We benchmark normalization and attention backendsfor RF spectrum models. An AttentionModelAdapter providesa unified interface to baseline MHA, FlashMHA, groupedquery attention (GQA), and latent attention, while a swap fromLayerNorm to RMSNorm reduces latency. On streaming FFTpower spectra, the best backend (Latent) achieves 90.6% accuracywith p50 latency 22.0 ms, 480 MB peak KV memory, and 1900samples/s throughput … Continue reading Normalization & Attention Backends for RF: RMSNorm + AttentionModelAdapter comparing FlashMHA, Grouped, Latent, and Baseline MHA