NVIDIA’s Hymba represents a significant advancement in small language model architecture, combining transformer attention mechanisms with state space models (SSMs) to enhance efficiency and performance in natural language processing tasks. With 1.5 billion parameters, Hymba outperforms other sub-2B models in accuracy, throughput, and cache efficiency. Key innovations include parallel processing of attention and SSM heads, meta-tokens for learned cache initialization, and cross-layer KV cache sharing. Hymba demonstrates superior performance across various benchmarks, making it suitable for a wide range of applications from enterprise AI to edge computing.