NVIDIA Explains How Full-Stack Co-Design Eases HBM Pressure
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On November 6, 2026, NVIDIA published a post on its official blog explaining how it uses full-stack co-design to address the HBM memory pressure brought by agentic inference. The article notes that simply increasing HBM capacity cannot meet AI demand, and that NVIDIA's approach is to co-design the entire stack—from GPU, CPU, networking, and storage to software—to improve the utilization of every byte of memory. Using the generational evolution from Blackwell Ultra to the Vera Rubin platform as an example: per-GPU HBM capacity increased only about 3% across this generation, yet on SemiAnalysis's AgentX benchmark, agent throughput per dollar improved by up to 30x, illustrating that memory efficiency optimization, rather than simply stacking capacity, is the key path to relieving HBM pressure.
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- NVIDIA BlogMore Performance From Every Byte: How Full-Stack Codesign Solves AI’s Memory Challenge
NVIDIA uses full-stack codesign across GPU, CPU, networking, storage, and software to improve utilization of every byte of memory, addressing HBM pressure from agentic inference. From Blackwell Ultra to the Vera Rubin platform, per-GPU HBM capacity rises only about 3%, yet agent throughput per dollar on the SemiAnalysis AgentX benchmark improves by up to 30x.
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