Is Transformer inference fundamentally necessary or just architectural artifact?
prateekj · x · 2026-08-19
We don't know the irreducible amount of computation required for inference versus what is an artifact of current Transformer implementations. There is no established lower bound connecting capability to flops, parameter reads, or energy.
Key Points
- The fundamental operation is computing enough info to pick the next token given input and state.
- Dense Transformers have clear non-fundamental costs: touching the whole model per token, moving weights, growing KV cache, fixed-depth computation.
- Current work per token may be 3x, 20x, or 500x above the theoretical minimum. These are properties of the representation, not laws of intelligence.
- Future inference might look like selectively retrieving and updating a small relevant state.
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