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xc-llm-ascend/docs/source/tutorials/models/DeepSeek-V3.2.md
wangxiyuan 7d4833bce9 [Doc][Misc] Restructure tutorial documentation (#6501) 
### What this PR does / why we need it?

This PR refactors the tutorial documentation by restructuring it into
three categories: Models, Features, and Hardware. This improves the
organization and navigation of the tutorials, making it easier for users
to find relevant information.

- The single `tutorials/index.md` is split into three separate index
files:
  - `docs/source/tutorials/models/index.md`
  - `docs/source/tutorials/features/index.md`
  - `docs/source/tutorials/hardwares/index.md`
- Existing tutorial markdown files have been moved into their respective
new subdirectories (`models/`, `features/`, `hardwares/`).
- The main `index.md` has been updated to link to these new tutorial
sections.

This change makes the documentation structure more logical and scalable
for future additions.

### Does this PR introduce _any_ user-facing change?

Yes, this PR changes the structure and URLs of the tutorial
documentation pages. Users following old links to tutorials will
encounter broken links. It is recommended to set up redirects if the
documentation framework supports them.

### How was this patch tested?

These are documentation-only changes. The documentation should be built
and reviewed locally to ensure all links are correct and the pages
render as expected.

- vLLM version: v0.15.0
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.15.0

