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Semiconductors · Latest

Supply chains and chip production

5 selected reportsIn the last 30 days 4 reportsIndexed 16 reports

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Selected: Semiconductors

Oct 29ThuItems 1–5
  1. Decrypt

    SK Hynix Reports Record Q3 Revenue and Profit as AI Memory Demand Outpaces Supply

    SK Hynix announced third‑quarter revenue of 104.6 trillion won (about $78 billion) and operating profit of 83.3 trillion won (about $65.5 billion), a 328 % and 632 % increase YoY respectively, giving the company an 80 % operating margin. It is the first time the South Korean memory‑chip maker’s quarterly sales exceeded 100 trillion won. The company said that demand for DRAM and high‑bandwidth memory (HBM) will continue to outstrip supply through 2030, and that its DRAM and HBM volumes through 2029 are effectively sold out. Long‑term agreements with 14 key customers cover more than half of SK Hynix’s 2027 and 2028 production capacity, locking in future demand. CFO Kim Woo‑hyun noted that the drop in net income from the second quarter was largely due to non‑operating expenses, including an 11.6 trillion won currency loss and a 22.9 trillion won valuation loss on investments.

    Read source decrypt.co
Oct 21Wed
  1. NVIDIA BlogFirst-party

    NVIDIA Cloud Partners Expand European AI Infrastructure Across 20 Countries

    NVIDIA Cloud Partners across Europe, the Nordics, Africa and Central Asia are expanding regional compute capacity for model training and inference. Nscale demonstrated 37% more GPUs and nearly 50% higher inference throughput using NVIDIA DSX MaxLPS within the same power budget. Crusoe is developing liquid-cooled AI factories for the NVIDIA Vera Rubin platform to serve EMEA developers.

    Read source blogs.nvidia.com
  2. Hugging Face BlogFirst-party

    Hugging Face Launches TorchTPU, Making TPUs a Native PyTorch Backend

    Hugging Face has released TorchTPU, a native PyTorch backend that lets users run Transformers, Diffusers, and Kernels directly on Google’s Tensor Processing Units (TPUs). By specifying device_map="tpu", models from the Hub load onto the TPU without extra conversion steps, and the library includes Pallas kernels, distributed training support, and torch.compile integration for speed. This removes the need for the older torch_xla stack and Optimum TPU for TPU inference and training.

    Read source huggingface.co
Oct 15Thu
Sep 28Mon