In Depth
Sparse models are neural networks where only a fraction of parameters are active for each input, as opposed to dense models where every parameter participates in every computation. Sparsity can be structural (entire neurons or layers are inactive) or unstructured (individual weights are zero). The Mixture of Experts architecture is the most prominent example, activating only a subset of expert networks for each input.
Sparsity offers a compelling trade-off: a sparse model can have the total parameter count (and knowledge capacity) of a very large model while requiring only the computation of a much smaller one. For example, Mixtral 8x7B has 47B total parameters but activates only about 13B per token, achieving performance comparable to much larger dense models at a fraction of the inference cost.
Research into sparsity spans hardware support (sparse tensor cores in modern GPUs), training methods (gradually introducing sparsity during training), and architectural innovations (dynamic routing, top-k selection). As models continue to grow, sparsity is increasingly seen as essential for making large-scale AI economically viable, enabling bigger models without proportionally bigger compute requirements.