FP8 model core values
- Video Memory Saving: Reduction of 50% communication data compared to BF16
- Energy Efficiency Improvement: Reduce HBM access power consumption
- Controlled precision: Maintaining model accuracy through loss compensation algorithms
Configuration steps
- Check hardware support: Ampere architecture and above GPU required
- Specify explicitly in the communication interface
FP8data type - utilization
test_fp8.pyVerification of accuracy loss
Tuning Recommendations
- Mixing accuracy: Keep BF16 for the key layer, FP8 for the rest.
- scaling factor: Dynamically adjusts to the tensor range
- Monitoring Indicators::
- Gradient spillover rate
- Weighting update range
- Loss function convergence curve
Typical Benefits
Actual cases show:
On an 8-node cluster, FP8 mode enables:
- 1.8x faster training iterations
- Total energy consumption reduced by 35%
- Final accuracy loss <0.5%
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 " How can DeepEP's FP8 support be utilized in resource constrained environments?
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