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ERNIE-4.5-21B-A3B

Environmental Preparation

1.1 Hardware requirements

The minimum number of GPUs required to deploy ERNIE-4.5-21B-A3B on the following hardware for each quantization is as follows:

WINT8 WINT4 FP8
H800 80GB 1 1 1
A800 80GB 1 1 /
H20 96GB 1 1 1
L20 48GB 1 1 1
A30 40GB 2 1 /
A10 24GB 2 1 /

Tips: 1. To modify the number of deployment GPUs, specify --tensor-parallel-size 2 in starting command. 2. For hardware not listed in the table, you can estimate whether it can be deployed based on the GPU memory.

1.2 Install fastdeploy and prepare the model

  • Installation: For detail, please refer to Fastdeploy Installation.

  • Model Download，For detail, please refer to Supported Models. Please note that models with Paddle suffix need to be used for Fastdeploy：

2.How to Use

2.1 Basic: Launching the Service

Start the service by following command:

python -m fastdeploy.entrypoints.openai.api_server \
       --model baidu/ERNIE-4.5-21B-A3B-Paddle \
       --tensor-parallel-size 1 \
       --quantization wint4 \
       --max-model-len 32768 \
       --kv-cache-ratio 0.75 \
       --max-num-seqs 128
  • --quantization: indicates the quantization strategy used by the model. Different quantization strategies will result in different performance and accuracy of the model. It could be one of wint8 / wint4 / block_wise_fp8(Hopper is needed).
  • --max-model-len: Indicates the maximum number of tokens supported by the currently deployed service. The larger the value, the longer the context length the model can support, but the more GPU memory is occupied, which may affect the concurrency.

For more parameter meanings and default settings, see FastDeploy Parameter Documentation。

2.2 Advanced: How to get better performance

2.2.1 Correctly set parameters that match the application scenario

Evaluate average input length, average output length, and maximum context length - Set max-model-len according to the maximum context length. For example, if the average input length is 1000 and the output length is 30000, then it is recommended to set it to 32768 - Enable the service management global block

export ENABLE_V1_KVCACHE_SCHEDULER=1

2.2.2 Prefix Caching

Idea: The core idea of Prefix Caching is to avoid repeated calculations by caching the intermediate calculation results of the input sequence (KV Cache), thereby speeding up the response speed of multiple requests with the same prefix. For details, refer to prefix-cache

How to enable: Add the following lines to the startup parameters, where --enable-prefix-caching enables prefix caching, and --swap-space enables CPU cache in addition to GPU cache. The size is GB and should be adjusted according to the actual situation of the machine.

--enable-prefix-caching
--swap-space 50

2.2.3 Chunked Prefill

Idea: This strategy is adopted to split the prefill stage request into small-scale sub-chunks, and execute them in batches mixed with the decode request. This can better balance the computation-intensive (Prefill) and memory-intensive (Decode) operations, optimize GPU resource utilization, reduce the computational workload and memory usage of a single Prefill, thereby reducing the peak memory usage and avoiding the problem of insufficient memory. For details, please refer to Chunked Prefill

How to enable: Add the following lines to the startup parameters

--enable-chunked-prefill

2.2.4 MTP (Multi-Token Prediction)

Idea: By predicting multiple tokens at once, the number of decoding steps is reduced to significantly speed up the generation speed, while maintaining the generation quality through certain strategies. For details, please refer to Speculative Decoding。

How to enable: Add the following lines to the startup parameters

--speculative-config '{"method": "mtp", "num_speculative_tokens": 1, "model": "${path_to_mtp_model}"}'

2.2.5 CUDAGraph

Idea: CUDAGraph is a GPU computing acceleration technology provided by NVIDIA. It achieves efficient execution and optimization of GPU tasks by capturing CUDA operation sequences into a graph structure. The core idea of CUDAGraph is to encapsulate a series of GPU computing and memory operations into a re-executable graph, thereby reducing CPU-GPU communication overhead, reducing kernel startup latency, and improving overall computing performance.

How to enable: Add the following lines to the startup parameters

--use-cudagraph

Notes: 1. Usually, no additional parameters need to be set, but CUDAGraph will generate some additional memory overhead, which may need to be adjusted in some scenarios with limited memory. For detailed parameter adjustments, please refer to GraphOptimizationBackend for related configuration parameter descriptions 2. When CUDAGraph is enabled, only single-card inference is supported, that is, --tensor-parallel-size 1 3. When CUDAGraph is enabled, it is not supported to enable Chunked Prefill and Prefix Caching at the same time

2.2.6 Rejection Sampling

Idea: Rejection sampling is to generate samples from a proposal distribution that is easy to sample, avoiding explicit sorting to increase the sampling speed, which has a significant improvement on small-sized models.

How to enable: Add the following environment variables before starting

export FD_SAMPLING_CLASS=rejection

2.2.7 Disaggregated Deployment

Idea: Deploying Prefill and Decode separately in certain scenarios can improve hardware utilization, effectively increase throughput, and reduce overall sentence latency.

How to enable: Take the deployment of a single machine with 8 GPUs and 1P1D (4 GPUs each) as an example. Compared with the default hybrid deployment method, --splitwise-role is required to specify the role of the node. And the GPUs and logs of the two nodes are isolated through the environment variables FD_LOG_DIR and CUDA_VISIBLE_DEVICES.

# prefill
export CUDA_VISIBLE_DEVICES=0,1,2,3
export INFERENCE_MSG_QUEUE_ID=1315
export FLAGS_max_partition_size=2048
export FD_ATTENTION_BACKEND=FLASH_ATTN
export FD_LOG_DIR="prefill_log"

quant_type=block_wise_fp8
export FD_USE_DEEP_GEMM=0

python -m fastdeploy.entrypoints.openai.api_server --model baidu/ERNIE-4.5-21B-A3B-Paddle \
    --max-model-len 131072 \
    --max-num-seqs 20 \
    --num-gpu-blocks-override 40000 \
    --quantization ${quant_type} \
    --gpu-memory-utilization 0.9 --kv-cache-ratio 0.9 \
    --port 7012 --engine-worker-queue-port 7013 --metrics-port 7014 --tensor-parallel-size 4 \
    --cache-queue-port 7015 \
    --splitwise-role "prefill" \

# decode
export CUDA_VISIBLE_DEVICES=4,5,6,7
export INFERENCE_MSG_QUEUE_ID=1215
export FLAGS_max_partition_size=2048
export FD_LOG_DIR="decode_log"

quant_type=block_wise_fp8
export FD_USE_DEEP_GEMM=0

python -m fastdeploy.entrypoints.openai.api_server --model baidu/ERNIE-4.5-21B-A3B-Paddle \
    --max-model-len 131072 \
    --max-num-seqs 20 \
    --quantization ${quant_type} \
    --gpu-memory-utilization 0.85 --kv-cache-ratio 0.1 \
    --port 9012 --engine-worker-queue-port 8013 --metrics-port 8014 --tensor-parallel-size 4 \
    --cache-queue-port 8015 \
    --innode-prefill-ports 7013 \
    --splitwise-role "decode"

FAQ

If you encounter any problems during use, you can refer to FAQ.