Lobotomy-less REAP by Samsung (REAM)
A new technique could make massive AI models dramatically smaller and cheaper to run.
Deep Dive
Samsung has reportedly developed REAM, an alternative to Cerebras's REAP method for model compression. Early examples show Qwen3 models being shrunk from 235B to 108B and 80B to 60B parameters. The key claim is that REAM causes less performance damage than previous pruning techniques. This could enable running billion-parameter models on consumer hardware, though questions remain about its compatibility with quantization and fine-tuning.
Why It Matters
If effective, this could drastically reduce the cost and hardware needed to deploy state-of-the-art large language models.