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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 /

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. 3. ERNIE-4.5-21B-A3B-Thinking requires FastDeploy version >= 2.2.

1.2 Install fastdeploy and prepare the model

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-Thinking \
       --load_choices "default_v1" \
       --tensor-parallel-size 1 \
       --max-model-len 131072 \
       --quantization wint8 \
       --reasoning-parser ernie_x1 \
       --tool-call-parser ernie_x1 \
       --max-num-seqs 32
  • --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.
  • --load_choices: Indicates the version of the loader. "default_v1" means enabling the v1 version of the loader, which has faster loading speed and less memory usage.
  • --reasoning-parser, --tool-call-parser: Indicates the corresponding reasoning content and tool call parser.

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 2000 and the output length is 80000, then it is recommended to set it to 131072

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: Since version 2.2 (including the develop branch), Prefix Caching has been enabled by default.

For versions 2.1 and earlier, you need to enable it manually by adding 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. The recommended value is (total machine memory - model size) * 20%. If the service fails to start because other programs are occupying memory, try reducing the --swap-space value.

--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: Since version 2.2 (including the develop branch), Chunked Prefill has been enabled by default.

For versions 2.1 and earlier, you need to enable it manually by adding

--enable-chunked-prefill

2.2.4 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: - 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

2.2.5 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

FAQ

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