推理专用架构设计
- 纯RDMA路径：绕过传统协议栈，时延降低至6ms以下
- Batch optimization：针对hidden_size=7168等常见配置预编译内核
- 零拷贝技术
Realization details
关键创新包括：
- 自适应路由技术(
NVSHMEM_ENABLE_ADAPTIVE_ROUTING) - 流水线式请求处理
- 动态负载均衡算法
usage example
#include "deep_ep.h"
void moe_infer(float* query, float* result, int batch_size) {
deep_ep_low_latency_all_to_all(query, result, batch_size);
}
性能验证方法
Run the test command:python tests/test_inference.py --batch_size 128 --hidden_size 7168
输出应包含：
- 单次推理时延(通常<10ms)
- 99%分位延迟数据
- GPU显存波动情况
This answer comes from the articleDeepEP: An Open Source Tool to Optimize Communication Efficiency Specifically for MoE Models (DeepSeek Open Source Week Day 2)The
May not be reproduced without permission:AI productivity tools " DeepEP针对推理场景做了哪些特殊优化？
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