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
2026-02-10 15:03:35 +08:00

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# DeepSeek-V3.2
## Introduction
DeepSeek-V3.2 is a sparse attention model. The main architecture is similar to DeepSeek-V3.1, but with a sparse attention mechanism, which is designed to explore and validate optimizations for training and inference efficiency in long-context scenarios.
This document will show the main verification steps of the model, including supported features, feature configuration, environment preparation, single-node and multi-node deployment, accuracy and performance evaluation.
## Supported Features
Refer to [supported features](../../user_guide/support_matrix/supported_models.md) to get the model's supported feature matrix.
Refer to [feature guide](../../user_guide/feature_guide/index.md) to get the feature's configuration.
## Environment Preparation
### Model Weight
- `DeepSeek-V3.2-Exp`(BF16 version): require 2 Atlas 800 A3 (64G × 16) nodes or 4 Atlas 800 A2 (64G × 8) nodes. [Download model weight](https://modelers.cn/models/Modelers_Park/DeepSeek-V3.2-Exp-BF16)
- `DeepSeek-V3.2-Exp-w8a8`(Quantized version): require 1 Atlas 800 A3 (64G × 16) node or 2 Atlas 800 A2 (64G × 8) nodes. [Download model weight](https://modelers.cn/models/Modelers_Park/DeepSeek-V3.2-Exp-w8a8)
- `DeepSeek-V3.2`(BF16 version): require 2 Atlas 800 A3 (64G × 16) nodes or 4 Atlas 800 A2 (64G × 8) nodes. Model weight in BF16 not found now.
- `DeepSeek-V3.2-w8a8`(Quantized version): require 1 Atlas 800 A3 (64G × 16) node or 2 Atlas 800 A2 (64G × 8) nodes. [Download model weight](https://www.modelscope.cn/models/vllm-ascend/DeepSeek-V3.2-W8A8/)
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.md#verify-multi-node-communication).
### Installation
You can use our official docker image to run `DeepSeek-V3.2` directly.
:::::{tab-set}
:sync-group: install
::::{tab-item} A3 series
:sync: A3
Start the docker image on your each node.
```{code-block} bash
:substitutions:
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-a3
docker run --rm \
--name vllm-ascend \
--shm-size=1g \
--net=host \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--device /dev/davinci4 \
--device /dev/davinci5 \
--device /dev/davinci6 \
--device /dev/davinci7 \
--device /dev/davinci8 \
--device /dev/davinci9 \
--device /dev/davinci10 \
--device /dev/davinci11 \
--device /dev/davinci12 \
--device /dev/davinci13 \
--device /dev/davinci14 \
--device /dev/davinci15 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-it $IMAGE bash
```
::::
::::{tab-item} A2 series
:sync: A2
Start the docker image on your each node.
```{code-block} bash
:substitutions:
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend \
--shm-size=1g \
--net=host \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--device /dev/davinci4 \
--device /dev/davinci5 \
--device /dev/davinci6 \
--device /dev/davinci7 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-it $IMAGE bash
```
::::
:::::
In addition, if you don't want to use the docker image as above, you can also build all from source:
- Install `vllm-ascend` from source, refer to [installation](../../installation.md).
If you want to deploy multi-node environment, you need to set up environment on each node.
## Deployment
:::{note}
In this tutorial, we suppose you downloaded the model weight to `/root/.cache/`. Feel free to change it to your own path.
:::
### Single-node Deployment
- Quantized model `DeepSeek-V3.2-w8a8` can be deployed on 1 Atlas 800 A3 (64G × 16).
Run the following script to execute online inference.
```shell
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 VLLM_ASCEND_ENABLE_MLAPO=1
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V3.2-W8A8 \
--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"}'
```
### Multi-node Deployment
- `DeepSeek-V3.2-w8a8`: require at least 2 Atlas 800 A2 (64G × 8).
Run the following scripts on two nodes respectively.
:::::{tab-set}
:sync-group: install
::::{tab-item} A3 series
:sync: A3
**Node0**
```{code-block} bash
:substitutions:
# 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 VLLM_ASCEND_ENABLE_MLAPO=1
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V3.2-W8A8 \
--host 0.0.0.0 \
--port 8077 \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 12890 \
--tensor-parallel-size 16 \
--quantization ascend \
--seed 1024 \
--served-model-name deepseek_v3_2 \
--enable-expert-parallel \
--max-num-seqs 16 \
--max-model-len 8192 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.92 \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}'
```
**Node1**
```{code-block} bash
:substitutions:
# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="xxx"
# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxxx"
export HCCL_OP_EXPANSION_MODE="AIV"
export HCCL_IF_IP=$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 VLLM_ASCEND_ENABLE_MLAPO=1
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-V3.2-W8A8 \
--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 $node0_ip \
--data-parallel-rpc-port 12890 \
--tensor-parallel-size 16 \
--quantization ascend \
--seed 1024 \
--served-model-name deepseek_v3_2 \
