Unverified50% confidenceFactExact time
A 360M-parameter model has low memory usage after quantization (can run within a few GB), fast generation speed, and is suitable for edge devices and real-time interaction.
1
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50%
Confidence
Long-term
Relevance
10/22/2026
First Seen
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Related Claims
UnverifiedModels with 2B parameters can run on a single consumer-grade GPU or even a high-performance CPU during inference, with memory usage typically not exceeding 2GB after INT4 quantization.69% similarUnverified6GB显存可运行基础模型如SD 1.5，生成512×512常规尺寸图像，单张生成时间在数秒到十几秒68% similarUnverifiedThe model used to run intelligent NPC dialogue on 4GB of VRAM is a quantized and compressed small-parameter model; the weights of a 3-billion-parameter model quantized to 4-bit can be compressed to under 2GB.68% similarUnverifiedA model with 2B parameters typically requires only about 1–2 GB of memory after INT4 quantization, enabling it to run on mainstream smartphones, embedded development boards, and similar devices.67% similarUnverifiedEmbedding models (such as BGE and text-embedding-3-small) are much smaller than LLMs and can typically run on CPUs or low-spec GPUs, with inference latency in the millisecond range.67% similar
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