Practical solutions to solve the problem of insufficient memory
Memory management is the key challenge in the face of a large model with 685 billion participant count. The following are specific solutions:
1. Hardware optimization
- Use multi-GPU parallel computing to spread memory pressure
- Upgrade to a GPU with more video memory (e.g. A100 80GB, etc.)
2. Model optimization techniques
- Adoption of model parallelism frameworks such as DeepSpeed
- Utilizing Model Sharding Technology
- Enable Gradient Checkpointing
3. Accuracy adjustments
- Reduction in calculation accuracy: change from BF16 to F8_E4M3
- Selective use of mixed precision training
4. Batch optimization
- Reducing batch size
- Use of dynamic batch technology
Other practical tips
- Prioritize shorter input sequences
- Clean up unnecessary memory usage
- Regularly check CUDA memory usage
If the above methods are still ineffective, it is recommended to consider using cloud computing resources or applying for Hugging Face's inference service support.
This answer comes from the articleDeepSeek-V3.1-Base: a large-scale language model for efficiently processing complex tasksThe
May not be reproduced without permission:AI productivity tools " What should I do if I encounter out-of-memory problem when using DeepSeek-V3.1-Base?
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