Transformer components can be modeled as associative memory stores that retrieve value vectors from similar key vectors, with recall quality limited by interference from other stored memories. For random unit-length keys, a simple outer-product linear store has capacity near the key dimension: 64-dimensional keys can hold about 64 memories before interference matches the desired signal. An optimal linear regression-based store whitens key correlations, recalls up to 64 linearly independent memories exactly, and reaches capacity near 128 memories. Nonlinear MLP activations suppress low-similarity keys and increase estimated capacity. A ReLU MLP reaches about 128 memories with 64-dimensional keys, while squared ReLU and similarly configured SwiGLU MLPs reach roughly 2,700 to 2,900; cubed ReLU reaches about 35,000. Feedforward layers in trained transformers have been found to act like key-value memories, with learned keys responding to recognizable input patterns. Softmax self-attention applies exponential weighting to similarities and uses context tokens rather than trained parameters as its keys and values. Under the stated random-vector model, 64-dimensional self-attention with sharpness β = 8 has estimated capacity of about 1.2 million memories. Increasing attention-logit scaling with context length, including logarithmic schemes used by Qwen and YaRN, can maintain low interference beyond 2 million distinct key-value pairs in the described experiments. These estimates measure random-vector recall in a single layer rather than total information learned from training data.