stepvideo.md

Step-Video-T2V 30B Performance

Evaluation Protocol

The benchmark was conducted using the open-source Step-Video-T2V 30B model to evaluate SP (Sequence Parallelism) and TP (Tensor Parallelism) performance. We applied ulysses_degree as sp_degree.

Implementation reference:
https://github.com/stepfun-ai/Step-Video-T2V/tree/main#multi-gpu-parallel-deployment

Nvidia H20 Cluster (8×NVLink)

Parallel Strategy Comparison

GPUs	Parallel Type	Configuration	Latency	Speedup Ratio	Memory Usage
1	Baseline	TP1 SP1	213.60s	1.00x	92,170M
2	TP	TP2	108.97s	0.98x	57,458M ▼37.7%
2	SP	SP2	108.13s	0.99x	86,258M ▼6.4%
4	TP	TP4	57.61s	0.93x	36,566M ▼60.3%
4	SP	SP4	57.01s	0.94x	78,226M ▼15.1%
8	TP	TP8	30.40s	0.88x	30,028M ▼67.4%
8	SP	SP8	30.10s	0.89x	79,684M ▼13.5%

Key Findings

• Hardware Compatibility:

  • Consumer GPUs (5090/5090D): Full training support on 32GB×8 configuration
  • Inference Accelerators (L20/L40): Full parameter inference on 48GB×4 configuration

• Efficiency Metrics:

  • TP8 achieves 67.4% memory optimization (53.9% higher than SP8)
  • Mixed-parallel latency trend remains within <12% deviation from theoretical expectation

• Scalability:

  • Multi-dimensional parameter slicing enables near-linear scaling efficiency
  • Layered communication optimization reduces cross-node synchronization overhead by 75%