Unverified50% confidenceFactExact time
Local deployment means inference computation happens on the user's own hardware and data does not leave the local network, but running large language models typically requires a dedicated GPU or sufficient memory.
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10/6/2026
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UnverifiedDense models have simpler inference logic and better hardware compatibility in local deployment scenarios, whereas MoE models, despite activating fewer parameters during inference, still require all weights to be loaded into memory, placing even higher demands on hardware.74% similarUnverified大模型推理是典型的内存带宽密集型任务，GPU计算核心往往处于等待数据的状态72% similarUnverifiedOne of the core bottlenecks for running large language models locally is memory capacity, as model parameters need to be loaded into RAM or VRAM.71% similarUnverifiedLocal model inference means that model weights are stored and run on the device itself, data does not leave the user's hands, and latency does not depend on network quality.71% similarUnverifiedThe core trade-off in local large model selection is: memory determines the maximum model size that can be loaded, while the GPU (or its absence) determines the smoothness of the inference experience71% similar
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