of FlashAttention-2 (Dao, 2023). We conduct all experiments on a cluster equipped with NVIDIA H800 GPUs. Each node in the H800 cluster contains 8 GPUs connected using NVLink and NVSwitch within nodes. attain a relatively high Model FLOPs Utilization (MFU). During our practical training on the H800 cluster, for training on each trillion tokens, DeepSeek 67B requires 300.6K GPU hours, while DeepSeek-V2

Momentum Performance, 16-bit FLOP/s +150% / Year Enabled by 1.6x annual growth in chips per cluster and 1.6x annual growth in performance per chip Performance of Leading AI Supercomputers (FLOP/s) delivers advanced AI chip ‘cluster’ to Chinese clients cut off from Nvidia’ (4/29/25) Huawei has started the delivery of its advanced artificial intelligence chip ‘cluster’ to Chinese clients who are

ticket to a human. Orchestration Agents themselves can serve as tools for other agents—see the Manager Pattern in the Orchestration section. Refund agent, Research agent, Writing agent. 9 A practical requirements, our experience with customers highlights two broadly applicable categories: Manager (agents as tools) A central “manager” agent coordinates multiple specialized agents via tool calls, each handling specializations. Multi-agent systems can be modeled as graphs, with agents represented as nodes. In the manager pattern, edges represent tool calls whereas in the decentralized pattern, edges represent handoffs

下和全于由 loss = loss_fn() opt = tf.Adamoptimizer(learning_rate=...) # Choose a 1oss Scale manager which decides how to pick the right loss scale # throughout the training process. 1oss_scale_manger original optimizer in a LossScale0ptimizer . loss_scale_optimizer = LossScaleOptimizer(opt，1oss_scale_manager) # Call minimize() on the loss scale optimizer. train_op = loss_scale_optimizer.minimize(1oss) Loss