Cerebras WSE 3 chip with 4 trillion transistors deliver 2x performance compared WSE 2
Cerebras Systems break its own record of designing a chip with larger transistor count compared to its previous Cerebras WSE-2. The new Wafer Scale Engine 3 (WSE-3), delivers double the performance without compromising on power consumption or cost. Engineered specifically for training the most extensive AI models, the WSE-3, built on a 5nm process, boasts 4 trillion transistors and 900,000 AI cores, enabling the Cerebras CS-3 AI supercomputer to achieve an impressive 125 petaflops of peak AI performance.
PIC: WSE 3 Key Features of WSE 3: 4 trillion transistors 900,000 AI cores 125 petaflops of peak AI performance 44GB on-chip SRAM 5nm TSMC process External memory: 1.5TB, 12TB, or 1.2PB Trains AI models up to 24 trillion parameters Cluster size of up to 2048 CS-3 systems Let's compare this with WSE 2: WSE-2 packs 2.6 trillion transistors and 40 gigabytes of on-chip memory with an active area of 46,225 mm2 fabricated on 300mm silicon wafers using TSMC's 7nm complementary metal-oxide semiconductor (CMOS) technology. With a vast memory capacity of up to 1.2 petabytes, the CS-3 empowers the training of next-generation AI models, surpassing previous benchmarks such as GPT-4 and Gemini by tenfold. Its capability to handle models with up to 24 trilli...
PIC: WSE 3 Key Features of WSE 3: 4 trillion transistors 900,000 AI cores 125 petaflops of peak AI performance 44GB on-chip SRAM 5nm TSMC process External memory: 1.5TB, 12TB, or 1.2PB Trains AI models up to 24 trillion parameters Cluster size of up to 2048 CS-3 systems Let's compare this with WSE 2: WSE-2 packs 2.6 trillion transistors and 40 gigabytes of on-chip memory with an active area of 46,225 mm2 fabricated on 300mm silicon wafers using TSMC's 7nm complementary metal-oxide semiconductor (CMOS) technology. With a vast memory capacity of up to 1.2 petabytes, the CS-3 empowers the training of next-generation AI models, surpassing previous benchmarks such as GPT-4 and Gemini by tenfold. Its capability to handle models with up to 24 trilli...
You've read this far — sign in to keep reading