--enable-expert-parallel \
--max-num-seqs 16 \
--max-model-len 8192 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.92 \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}'
```
::::
::::{tab-item} A2 series
:sync: A2
**Node0**
```{code-block} bash
:substitutions:
# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="xxx"
# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxxx"
export HCCL_OP_EXPANSION_MODE="AIV"
export HCCL_IF_IP=$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 VLLM_ASCEND_ENABLE_MLAPO=1
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_ENABLE_FLASHCOMM1=0
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 \
--host 0.0.0.0 \
--port 8077 \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 13389 \
--tensor-parallel-size 8 \
--quantization ascend \
--seed 1024 \
--served-model-name deepseek_v3_2 \
--enable-expert-parallel \
--max-num-seqs 16 \
--max-model-len 8192 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.92 \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY", "cudagraph_capture_sizes":[3,6,9,12,15,18,21,24,27,30,33,36,39,42,45,48]}' \
--speculative-config '{"num_speculative_tokens": 2, "method": "deepseek_mtp"}'
```
**Node1**
```{code-block} bash
:substitutions:
# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="xxx"
# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxxx"
export HCCL_OP_EXPANSION_MODE="AIV"
export HCCL_IF_IP=$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 VLLM_ASCEND_ENABLE_MLAPO=1
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_ENABLE_FLASHCOMM1=0
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 \
--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":[3,6,9,12,15,18,21,24,27,30,33,36,39,42,45,48]}' \
--speculative-config '{"num_speculative_tokens": 2, "method": "deepseek_mtp"}'
```
::::
:::::
### Prefill-Decode Disaggregation
We'd like to show the deployment guide of `DeepSeek-V3.2` on multi-node environment with 1P1D for better performance.
Before you start, please
1. prepare the script `launch_online_dp.py` on each node.
```python
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 0
```shell
nic_name="enp48s3u1u1" # change to your own nic name
local_ip=141.61.39.105 # change to your own ip
export HCCL_OP_EXPANSION_MODE="AIV"
export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=256
export ASCEND_AGGREGATE_ENABLE=1
export ASCEND_TRANSPORT_PRINT=1
export ACL_OP_INIT_MODE=1
export ASCEND_A3_ENABLE=1
export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000
export ASCEND_RT_VISIBLE_DEVICES=$1
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1
vllm serve /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-QuaRot \
--host 0.0.0.0 \
--port $2 \
--data-parallel-size $3 \
--data-parallel-rank $4 \
--data-parallel-address $5 \
--data-parallel-rpc-port $6 \
--tensor-parallel-size $7 \
--enable-expert-parallel \
--speculative-config '{"num_speculative_tokens": 2, "method":"deepseek_mtp"}' \
--profiler-config \
'{"profiler": "torch",
"torch_profiler_dir": "./vllm_profile",
"torch_profiler_with_stack": false}' \
--seed 1024 \
--served-model-name dsv3 \
--max-model-len 68000 \
--max-num-batched-tokens 32550 \
--trust-remote-code \
--max-num-seqs 64 \
--gpu-memory-utilization 0.82 \
--quantization ascend \
--enforce-eager \
--no-enable-prefix-caching \
--additional-config '{"layer_sharding": ["q_b_proj", "o_proj"]}' \
--kv-transfer-config \
'{"kv_connector": "MooncakeConnectorV1",
"kv_role": "kv_producer",
"kv_port": "30000",
"engine_id": "0",
"kv_connector_extra_config": {
"use_ascend_direct": true,
"prefill": {
"dp_size": 2,
"tp_size": 16
},
"decode": {
"dp_size": 8,
"tp_size": 4
}
}
}'
```
2. Prefill node 1
```shell
nic_name="enp48s3u1u1" # change to your own nic name
local_ip=141.61.39.113 # change to your own ip
export HCCL_OP_EXPANSION_MODE="AIV"
export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=256
export ASCEND_AGGREGATE_ENABLE=1
export ASCEND_TRANSPORT_PRINT=1
export ACL_OP_INIT_MODE=1
export ASCEND_A3_ENABLE=1
export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000
export ASCEND_RT_VISIBLE_DEVICES=$1
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1
vllm serve /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-QuaRot \
--host 0.0.0.0 \
--port $2 \
--data-parallel-size $3 \
--data-parallel-rank $4 \
--data-parallel-address $5 \
--data-parallel-rpc-port $6 \
--tensor-parallel-size $7 \
--enable-expert-parallel \
--speculative-config '{"num_speculative_tokens": 2, "method":"deepseek_mtp"}' \
--profiler-config \
'{"profiler": "torch",
"torch_profiler_dir": "./vllm_profile",
"torch_profiler_with_stack": false}' \
--seed 1024 \
--served-model-name dsv3 \
--max-model-len 68000 \
--max-num-batched-tokens 32550 \
--trust-remote-code \
--max-num-seqs 64 \
--gpu-memory-utilization 0.82 \
--quantization ascend \
--enforce-eager \
--no-enable-prefix-caching \
--additional-config '{"layer_sharding": ["q_b_proj", "o_proj"]}' \
--kv-transfer-config \
'{"kv_connector": "MooncakeConnectorV1",
"kv_role": "kv_producer",
"kv_port": "30000",
"engine_id": "0",
"kv_connector_extra_config": {
"use_ascend_direct": true,
"prefill": {
"dp_size": 2,
"tp_size": 16
