \
       --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 dsv4 \
       --max-model-len 1048576 \
       --max-num-batched-tokens 8192 \
       --max-num-seqs 16 \
       --no-disable-hybrid-kv-cache-manager \
       --model-loader-extra-config='{"enable_multithread_load": true, "num_threads": 128}' \
       --speculative-config '{"num_speculative_tokens": 1,"method": "mtp","enforce_eager": true}' \
       --trust-remote-code \
       --block-size 32 \
       --tokenizer-mode deepseek_v4 \
       --tool-call-parser deepseek_v4 \
       --enable-auto-tool-choice \
       --reasoning-parser deepseek_v4 \
       --gpu-memory-utilization 0.9 \
       --quantization ascend \
       --enforce-eager \
       --additional-config '{"enable_cpu_binding": true, "enable_shared_expert_dp": true,  "enable_dsa_cp": true, "enable_flashcomm1":true}' \
       --kv-transfer-config \
       '{"kv_connector": "MooncakeHybridConnector",
       "kv_role": "kv_producer",
       "kv_port": "30000",
       "engine_id": "0",
       "kv_connector_extra_config": {
                   "prefill": {
                           "dp_size": 4,
                           "tp_size": 4
                   },
                   "decode": {
                           "dp_size": 16,
                           "tp_size": 1
                   }
           }
       }'
   

2. Decode node

   nic_name="xxxx" # change to your own nic name
   local_ip=xx.xx.xx.2 # change to your own ip
   
   export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
   export HCCL_OP_EXPANSION_MODE="AIV"
   export TASK_QUEUE_ENABLE=1
   export VLLM_RPC_TIMEOUT=3600000
   export VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=30000
   export HCCL_EXEC_TIMEOUT=204
   export HCCL_CONNECT_TIMEOUT=1200
   export HCCL_IF_IP=$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 HCCL_BUFFSIZE=1024
   export ASCEND_RT_VISIBLE_DEVICES=$1
   
   vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V4-Flash-w8a8-mtp \
       --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 dsv4 \
       --max-model-len 1048576 \
       --max-num-batched-tokens 120 \
       --max-num-seqs 60 \
       --block-size 32 \
       --no-disable-hybrid-kv-cache-manager \
       --no-enable-prefix-caching \
       --trust-remote-code \
       --tokenizer-mode deepseek_v4 \
       --model-loader-extra-config='{"enable_multithread_load": true, "num_threads": 128}' \
       --tool-call-parser deepseek_v4 \
       --enable-auto-tool-choice \
       --reasoning-parser deepseek_v4 \
       --gpu-memory-utilization 0.9 \
       --quantization ascend \
       --speculative-config '{"num_speculative_tokens": 1,"method": "mtp","enforce_eager": true}' \
       --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
       --kv-transfer-config \
       '{"kv_connector": "MooncakeHybridConnector",
       "kv_role": "kv_consumer",
       "kv_port": "30100",
       "engine_id": "1",
       "kv_connector_extra_config": {
                   "prefill": {
                           "dp_size": 4,
                           "tp_size": 4
                   },
                   "decode": {
                           "dp_size": 16,
                           "tp_size": 1
                   }
           }
       }' \
       --additional-config '{
           "ascend_compilation_config":{
               "enable_npugraph_ex":true,
               "enable_static_kernel":false
           },
           "enable_cpu_binding":true,
           "multistream_overlap_shared_expert":true,
           "recompute_scheduler_enable":true
       }'
   

