How NVIDIA Reduces the Cost of Long Context Reasoning

Nvidia reduces the computational and memory costs of long-context reasoning in AI agents by developing a technique that interleaves Mamba recurrent models with vanilla transformer self-attention layers. According to the available material, this approach, implemented in Nemotron 3.5 Super, achieves high performance on software-engineering benchmarks while activating significantly fewer parameters per token. Open article →