NVIDIA’s Inference Context Memory Storage Platform, announced at CES 2026, marks a major shift in how AI inference is architected. Instead of forcing massive KV caches into limited GPU HBM, NVIDIA formalizes a hierarchical memory model that spans GPU HBM, CPU memory, cluster-level shared context, and persistent NVMe SSD storage.
This enables longer-context and multi-agent inference by keeping the most active KV data in HBM while offloading less frequently used context to NVMe—expanding capacity... moreNVIDIA’s Inference Context Memory Storage Platform, announced at CES 2026, marks a major shift in how AI inference is architected. Instead of forcing massive KV caches into limited GPU HBM, NVIDIA formalizes a hierarchical memory model that spans GPU HBM, CPU memory, cluster-level shared context, and persistent NVMe SSD storage.
This enables longer-context and multi-agent inference by keeping the most active KV data in HBM while offloading less frequently used context to NVMe—expanding capacity without sacrificing performance. This shift also has implications for AI infrastructure procurement and the secondary GPU/DRAM market, as demand moves toward higher bandwidth memory and context-centric architectures.
NVIDIA used CES 2026 to signal a strategic shift in AI infrastructure. Instead of launching a new consumer GPU, the company unveiled Vera Rubin, a rack-scale AI supercomputing platform designed as a fully integrated system.
Rubin combines GPUs, CPUs, interconnects, networking, storage, and security into a single co-designed architecture. NVIDIA claims up to 5× inference performance, 3.5× training performa... morehttps://www.buysellram.com/blog/nvidias-vera-rubin-the-beginning-of-ai-as-infrastructure/
NVIDIA used CES 2026 to signal a strategic shift in AI infrastructure. Instead of launching a new consumer GPU, the company unveiled Vera Rubin, a rack-scale AI supercomputing platform designed as a fully integrated system.
Rubin combines GPUs, CPUs, interconnects, networking, storage, and security into a single co-designed architecture. NVIDIA claims up to 5× inference performance, 3.5× training performance, and 10× lower inference cost per token compared to Blackwell, achieved through system-level optimization rather than standalone chip speed.
With production already underway and major cloud providers set to deploy Rubin in late 2026, NVIDIA is moving AI from GPU clusters toward industrialized, factory-style infrastructure—reshaping both primary deployments and secondary hardware markets.