1. Prefill node

   nic_name="xxxx" # change to your own nic name
   local_ip=xx.xx.xx.1 # change to your own 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 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=2560
   export TASK_QUEUE_ENABLE=1
   export HCCL_OP_EXPANSION_MODE="AIV"
   export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
   export ASCEND_RT_VISIBLE_DEVICES=$1
   export VLLM_PREFIX_CACHE_RETENTION_INTERVAL=4096
   
   vllm serve /path/to/DeepSeek-V4-Flash-0731-w8a8 \
       --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 dsv4-spark \
       --max-model-len 1048576 \
       --max-num-batched-tokens 8192 \
       --max-num-seqs 16 \
       --no-disable-hybrid-kv-cache-manager \
       --model-loader-extra-config='{"enable_multithread_load": true, "num_threads": 128}' \
       --speculative-config '{"num_speculative_tokens": 7,"method": "dspark","enforce_eager": true}' \
       --trust-remote-code \
       --block-size 32 \
       --tokenizer-mode deepseek_v4 \
       --tool-call-parser deepseek_v4 \
       --enable-auto-tool-choice \
       --reasoning-parser deepseek_v4 \
       --gpu-memory-utilization 0.9 \
       --quantization ascend \
       --enforce-eager \
       --additional-config '{"enable_cpu_binding": true, "enable_shared_expert_dp": true,  "enable_dsa_cp": false, "enable_flashcomm1":true}' \
       --kv-transfer-config \
       '{"kv_connector": "MooncakeHybridConnector",
       "kv_role": "kv_producer",
       "kv_port": "30000",
       "engine_id": "0",
       "kv_connector_extra_config": {
                   "prefill": {
                           "dp_size": 4,
                           "tp_size": 4
                   },
                   "decode": {
                           "dp_size": 16,
                           "tp_size": 1
                   }
           }
       }'
   

2. Decode node

   nic_name="xxxx" # change to your own nic name
   local_ip=xx.xx.xx.2 # change to your own ip
   
   export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
   export HCCL_OP_EXPANSION_MODE="AIV"
   export TASK_QUEUE_ENABLE=1
   export VLLM_RPC_TIMEOUT=3600000
   export VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=30000
   export HCCL_EXEC_TIMEOUT=204
   export HCCL_CONNECT_TIMEOUT=1200
   export HCCL_IF_IP=$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 HCCL_BUFFSIZE=1024
   export ASCEND_RT_VISIBLE_DEVICES=$1
   
   vllm serve /path/to/DeepSeek-V4-Flash-0731-w8a8 \
       --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 dsv4 \
       --max-model-len 1048576 \
       --max-num-batched-tokens 256 \
       --max-num-seqs 32 \
       --async-scheduling \
       --block-size 32 \
       --no-disable-hybrid-kv-cache-manager \
       --no-enable-prefix-caching \
       --trust-remote-code \
       --tokenizer-mode deepseek_v4 \
       --model-loader-extra-config='{"enable_multithread_load": true, "num_threads": 128}' \
       --tool-call-parser deepseek_v4 \
       --enable-auto-tool-choice \
       --reasoning-parser deepseek_v4 \
       --gpu-memory-utilization 0.9 \
       --quantization ascend \
       --speculative-config '{"num_speculative_tokens": 7,"method": "dspark","enforce_eager": true}' \
       --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
       --kv-transfer-config \
       '{"kv_connector": "MooncakeHybridConnector",
       "kv_role": "kv_consumer",
       "kv_port": "30100",
       "engine_id": "1",
       "kv_connector_extra_config": {
                   "prefill": {
                           "dp_size": 4,
                           "tp_size": 4
                   },
                   "decode": {
                           "dp_size": 16,
                           "tp_size": 1
                   }
           }
       }' \
       --additional-config '{
           "ascend_compilation_config":{
               "enable_npugraph_ex":true,
               "enable_static_kernel":false
           },
           "enable_cpu_binding":true,
           "multistream_overlap_shared_expert":true,
           "recompute_scheduler_enable":true
       }'
   

1. Start the server with the following command on each node.

   1. Prefill node

      # change ip to your own
      python launch_online_dp.py --dp-size 4 --tp-size 4 --dp-size-local 4 --dp-rank-start 0 --dp-address xx.xx.xx.1 --dp-rpc-port 12321 --vllm-start-port 7100
      

   2. Decode node

      # change ip to your own
      python launch_online_dp.py --dp-size 16 --tp-size 1 --dp-size-local 16 --dp-rank-start 0 --dp-address xx.xx.xx.2 --dp-rpc-port 12321 --vllm-start-port 7100
      

2. Deploy the P-D disaggregation proxy.

   Refer to Prefill-Decode Disaggregation (Deepseek) to deploy the P-D disaggregation proxy.

