feat: support GLM-5.3-Flash inference

[sufubao]

最终镜像

registry.ms-sc-01.maoshanwangtech.com/ms-ccr/lightllm@sha256:0260e9884e46de899f4b845aa3796d0603b7b6ee7593c1fded35b7cee4462169

H100 启动命令

IMAGE="registry.ms-sc-01.maoshanwangtech.com/ms-ccr/lightllm@sha256:0260e9884e46de899f4b845aa3796d0603b7b6ee7593c1fded35b7cee4462169"

sudo docker pull "$IMAGE"
sudo docker run -d \
  --name glm53-lightllm-16k256-kpool \
  --restart unless-stopped \
  --network host --ipc host --gpus all \
  -e LIGHTLLM_ENABLE_KPOOL_DECODE_FASTPATH=1 \
  -e PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \
  --ulimit memlock=-1 --ulimit nofile=1048576:1048576 \
  -v /home/devsft/models/GLM-5.3-Flash:/model:ro \
  -v /home/devsft/cache-glm53-lightllm:/root/.cache \
  -v /home/devsft/cache-glm53-triton:/root/.triton \
  -v /home/devsft/cache-glm53-deep-gemm:/root/.deep_gemm \
  "$IMAGE"

16K 输入 / 256 输出

同一台 8×H100 80GB、同一 FP8 checkpoint、TP8、32K batch token、关闭 prompt cache；总吞吐单位为 tok/s。

并发	LightLLM	vLLM	LightLLM / vLLM
1	5,373.86	6,542.89	82.13%
8	16,444.11	17,635.33	93.25%
16	20,669.61	25,246.99	81.87%
64	25,614.05	31,485.12	81.35%
128	25,588.22	27,988.09	91.43%
256	25,623.93	31,206.94	82.11%

精度：GSM8K 99/100；精确 16,384-token needle PASS，找回 ZEBRA-4821。