Unverified50% confidenceCautionExact time
Local deployment means a higher installation barrier for ordinary users, and hardware compute directly determines how large a model can run and how fast it can go.
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10/12/2026
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VerifiedWhen running local LLMs, VRAM capacity is often more critical than computational speed, as VRAM capacity directly determines which models can be run.80% similarVerifiedWhen selecting hardware for local large model inference, memory capacity should be prioritized first, followed by architecture and memory bandwidth.75% similarUnverifiedLocalized RAG requires both the embedding model and the generative model to run on-device, placing high demands on memory bandwidth and compute, with the 740M parameter scale being a trade-off choice made in response to these constraints.74% similarUnverifiedThe trade-off of local processing is that processing speed is limited by the CPU/GPU performance of the user's local device, and complex or high-resolution projects may face performance bottlenecks.74% similarUnverified模型规模越大对硬件配置要求越高，应根据机器条件选择合适规模的版本73% similar
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