5.2.2 A2 Series PD Separation Deployment

This section shows the deployment guide of DeepSeek-V4-Flash on Atlas 800 A2 (64GB × 8) multi-node environment with 4*1P 1*4D 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(visible_devices, dp_rank, vllm_engine_port):
       command = [
           "bash",
           "./run_dp_template.sh",
           visible_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
           visible_devices = ",".join(str(x) for x in range(i * tp_size, (i + 1) * tp_size))
           process = multiprocessing.Process(target=run_command, args=(visible_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 (4 P nodes share the same script)

      For each P instance, only these two configuration values need to be modified: kv_port and engine_id. The engine_id should start from 0 and increment sequentially, while the kv_port (e.g., 30100) must be unique for each P instance, such as 30000, 30100, etc.

      unset ftp_proxy
      unset https_proxy
      unset http_proxy
      rm -rf ~/ascend/log
      
      nic_name="xxxxxx" #eg."enp67s0f0np0"
      local_ip=`hostname -I|awk -F " " '{print$1}'`
      
      export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
      export HCCL_OP_EXPANSION_MODE="AIV"
      export TASK_QUEUE_ENABLE=1
      export VLLM_RPC_TIMEOUT=3600000
      export VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=30000
      export HCCL_EXEC_TIMEOUT=204
      export HCCL_CONNECT_TIMEOUT=1200
      
      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 HCCL_BUFFSIZE=1024
      
      export ASCEND_RT_VISIBLE_DEVICES=$1
      export TASK_QUEUE_ENABLE=1
      
      vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V4-Flash-w8a8-mtp \
          --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 dsv4 \
          --max-model-len 135000 \
          --max-num-batched-tokens 4096 \
          --max-num-seqs 16 \
          --block-size 128 \
          --enforce-eager \
          --no-disable-hybrid-kv-cache-manager \
          --trust-remote-code \
          --gpu-memory-utilization 0.9 \
          --quantization ascend \
          --model-loader-extra-config='{"enable_multithread_load": true, "num_threads": 128}' \
          --tokenizer-mode deepseek_v4 \
          --tool-call-parser deepseek_v4 \
          --enable-auto-tool-choice \
          --reasoning-parser deepseek_v4 \
          --additional-config '{"enable_cpu_binding": true, "enable_shared_expert_dp": true}' \
          --speculative-config '{"num_speculative_tokens": 1, "method": "mtp","enforce_eager": true}' \
          --kv-transfer-config \
          '{"kv_connector": "MooncakeHybridConnector",
          "kv_role": "kv_producer",
          "kv_port": "30000",
          "engine_id": "0",
          "kv_connector_extra_config": {
                      "prefill": {
                          "dp_size": 8,
                          "tp_size": 1
                      },
                      "decode": {
                          "dp_size": 32,
                          "tp_size": 1
                      }
              }
          }'
      

   2. Decode node (4 D nodes share the same script)

      unset ftp_proxy
      unset https_proxy
      unset http_proxy
      rm -rf ~/ascend/log
      
      nic_name="xxxxxx" #eg."enp67s0f0np0"
      local_ip=`hostname -I|awk -F " " '{print$1}'`
      
      export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
      export HCCL_OP_EXPANSION_MODE="AIV"
      export TASK_QUEUE_ENABLE=1
      export VLLM_RPC_TIMEOUT=3600000
      export VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=30000
      export HCCL_EXEC_TIMEOUT=204
      export HCCL_CONNECT_TIMEOUT=1200
      
