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Lower FLOPs, lower latency—right?

This story is from 2026-08-20. It is preserved in the archive; the latest stories are on the live feed.

Not always. Token pruning frameworks like HiPrune have shown major speedups on models like LLaVA-NeXT-7B. Here, pruning reduced visual tokens from 2,880 to 160 and cut prefill latency from 272 ms to 29.7 ms. On Gemma 4 E4B, which starts with only ~262 visual tokens on average, HiPrune retained 99.2…

Read the full story at r/learnmachinelearning ↗

Timeline · 2 reports

  1. 2026-08-20 16:30 · r/learnmachinelearning
    Lower FLOPs, lower latency—right?
  2. 2026-08-20 16:29 · r/computervision
    Lower FLOPs, lower latency—right?

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