Model Compression

Artificial Intelligence 16 Sep 2026 5 min read

Prune Transformer Attention Heads with Measured Impact

A multi-head attention block can contain heads whose removal changes a target metric very little on a chosen evaluation set. That observation makes attention head pruning attractive: identify low-impact heads, remove their contribution, and retain the heads that matter more for the target workload. The difficult part is not setting a head output to zero. It is deciding what that intervention measures and whether the resulting model actually executes less work. A masked head, a structurally removed head, and a faster attention kernel are related ideas, but they are not the same result.

Artificial Intelligence Transformers

Artificial Intelligence 12 Sep 2026 10 min read

Compress Neural Networks with Knowledge Distillation

Compress Neural Networks with Knowledge Distillation A model can meet your quality target in a notebook and still be too expensive to serve. A large network may consume too much memory, add unacceptable latency, or make high request volume costly. Knowledge distillation addresses this problem by using a stronger model, called the teacher, to guide the training of a smaller student model. The key idea is richer than copying the teacher’s final answer. The teacher produces a distribution across possible outputs, and that distribution can reveal useful relationships between alternatives. A student can train against those soft targets while also using the original labels.

Artificial Intelligence Neural Networks

Artificial Intelligence 07 Sep 2026 10 min read

Neural Network Pruning: Sparsity, Structure, and Speed

A neural network can contain parameters that contribute little to its useful predictions. Removing some of them can reduce storage or computation, but there is an important trap: a model with fewer nonzero weights is not automatically a model that runs faster. That distinction matters when developers use pruning to compress a trained network. The pruning rule determines what disappears, the hardware and runtime determine whether the resulting structure can be exploited, and the evaluation procedure determines whether the saved resources are worth any quality loss.

Artificial Intelligence Neural Networks

Artificial Intelligence 07 Sep 2026 11 min read

Compress Neural Network Layers with Low-Rank Factorization

Large neural networks spend much of their memory and computation multiplying activations by weight matrices. Some of those matrices contain more independent structure than the model actually needs for a particular deployment. If so, we can approximate one large matrix with two smaller matrices and reduce the number of stored parameters and multiply-add operations. This technique is called low-rank factorization. The central idea is simple, but using it well requires more than choosing a smaller number. Compression changes the weights, approximation error can accumulate through a network, and fewer arithmetic operations do not guarantee lower wall-clock latency on every device.

Artificial Intelligence Machine Learning