      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 HCCL_BUFFSIZE=1024
      
      export ASCEND_RT_VISIBLE_DEVICES=$1
      
      vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V4-Flash-w8a8-mtp \
          --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 dsv4 \
          --max-model-len 135000 \
          --max-num-batched-tokens 60 \
          --max-num-seqs 30 \
          --block-size 128 \
          --no-disable-hybrid-kv-cache-manager \
          --no-enable-prefix-caching \
          --trust-remote-code \
          --gpu-memory-utilization 0.9 \
          --quantization ascend \
          --model-loader-extra-config='{"enable_multithread_load": true, "num_threads": 128}' \
          --tokenizer-mode deepseek_v4 \
          --tool-call-parser deepseek_v4 \
          --enable-auto-tool-choice \
          --reasoning-parser deepseek_v4 \
          --speculative-config '{"num_speculative_tokens": 1, "method": "mtp","enforce_eager": true}' \
          --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
          --kv-transfer-config \
          '{"kv_connector": "MooncakeHybridConnector",
          "kv_role": "kv_consumer",
          "kv_port": "30400",
          "engine_id": "4",
          "kv_connector_extra_config": {
                      "prefill": {
                          "dp_size": 8,
                          "tp_size": 1
                      },
                      "decode": {
                          "dp_size": 32,
                          "tp_size": 1
                      }
              }
          }' \
          --additional-config '{
              "ascend_compilation_config":{
                    "enable_npugraph_ex":true,
                    "enable_static_kernel":false
              },
             "enable_cpu_binding":true,
             "multistream_overlap_shared_expert":true,
             "recompute_scheduler_enable":true
          }'
      

3. Start the server with the following command on each node.

   1. Prefill node

      # change ip to your own
      python launch_online_dp.py --dp-size 8 --tp-size 1 --dp-size-local 8 --dp-rank-start 0 --dp-address x.x.x.x --dp-rpc-port 12321 --vllm-start-port 7100
      

      For each P instance, only the --dp-address parameter differs and must be configured as the IP address of the service within the same subnet as the other instances.

   2. Decode node

      # change ip to your own
      python launch_online_dp.py --dp-size 32 --tp-size 1 --dp-size-local 8 --dp-rank-start x --dp-address x.x.x.x --dp-rpc-port 12321 --vllm-start-port 7100
      

      For each D instance, only the --dp-rank-start parameter differs, which should be configured as 0, 8, 16, and 24 respectively. Each instance's --dp-address must be set to the IP address of the main D node, which is the IP of the Decode instance with --dp-rank-start set to 0.

4. Deploy the P-D disaggregation proxy.

   The proxy is also implemented by referring to Prefill-Decode Disaggregation (Deepseek).

Key Parameter Descriptions:

• --no-disable-hybrid-kv-cache-manager keeps the hybrid KV cache manager enabled. DeepSeek-V4 KV Pool deployments require this flag; otherwise, the service may OOM during startup.
• --enforce-eager forces eager execution on prefill nodes instead of graph compilation.
• kv_connector_extra_config.prefill.dp_size/tp_size and decode.dp_size/tp_size must match the actual global DP and TP layout on the prefill and decode sides.
• enable_flashcomm1: enables the communication optimization function on the prefill nodes.
• recompute_scheduler_enable: true: enables the recomputation scheduler. When the 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, enable this configuration only on decode nodes.
• speculative-config: When DSpark is enabled, Prefill and Decode must use the same number of speculative tokens, and num_speculative_tokens must be at least 5 (check the checkpoint's config.json). For MTP, we recommend setting Prefill to 1 and Decode to the actual number of speculative tokens.
• MooncakeHybridConnector: the KV transfer connector used for PD separation, transferring KV Cache between prefill and decode nodes.
• enable_shared_expert_dp: true: enables data parallelism for shared experts, applicable to MoE models.
• VLLM_PREFIX_CACHE_RETENTION_INTERVAL: Controls the retention interval, in tokens, for prefix-cache checkpoints of hybrid attention layers. It is applicable to DeepSeek-V4 and takes effect only when prefix caching is enabled. Under KV-cache pressure, it can improve the effective prefix-cache hit rate for reusable long prefixes. The value must be a non-negative multiple of --block-size; for DeepSeek-V4-Flash, 128 times --block-size is recommended. Set it to 4096 when --block-size is 32, or 16384 when --block-size is 128.

