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Huge congratulations to the @SpaceX team on a historic IPO debut. Fueling the next frontier of space and AI. 🌌 NVIDIA's partnership with SpaceX spans nearly a decade, from hand-delivering the world's first #NVIDIADGX-1# supercomputer in 2016 to the custom DGX Spark handoff at Starbase. Together, we've been pushing the boundaries of accelerated computing to help power the future of space exploration.

What a moment for @NPS_Monterey and the public sector. At Converge @ NPS, our CEO Jensen Huang joined federal leaders and ecosystem partners to commission the #NVIDIADGX# GB300 system, providing 1,500 students and 600 faculty with on-premises access to large-scale AI computing for research, model training, simulations, and real-world application development. The visit marked a shared commitment to advancing AI education, research, and mission-focused innovation.

16 parallel runs of Gemma 4 26B A4B on a single NVIDIA DGX Spark! Pushing 18 tok/s per instance and a 300 tok/s aggregate. It can even hit 32 parallel runs. This level of concurrency highlights how efficient the architecture is.

THIS DEVELOPER CONNECTED 8 NVIDIA DGX SPARKS INTO ONE CLUSTER - AND RAN AN 800GB MODEL THAT MADE HIM 10X MORE PRODUCTIVE 21:47 he says it straight - "this is a terabyte of VRAM - we ran Quen 3.5, 800GB on disk, a model that doesn't even fit on a single Mac Studio - 24 tokens per second - I'd say that's a win" 8 Sparks connected through a $1,300 switch via RDMA over Ethernet - each node adding 128GB of memory into one unified pool of 1TB started with one Spark at 3 tokens per second - every added node doubled the speed - and eight together deliver 24 tokens on a model that physically cannot run anywhere else Kimi K2 at 600GB loaded in 15 minutes, 115GB per node, 13 tokens per second - a model that simply cannot run on anything smaller Claude helped configure the entire cluster - SSH mesh across all 8 machines, network config, jumbo frames, QSFP port speeds - all from one terminal most people rent cloud compute for models this size at $2,000+/month - he built the cluster once and now every token costs 20x less

Ojo a la jugada de Nvidia con su nuevo RTX Spark. Quieren replicar el éxito de Apple Silicon pero llevado al extremo: fusión de CPU y GPU bajo arquitectura ARM para reventar el rendimiento en edición 3D pesada y diseño con IA local en Windows. Una declaración de guerra total a Intel, AMD y los de Cupertino. ¿De verdad necesitamos tanta potencia bruta orientada a agentes de IA en el día a día? #nvidiatdg#