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实际上，机器人创业热的另一个底层原因，是“大模型+人形机器人”的具身智能技术路线上，对比大模型研发需要海量算力、长期训练、严苛评测的高门槛，人形机器人赛道的“组装式创业”捷径太过明显，因此投机门槛被刻意拉低，从而导致乱象丛生。
By default, freeing memory in CUDA is expensive because it does a GPU sync. Because of this, PyTorch avoids freeing and mallocing memory through CUDA, and tries to manage it itself. When blocks are freed, the allocator just keeps them in their own cache. The allocator can then use the free blocks in the cache when something else is allocated. But if these blocks are fragmented and there isn’t a large enough cache block and all GPU memory is already allocated, PyTorch has to free all the allocator cached blocks then allocate from CUDA, which is a slow process. This is what our program is getting blocked by. This situation might look familiar if you’ve taken an operating systems class.。WhatsApp Web 網頁版登入对此有专业解读