Skip to content On the map On the map: Hybrid architectures uses 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 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 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 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 The 1,802-second hang: serving a hybrid linear-attention MoE under agent load — In July I ran coding-agent evaluations on SWE-bench Verified against Qwen3.6-35B-A3B, served by vLLM 0.21 ( ) on one DGX Spark, four requests at a time. The… The 1,802-second hang Show on the main map →