Rubin CPX forms part of Nvidia’s upcoming Vera Rubin platform, marrying specialized hardware and disaggregated infrastructure to meet burgeoning AI demands. Integrated into the Vera Rubin NVL144 CPX rack, the chip delivers up to 8 exaFLOPS of NVFP4 compute power—7.5 times the performance of the current GB300 NVL72 systems—and includes a staggering 100 TB of high-speed memory with 1.7 PB/s bandwidth. This consolidated architecture enables long-context inference at scale, trimming inference latency and cost while opening doors to generative video and complex coding tasks. The platform’s dense configuration and orchestration over NVIDIA’s Dynamo, InfiniBand and Spectrum-X networking components redefine efficiency across full-stack AI infrastructure. Some projections estimate a $5 billion revenue return from a $100 million deployment, underscoring the economic potential of mass-scale token processing.
Chief among the innovations of Rubin CPX, as paraphrased here, is its nature as “the first CUDA GPU purpose-built for massive-context AI.” It is designed to process sequences measured in millions of tokens, empowering models to handle entire hours of video or full-scale codebases in a single inference pass. The chip combines video decoding, encoding, and inference capabilities on a monolithic die—with 128 GB of GDDR7 memory, NVFP4 precision pushing 30 petaflops of compute, and attention mechanisms operating up to three times faster than current systems.
This launch is supported by strategic tape-out progress. Earlier confirmation from Nvidia’s CFO stated that both Rubin GPUs and Vera CPUs, along with accompanying network accelerators and photonics components, have entered fabrication at TSMC, keeping the Vera Rubin platform on track for 2026 rollout.
Industry players are already signalling adoption plans. AI-powered software developer Cursor anticipates Rubin CPX will enable “lightning-fast code generation and developer insights,” while generative video studio Runway expects to scale cinematic creation “with unmatched speed, realism and control.” Even models with 100-million-token context windows—covering entire codebases, documentation, and interaction histories—stand to gain from faster compute and context retention.
Observers including TechRepublic and TechWire Asia highlight the practical leap Rubin CPX introduces for long-context AI. Processing tasks such as complex coding and video generation on hour-long content becomes feasible, supported by hardware capable of seamlessly managing massive token sequences.
Topics
Technology