DeepSeek has open-sourced its internal inference framework Deepinfer on GitHub, with the goal of reducing the compute and energy costs of deploying models on your own. During the inference stage, the framework distributes load more evenly across available processor cores, uses speculative decoding to speed up responses, and improves the structure of results before output; it currently supports only DeepSeek-V4.1-Flash, with more of its own models to be opened up later. On the hardware side, it requires Nvidia GPUs with the SM100 architecture or Huawei Ascend 950 NPUs, so it is not aimed at individual users.
DeepSeek has open-sourced its internal inference framework Deepinfer on GitHub, with the goal of reducing the compute and energy costs of deploying models on your own. During the inference stage, the framework distributes load more evenly across available processor cores, uses speculative decoding to speed up responses, and improves the structure of results before output; it currently supports only DeepSeek-V4.1-Flash, with more of its own models to be opened up later. On the hardware side, it r
At the Guoxin Exhibition, Dongfang Suanxin announced that its 128-card DF1000 training cluster is running stably and that it has achieved large-scale training cluster delivery, with plans to achieve thousand-card-level cluster delivery by the end of the year. The cluster trained continuously for 72 hours without interruption and has carried training tasks for models such as Llama2-70B and Step 130B; the hundred-billion-parameter-level Step 130B completed training adaptation in only 7 days. The DF1000 adopts 3D-stacked near-memory computing, with per-chip memory access bandwidth of 6.4TB/s, and has completed a closed loop across the domestic industrial chain from chip design and 14nm wafer manufacturing to 3D hybrid bonding packaging.
At the Guoxin Exhibition, Dongfang Suanxin announced that its 128-card DF1000 training cluster is running stably and that it has achieved large-scale training cluster delivery, with plans to achieve thousand-card-level cluster delivery by the end of the year. The cluster trained continuously for 72 hours without interruption and has carried training tasks for models such as Llama2-70B and Step 130B; the hundred-billion-parameter-level Step 130B completed training adaptation in only 7 days. The D
A Mac user was using Claude to develop a temperature monitoring tool when Claude discovered the device was running an XMRig mining program, with the process consuming about 1175% CPU (roughly 12 cores) and temperatures reaching 112 degrees Celsius. Claude also identified that the program achieved persistence through a disguised LaunchAgent startup item, and after obtaining the user's authorization, helped terminate the process and move suspicious files into a quarantine directory; only one file required manual deletion due to permission restrictions.
A Mac user was using Claude to develop a temperature monitoring tool when Claude discovered the device was running an XMRig mining program, with the process consuming about 1175% CPU (roughly 12 cores) and temperatures reaching 112 degrees Celsius. Claude also identified that the program achieved persistence through a disguised LaunchAgent startup item, and after obtaining the user's authorization, helped terminate the process and move suspicious files into a quarantine directory; only one file