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<div class="md-content" data-md-component="content">
<article class="md-content__inner md-typeset">
<h1 id="metax-gpu-c550-ernie-45">使用 Metax GPU C550 运行ERNIE 4.5 系列模型</h1>
<p>FastDeploy在Metax C550上对ERNIE 4.5系列模型进行了深度适配和优化，实现了推理入口和GPU的统一，无需修改即可完成推理任务的迁移。</p>
<p>环境准备：
- Python &gt;= 3.10
- Linux X86_64</p>
<table>
<thead>
<tr>
<th style="text-align: center;">Chip Type</th>
<th style="text-align: center;">Driver Version</th>
<th style="text-align: center;">KMD Version</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: center;">MetaX C550</td>
<td style="text-align: center;">3.0.0.1</td>
<td style="text-align: center;">2.14.6</td>
</tr>
</tbody>
</table>
<h2 id="1">1. 容器镜像获取</h2>
<pre><code class="language-shell">docker login --username=cr_temp_user --password=eyJpbnN0YW5jZUlkIjoiY3JpLXpxYTIzejI2YTU5M3R3M2QiLCJ0aW1lIjoiMTc1NTUxODEwODAwMCIsInR5cGUiOiJzdWIiLCJ1c2VySWQiOiIyMDcwOTQwMTA1NjYzNDE3OTIifQ:8226ca50ce5476c42062e24d3c465545de1c1780 cr.metax-tech.com &amp;&amp; docker pull cr.metax-tech.com/public-library/maca-native:3.0.0.4-ubuntu20.04-amd64
</code></pre>
<h2 id="2">2. 预安装</h2>
<pre><code class="language-shell">1）pip install paddlepaddle==3.0.0.dev20250729 -i https://www.paddlepaddle.org.cn/packages/nightly/cpu/
2）pip install paddle-metax-gpu==3.0.0.dev20250807 -i https://www.paddlepaddle.org.cn/packages/nightly/maca/
</code></pre>
<h2 id="3-fastdeploy">3. FastDeploy代码下载并编译</h2>
<pre><code class="language-shell">git clone https://github.com/PaddlePaddle/FastDeploy
cd FastDeploy
bash build.sh
</code></pre>
<p>The built packages will be in the <code>FastDeploy/dist</code> directory.</p>
<h2 id="4">4. 环境验证</h2>
<p>After installation, verify the environment with this Python code:</p>
<pre><code class="language-python">import paddle
from paddle.jit.marker import unified
# Verify GPU availability
paddle.utils.run_check()
# Verify FastDeploy custom operators compilation
from fastdeploy.model_executor.ops.gpu import beam_search_softmax
</code></pre>
<p>If the above code executes successfully, the environment is ready.</p>
<h2 id="5">5. 示例</h2>
<p>from fastdeploy import LLM, SamplingParams</p>
<p>prompts = [
"Hello. My name is",
]</p>
<p>sampling_params = SamplingParams(top_p=0.95, max_tokens=32, temperature=0.6)</p>
<p>llm = LLM(model="/root/model/ERNIE-4.5-21B-A3B-Paddle", tensor_parallel_size=1, max_model_len=256, engine_worker_queue_port=9135, quantization='wint8', static_decode_blocks=0, gpu_memory_utilization=0.9)</p>
<p>outputs = llm.generate(prompts, sampling_params)</p>
<p>print(f"Generated {len(outputs)} outputs")
print("=" * 50 + "\n")</p>
<p>for output in outputs:
prompt = output.prompt
generated_text = output.outputs.text
print(prompt)
print(generated_text)
print("-" * 50)</p>
<p>输出：
INFO 2025-08-18 10:54:18,455 416822 engine.py[line:202] Waiting worker processes ready...
Loading Weights: 100%|█████████████████████████████████████████████████████████████████████████| 100/100 [03:33&lt;00:00, 2.14s/it]
Loading Layers: 100%|██████████████████████████████████████████████████████████████████████████| 100/100 [00:18&lt;00:00, 5.54it/s]
INFO 2025-08-18 10:58:16,149 416822 engine.py[line:247] Worker processes are launched with 240.08204197883606 seconds.
Processed prompts: 100%|███████████████████████| 1/1 [00:21&lt;00:00, 21.84s/it, est. speed input: 0.00 toks/s, output: 0.00 toks/s]
Generated 1 outputs
==================================================</p>
<p>Hello. My name is
Alice and I'm here to help you. What can I do for you today?
Hello Alice! I'm trying to organize a small party</p>
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