},
"decode": {
"dp_size": 8,
"tp_size": 4
}
}
}'
```
3. Decode node 0
```shell
nic_name="enp48s3u1u1" # change to your own nic name
local_ip=141.61.39.117 # change to your own ip
export HCCL_OP_EXPANSION_MODE="AIV"
export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
#Mooncake
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=256
export ASCEND_AGGREGATE_ENABLE=1
export ASCEND_TRANSPORT_PRINT=1
export ACL_OP_INIT_MODE=1
export ASCEND_A3_ENABLE=1
export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000
export TASK_QUEUE_ENABLE=1
export ASCEND_RT_VISIBLE_DEVICES=$1
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 $7 \
--enable-expert-parallel \
--speculative-config '{"num_speculative_tokens": 2, "method":"deepseek_mtp"}' \
--profiler-config \
'{"profiler": "torch",
"torch_profiler_dir": "./vllm_profile",
"torch_profiler_with_stack": false}' \
--seed 1024 \
--served-model-name dsv3 \
--max-model-len 68000 \
--max-num-batched-tokens 12 \
--compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY", "cudagraph_capture_sizes":[3, 6, 9, 12]}' \
--trust-remote-code \
--max-num-seqs 4 \
--gpu-memory-utilization 0.95 \
--no-enable-prefix-caching \
--async-scheduling \
--quantization ascend \
--kv-transfer-config \
'{"kv_connector": "MooncakeConnectorV1",
"kv_role": "kv_consumer",
"kv_port": "30100",
"engine_id": "1",
"kv_connector_extra_config": {
"use_ascend_direct": true,
"prefill": {
"dp_size": 2,
"tp_size": 16
},
"decode": {
"dp_size": 8,
"tp_size": 4
}
}
}' \
--additional-config '{"recompute_scheduler_enable" : true}'
```
4. Decode node 1
```shell
nic_name="enp48s3u1u1" # change to your own nic name
local_ip=141.61.39.181 # change to your own ip
export HCCL_OP_EXPANSION_MODE="AIV"
export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
#Mooncake
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=256
export ASCEND_AGGREGATE_ENABLE=1
export ASCEND_TRANSPORT_PRINT=1
export ACL_OP_INIT_MODE=1
export ASCEND_A3_ENABLE=1
export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000
export TASK_QUEUE_ENABLE=1
export ASCEND_RT_VISIBLE_DEVICES=$1
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 dsv3 \
--max-model-len 68000 \
--max-num-batched-tokens 12 \
--compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY", "cudagraph_capture_sizes":[3, 6, 9, 12]}' \
--trust-remote-code \
--async-scheduling \
--max-num-seqs 4 \
--gpu-memory-utilization 0.95 \
--no-enable-prefix-caching \
--quantization ascend \
--kv-transfer-config \
'{"kv_connector": "MooncakeConnectorV1",
"kv_role": "kv_consumer",
"kv_port": "30100",
"engine_id": "1",
"kv_connector_extra_config": {
"use_ascend_direct": true,
"prefill": {
"dp_size": 2,
"tp_size": 16
},
"decode": {
"dp_size": 8,
"tp_size": 4
}
}
}' \
--additional-config '{"recompute_scheduler_enable" : true}'
```
Once the preparation is done, you can start the server with the following command on each node:
1. Prefill node 0
```shell
# change ip to your own
python launch_online_dp.py --dp-size 2 --tp-size 16 --dp-size-local 1 --dp-rank-start 0 --dp-address 141.61.39.105 --dp-rpc-port 12890 --vllm-start-port 9100
```
2. Prefill node 1
```shell
# change ip to your own
python launch_online_dp.py --dp-size 2 --tp-size 16 --dp-size-local 1 --dp-rank-start 1 --dp-address 141.61.39.105 --dp-rpc-port 12890 --vllm-start-port 9100
```
3. Decode node 0
```shell
# change ip to your own
python launch_online_dp.py --dp-size 8 --tp-size 4 --dp-size-local 4 --dp-rank-start 0 --dp-address 141.61.39.117 --dp-rpc-port 12777 --vllm-start-port 9100
```
4. Decode node 1
```shell
# change ip to your own
python launch_online_dp.py --dp-size 8 --tp-size 4 --dp-size-local 4 --dp-rank-start 4 --dp-address 141.61.39.117 --dp-rpc-port 12777 --vllm-start-port 9100
```
## Functional Verification
Once your server is started, you can query the model with input prompts:
```shell
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
}'
```
## Accuracy Evaluation
Here are two accuracy evaluation methods.
### Using AISBench
1. Refer to [Using AISBench](../../developer_guide/evaluation/using_ais_bench.md) 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](../../developer_guide/evaluation/using_lm_eval.md) for `lm_eval` installation.
2. Run `lm_eval` to execute the accuracy evaluation.
```shell
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.
## Performance
### Using AISBench
Refer to [Using AISBench for performance evaluation](../../developer_guide/evaluation/using_ais_bench.md#execute-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](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) 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.
```shell
export VLLM_USE_MODELSCOPE=true
vllm bench serve --model /root/.cache/Eco-Tech/DeepSeek-V3.2-w8a8-mtp-QuaRot --dataset-name random --random-input 200 --num-prompt 200 --request-rate 1 --save-result --result-dir ./
```
## Function Call
The function call feature is supported from v0.13.0rc1 on. Please use the latest version.
Refer to [DeepSeek-V3.2 Usage Guide](https://docs.vllm.ai/projects/recipes/en/latest/DeepSeek/DeepSeek-V3_2.html#tool-calling-example) for details.
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