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And then there is Cerebras, where scale‑up is essentially “inside one wafer” (one CS system), and scale‑out is multiple wafers connected via SwarmX + MemoryX over Ethernet. For scale-out, Cerebras connects multiple CS systems using the SwarmX interconnect plus MemoryX servers in a broadcast‑reduce topology. SwarmX does broadcast of weights to many wafers and reduction of gradients back into MemoryX, so that many CS‑3s train one large model in data‑parallel fashion. CS‑3 supports scale‑out clusters of up to 2,048 CS‑3 systems, with low‑latency RDMA‑over‑Ethernet links carrying only activations/gradients between wafers while keeping the bulk of traffic on‑wafer.
And then there is Cerebras, where scale‑up is essentially “inside one wafer” (one CS system), and scale‑out is multiple wafers connected via SwarmX + MemoryX over Ethernet. For scale-out, Cerebras connects multiple CS systems using the SwarmX interconnect plus MemoryX servers in a broadcast‑reduce topology. SwarmX does broadcast of weights to many wafers and reduction of gradients back into MemoryX, so that many CS‑3s train one large model in data‑parallel fashion. CS‑3 supports scale‑out clusters of up to 2,048 CS‑3 systems, with low‑latency RDMA‑over‑Ethernet links carrying only activations/gradients between wafers while keeping the bulk of traffic on‑wafer.
Cerebras uses dedicated servers, called MemoryX servers, which are SwarmX fabric-connected to the WSE-3 nodes. The MemoryX configuration can include up to 1.2PB of shared memory storage, consisting of DDR5 and Flash tiers. There is 44GB of SRAM on each WSE-3, and the SRAM has far lower latency and fabric latency than any HBM.
Cerebras uses dedicated servers, called MemoryX servers, which are SwarmX fabric-connected to the WSE-3 nodes. The MemoryX configuration can include up to 1.2PB of shared memory storage, consisting of DDR5 and Flash tiers. There is 44GB of SRAM on each WSE-3, and the SRAM has far lower latency and fabric latency than any HBM.
do they have system that actually goes to the PB connection for training? SRAM along, while seems to be big, is far from enough. Perhaps their position is like Groq's inference in AI world
do they have system that actually goes to the PB connection for training? SRAM along, while seems to be big, is far from enough. Perhaps their position is like Groq's inference in AI world
I'm not a big fan of TP Morgan for technical understanding, but Cerebras people provided the information and explanations in the article. However, the article is out of date, and Cerebras also does inference now. Claimed to be the world's fastest.
It is a pity that we can’t make silicon wafers any larger than 300 millimeters in diameter. If there were 450 millimeter diameter silicon wafers, as we
I'm not a big fan of TP Morgan for technical understanding, but Cerebras people provided the information and explanations in the article. However, the article is out of date, and Cerebras also does inference now. Claimed to be the world's fastest.
It is a pity that we can’t make silicon wafers any larger than 300 millimeters in diameter. If there were 450 millimeter diameter silicon wafers, as we