Unverified50% confidenceFactExact time
Because it runs on GPUs, a single simulation can be split into thousands of parallel environments to achieve large-scale parallelism.
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50%
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Long-term
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9/17/2026
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UnverifiedRunning RL environments on a GPU allows thousands of environment instances to be parallelized simultaneously, avoiding the overhead of frequent data transfers between CPU and GPU.77% similarVerifiedLarge model inference is essentially a large number of matrix multiplication operations, and GPUs with thousands of parallel compute cores are well-suited for such tasks.73% similarUnverifiedEach token generated by a large model requires a large number of matrix multiplication operations on the GPU, which is the core computational step of the attention mechanism in the Transformer architecture.71% similarUnverifiedThe full-stack GPU paradigm keeps environment state, dynamics simulation, policy network forward passes, and gradient computation entirely within video memory, eliminating the data transfer bottleneck between CPU and GPU.71% similarUnverified无进位乘法获得硬件支持后，批量数据加密、大规模GCM认证、区块链哈希运算等场景可借助GPU数千核心并发处理实现远超CPU的吞吐量70% similar
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