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