Deployment Verification:

After the PD separation service is fully started, send a request through the proxy port on the prefill master node to verify that Prefill and Decode nodes are working correctly together. Refer to Prefill-Decode Disaggregation (Deepseek) for the proxy verification method.

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

5.2.3 Ultra-Long Sequence Deployment

For ultra-long sequence scenarios, support can be achieved by adjusting the PD (Prefill/Decode) ratio and the model parallelism strategy. For example, in a 1M sequence scenario, a 1*4P-1*4D ratio can be used, with the model parallelism set to DP4TP8 mode.

6 Functional Verification

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

In :, use the IP address and port number of the primary node. If the primary and standby nodes are separated, use the IP address and port number of the proxy node.

curl http://<node0_ip>:<port>/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "dsv4",
        "messages": [
            {
                "role": "user",
                "content": "Who are you?"
            }
        ],
        "max_tokens": 256,
        "temperature": 0
    }'

Expected Result:

The service returns HTTP 200 OK with a JSON response containing the choices field.

7 Accuracy Evaluation

Here is the accuracy evaluation method using AISBench.

Using AISBench

1. Refer to Using AISBench for details.

2. After execution, you can get the result.

dataset	version	metric	mode	vllm-api-general-chat	note
GPQA	-	accuracy	gen	88.17	1 Atlas 800 A3 (128GB × 8)
GSM8K	-	accuracy	gen	96.30	1 Atlas 800 A3 (128GB × 8)
GPQA	v0.25.1rc	accuracy	gen	90.40	A3 1P1D DSpark w8a8
SWE Multilingual	v0.25.1rc	accuracy	gen	68.33	A3 1P1D DSpark w8a8

8 Performance Evaluation

Using AISBench

Refer to Using AISBench for performance evaluation for details.

Using vLLM Benchmark

Refer to vllm benchmark for more details.

9 Performance Tuning

9.1 Recommended Configurations

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.

Table 1: Scenario Overview

*Total NPUs indicates the total number of NPUs used across all nodes.

Scenario	Deployment Mode	*Total NPUs	Weight Version	Key Considerations
High Throughput	Single-Node Mixed	16 (A3)	DeepSeek-V4-Flash-w8a8-mtp	Use dp4 tp4 to balance memory capacity and compute efficiency
High Throughput	1P1D deployment	32 (A3)	DeepSeek-V4-Flash-w8a8-mtp	dp16 tp1 on both P and D nodes; balanced latency and throughput
Long Context (1M)	Single-Node (A3)	8 (A3)	DeepSeek-V4-Flash-w8a8-mtp	Use dp4 tp4 to balance memory capacity and compute efficiency
Long Context (1M)	1P1D deployment	32 (A3)	DeepSeek-V4-Flash-w8a8-mtp	dp16 tp1 on both P and D nodes; balanced latency and throughput

Table 2: Detailed Node Configuration

Scenario	Configuration	NPUs	TP	DP	Max Num Seqs	Max Num Batched Tokens	Max Model Len	MTP Speculation Num
High Throughput (A3)	Server / Single Machine	8	4	4	64	10240	1048576	1
Long Context (1M, A3)	Server / Single Machine	8	4	4	64	10240	1048576	1
PD Separation (A3)	Server-P Node	8	4	4	16	8192	1048576	1
PD Separation (A3)	Server-D Node	8	1	16	60	120	1048576	1

For complete startup commands and parameter descriptions, please refer to the deployment examples in Chapter 5.

9.2 Tuning Guidelines

9.2.1 General Tuning Reference

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

Please refer to the Feature Guide for detailed feature descriptions.

10 FAQ

For common environment, installation, and general parameter issues, please refer to the Public FAQ; this chapter only covers model-specific issues.