Unverified50% confidenceFactExact time
一个15亿参数的模型使用Adam优化器时每个GPU至少需要约24GB的状态信息
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50%
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Long-term
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8/26/2026
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UnverifiedThe GPU resources required for a single inference of a hundred-billion-parameter flagship model may be 10 to 20 times higher than those of a ten-billion-parameter model.78% similarUnverifiedModels with larger parameter counts and more reasoning steps may consume 10–50 times the GPU compute of lightweight models.71% similarUnverifiedA model with 100 billion parameters requires approximately 80 GB of VRAM to process a single inference request, while a model with tens of billions of parameters requires only about 20 GB.70% similarUnverifiedAlibaba's new-generation Qianwen model has 2.4 trillion parameters, making it the first multimodal large model in the series to exceed one trillion parameters; its code generation accuracy is reportedly 94.7%, an improvement of approximately 11 percentage points over the previous generation.70% similarUnverified一个2万亿参数的模型即便使用FP8量化，其权重也高达2TB，每生成一个token就需完整扫描这2TB数据68% similar
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