Nvidia's DMS Cuts LLM Memory Usage by 8x
Nvidia introduces Dynamic Memory Scheduling that reduces large language model memory consumption by eight times, enabling more efficient AI inference and
Someone found that Nvidia’s new Dynamic Memory Sparsification (DMS) technique cuts LLM memory usage by up to 8x without accuracy loss.
The trick is pretty clever - they retrofitted existing models to let attention layers decide which tokens to keep or evict from the KV cache. There’s also a “delayed eviction” feature that marks low-importance tokens but keeps them accessible briefly, so the model can extract useful info before dumping them.
This means models can handle longer context, run faster, and serve more concurrent requests on the same hardware. Pretty big deal for anyone running local LLMs.
Full breakdown here: https://venturebeat.com/orchestration/nvidias-new-technique-cuts-llm-reasoning-costs-by-8x-without-losing-accuracy
Worth checking out if you’re into self-hosted setups - this could seriously reduce hardware requirements for running bigger models.
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