Skip to content On the map On the map: State-space models uses related to State-space models — Sequence models, such as Mamba, that carry a fixed-size recurrent state through a sequence instead of attending to every earlier token, so that their memory per sequence does not grow with context length. State-space models Large language models — Neural network models, typically with billions of parameters, trained on very large text corpora to predict the next token. Large language models Hybrid architectures — Language model designs that interleave attention layers with layers of another kind, typically state-space or linear-attention layers, to lower the cost of long contexts. Hybrid architectures KV cache — The attention keys and values of the tokens a transformer has already processed, kept in memory so that they are not recomputed for every new token it generates. KV cache What 128 GB of unified memory buys you: serving LLMs on a DGX Spark — On a GB10 the GPU memory fraction is a share of the whole machine: size the KV cache from the workload, and expect bandwidth and prefill, not capacity, to set the limits. What 128 GB of unified memory buys you Show on the main map →