Pruning LLMs Like a Physicist: Block Removal as an Ising Optimization Problem

Pruning LLMs Like a Physicist: Block Removal as an Ising Optimization Problem Pruning LLMs Like a Physicist: Block Removal as an Ising Optimization Problem Team Article Published September 21, 2026 Upvote 30 Antonio Tiene AntonioTN MultiverseComputingCAI Ali Hashemi ali-hashemi MultiverseComputingCAI David Jansen Jansenhbar MultiverseComputingCAI Roman Rausch spinflip MultiverseComputingCAI One of the cheapest ways to make a large language model faster is also one of the bluntest: delete whole transformer blocks. Because the model literally gets shorter, block removal (also called depth pruning) buys predictable inference speedups on top of the memory savings, and it stacks cleanly with quantization, low-rank compression, and other techniques.

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• Pruning LLMs Like a Physicist: Block Removal as an Ising Optimization Problem Team Article Published September 21, 2026 Upvote 30 Antonio Tiene AntonioTN MultiverseComputingCAI Ali Hashemi ali-hashemi MultiverseComputingCAI David Jansen Jansenhbar MultiverseComputingCAI Roman Rausch spinflip MultiverseComputingCAI One of the cheapest ways to make a large language model faster is also one of the bluntest: delete whole transformer blocks.
• Because the model literally gets shorter, block removal (also called depth pruning) buys predictable inference speedups on top of the memory savings, and it stacks cleanly with quantization, low-rank compression, and other techniques.
• The hard part is deciding which blocks to cut.
• Remove the wrong ones and the model collapses; and the effect of removing any one block depends on which others you remove alongside it, so the choices interact.
• That makes it a combinatorial problem, not a ranking problem, and combinatorial problems with interacting binary variables are exactly what the physics of spin systems was built to describe.
• Our latest paper, LLM Compression by Block Removal with Constrained Binary Optimization , takes that correspondence literally.