0
Likes
0
Saves
Back to updates

[r/ML] Optimizing Transformer model size & inference beyond FP16 + ONNX (pruning/graph opt didn’t help much) [P]

Impact: 5/10
Swipe left/right

Summary

A developer is seeking further optimization strategies for a Transformer model, having hit a plateau after implementing FP16 conversion and ONNX Runtime optimizations. They found that pruning (unstructured/structured) and ONNX graph optimizations did not yield significant additional gains, with the model size still around 162 MB. This highlights the challenges in achieving further efficiency improvements beyond standard techniques for Transformer models.

Editorial note

AI Dose summarizes public reporting and links to original sources when they are available. Review the Editorial Policy, Disclaimer, or Contact page if you need to flag a correction or understand how this site handles sources.

Continue Reading

Explore related coverage about community news and adjacent AI developments: [r/ML] [D] MYTHOS-INVERSION STRUCTURAL AUDIT, [r/LocalLLaMA] karpathy / autoresearch, [HN] Is anyone else bothered that AI agents can basically do what they want?, [r/ML] Why production systems keep making “correct” decisions that are no longer right [D].

Related Articles

Next read

[r/ML] [D] MYTHOS-INVERSION STRUCTURAL AUDIT

Stay with the thread by reading one adjacent story before leaving this update.

Comments

Sign in to leave a comment.

Loading comments...