Solutions to optimize inference speed
For resource-limited GPU environments, performance and resource consumption can be balanced in the following ways:
- Data type demotion
Prefer F8_E4M3 format (hardware support required), compared with BF16, it can reduce the memory consumption by 50% but may lose some accuracy. Load the model through thetorch_dtype="f8_e4m3"parameter realization - Model Segmentation Techniques
Using Hugging Face'sdevice_mapfunction splits the model into multiple GPUs:model = AutoModelForCausalLM.from_pretrained(..., device_map="balanced") - Batch optimization
When multiple requests are processed at the same time, thepadding=Trueparameter to enable dynamic batch processing, which significantly increases throughput but requires monitoring of video memory usage. - quantitative compression
The use of 4-bit quantization (requires installation of the bitsandbytes library) reduces the model by a factor of 4:model = AutoModelForCausalLM.from_pretrained(..., load_in_4bit=True) - caching mechanism
Create a local caching system for duplicate queries, especially for Q&A scenarios
Implementation Recommendation: Prioritize testing the quantization scheme, and then try a combination of sharding + data type degradation scheme if it does not work well.
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 " How to optimize the inference speed of DeepSeek-V3.1-Base model in a resource-limited GPU environment?
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