๊ฐ€์ž… ํ›„ ์ดˆ๋Œ€ ๋งํฌ๋ฅผ ๊ณต์œ ํ•˜๋ฉด ๋™์˜์ƒ ์žฌ์ƒ ๋ฐ ์ดˆ๋Œ€ ๋ณด์ƒ์„ ๋ฐ›์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

vCluster
@vclusterlabs
Create & Manage Tenant Clusters Like a Hyperscaler
๊ฐ€์ž… May 2020
23 ํŒ”๋กœ์ž‰ ์ค‘    3.7K ํŒฌ
If you're at AI Infra Summit, this is the panel you shouldn't miss! The Speed Problem: What It Actually Takes to Stand Up Capacity โฐ Wed, Sept 16, 3:00 PM PT ๐Ÿ“Compute Track at AI Infra Summit in Santa Clara vCluster CEO Lukas Gentele is moderating this one live with Jay Jubran (@ZyphraAI), Kasra Danesh (@sfcompute), and Yujing Qian (@gmi_cloud) Three companies. Three completely different bets on the same problem. Zyphra trained a frontier mixture-of-experts model end to end on AMD silicon, zero NVIDIA in the stack, then turned that build into an AI Cloud. SF Compute built a physically settled compute market. Long-term contracts get supercomputers financed. Resale lets customers recover an average of 25% of what they spend on capacity they don't use. They just moved $245M of Blackwell B300 across two deals. GMI Cloud stayed on the default stack and bet everything on speed: NVIDIA Reference Architecture, a 99.9% uptime SLA, $500M in new capex behind nine-figure enterprise contracts. Silicon. Capital. Software. What's actually standing between "we announced capacity" and "a workload is running on it,". Let's find out. #AIInfraSummit# #AIInfrastructure# #AICloud# #Compute# #Inference#
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