与「NVIDIADGX-1」相关的搜索结果

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.

看了一下 NVIDIA DGX Spark 的售价，双机套装 8 万元，大脑瞬间开始运转，拉 8 个好兄弟众筹，一人只要出 1 个 W，算盘打得噼啪响。 本地直接怼上 GLM-5.3-Flash、DeepSeek-v4-Flash 或 Qwen3.8-Flash-Next。假设这台机器能当传家宝用上 10 年，一年 1000 块，一个月才 83 块钱，比两杯星巴克还便宜。未来还能持续跟进部署各家最新的 Flash 系列大模型，这么算下来还是挺香的呀。 或者考虑即将推出的 Mac Studio 256GB M5 Ultra 才 76999 元，8 人小团队算下来更低，这不就 Token 自由了吗。 当然，前提是这 8 个赛博算力难民里绝对不能有 Agent 狂魔。大家最好都跟我一样，离不开 AI，但需要每天查查资料、润色文档、写写小脚本的轻度用户，否则一人开一个高频循环工作流，机器分分钟就得被挤爆。

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#