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@gabibetts02: #babyboy 🩷💛

Gabi Betts
Gabi Betts
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Region: AU
Saturday 15 November 2025 02:03:43 GMT
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elise.aroney
elise.aroney :
Insane 🔥
2025-11-15 14:51:05
1
ollliivviiaaconricckkk
Oliviaconthicc :
Gorg girl
2025-11-15 03:02:15
1
lanibetts_
lanibetts :
Bday baddie 🔥🔥🔥
2025-11-15 02:22:34
1
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#xuhuongtiktok sài gòn mưa tui liền xuống đường 🤣 #variobacporsche #variodokieng❤️ #click150iประเทศไทย #variomodifikasi
#xuhuongtiktok sài gòn mưa tui liền xuống đường 🤣 #variobacporsche #variodokieng❤️ #click150iประเทศไทย #variomodifikasi
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You've probably heard about RDMA, but what makes it so special for AI workloads? Traditional networks are verrrryyyyyyy CPU-heavy. When data moves between two nodes in the network, it has to go through the OS, through the TCP/IP stack, get copied between buffers… This can be slow, and it burns CPU cycles. For normal application traffic - it’s fine. But for AI workloads? You’re dealing with massive data sets, GPU clusters, and super tight training loops - latency adds up fast. That’s where RDMA comes in. RDMA is Remote Direct Memory Access. It lets two servers move data directly from memory to memory, without touching the CPU, kernel, or cache. For AI, that’s a big deal because it means GPUs can talk to each other at full speed, meaning your your hardware gets fully utilized. RDMA originally used a protocol called InfiniBand, which meant new switches, new NICs, a whole new fabric. Now you can use RoCEv2 (RDMA over Converged Ethernet) so you can run RDMA on Ethernet networks. P.S. If you're designing networks for AI - what technology are you using the most? #informationtechnology #workingintech #technology #networkengineer #rdma
You've probably heard about RDMA, but what makes it so special for AI workloads? Traditional networks are verrrryyyyyyy CPU-heavy. When data moves between two nodes in the network, it has to go through the OS, through the TCP/IP stack, get copied between buffers… This can be slow, and it burns CPU cycles. For normal application traffic - it’s fine. But for AI workloads? You’re dealing with massive data sets, GPU clusters, and super tight training loops - latency adds up fast. That’s where RDMA comes in. RDMA is Remote Direct Memory Access. It lets two servers move data directly from memory to memory, without touching the CPU, kernel, or cache. For AI, that’s a big deal because it means GPUs can talk to each other at full speed, meaning your your hardware gets fully utilized. RDMA originally used a protocol called InfiniBand, which meant new switches, new NICs, a whole new fabric. Now you can use RoCEv2 (RDMA over Converged Ethernet) so you can run RDMA on Ethernet networks. P.S. If you're designing networks for AI - what technology are you using the most? #informationtechnology #workingintech #technology #networkengineer #rdma
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