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A Reddit user shared a method to significantly boost the processing speed of prompts for large language models like gpt-oss-120b, particularly when using partially offloaded models. By increasing the batch size (`-ub`) and adjusting the number of CPU-MoE layers (`–n-cpu-moe`), they observed substantial improvements in throughput.
For instance, with a 24 GB RTX 3090 GPU, doubling the micro-batch from the default 512 to 4096 improved prompt processing speed by about 8.7x compared to the original setting. This optimization also slightly reduced token generation speed but maintained acceptable performance for typical use cases.
This technique is valuable for users who need faster response times, especially in contexts requiring frequent large-scale text analysis or synthesis tasks. It highlights how tweaking model parameters can lead to significant gains without necessitating more powerful hardware.
- Increasing the batch size (`-ub`) can dramatically increase prompt processing speed for larger models like gpt-oss-120b, especially when combined with appropriate CPU-MoE layer adjustments.
- The trade-off is a slight reduction in token generation speed but remains manageable for most applications.
- This method demonstrates the importance of fine-tuning model parameters to optimize specific use cases without requiring additional hardware investments.
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