{"text": "今天是2026年8月5日,700E参数塞进4GB显卡。Aon用逐层搬运改写门槛。L,让塞不进显卡的大模型改用逐层搬运。它一次只把需要的一层送进GPU,让700E参数模型可用单张4GB显卡执行。小团队在工作站测试大模型,不必先租多卡主机。安装后,以AutoModel载入模型平台的模型。它会切分快取权重,预取再重叠,磁叠载入与运算。新版支援FP8与西数Mod。官方称KIMI-K3可低于4GB显存运行。今天增加1716颗星,低显存推论重新受到关注。代价是速度受磁叠吞吐影响,少次切分需足够储存空间。想在CUDA或Apple晶片探索超大模型,可先用小模型验证品质与速度,再把本地实验推向更大尺度。以上就是今天的Github开源焦点,喜欢这类工具介绍,记得按赞订阅频道,我们明天见。", "segments": [{"id": 0, "seek": 0, "start": 0.0, "end": 27.5, "text": "今天是2026年8月5日,700E参数塞进4GB显卡。Aon用逐层搬运改写门槛。L,让塞不进显卡的大模型改用逐层搬运。它一次只把需要的一层送进GPU,让700E参数模型可用单张4GB显卡执行。小团队在工作站测试大模型,不必先租多卡主机。安装后,以AutoModel载入模型平台的模型。", "tokens": [50365, 12074, 1541, 2009, 10880, 5157, 23, 6939, 20, 6890, 11, 18197, 36, 2129, 224, 33188, 13331, 252, 36700, 19, 8769, 1431, 122, 32681, 1543, 32, 266, 9254, 2215, 238, 9636, 224, 22220, 105, 3316, 238, 34490, 5676, 247, 8259, 101, 24880, 249, 1543, 43, 11, 33650, 13331, 252, 1960, 36700, 1431, 122, 32681, 1546, 3582, 41908, 39823, 34490, 9254, 2215, 238, 9636, 224, 22220, 105, 3316, 238, 1543, 11284, 27505, 14003, 16075, 35748, 1546, 2257, 9636, 224, 29309, 36700, 38, 8115, 11, 33650, 18197, 36, 2129, 224, 33188, 41908, 39823, 4429, 9254, 47446, 44059, 19, 8769, 1431, 122, 32681, 3416, 100, 8082, 1543, 7322, 3919, 95, 10034, 253, 3581, 41315, 34155, 11038, 233, 5233, 243, 3582, 41908, 39823, 11, 1960, 28531, 10108, 8204, 253, 6392, 32681, 13557, 37960, 1543, 16206, 8083, 227, 13547, 11, 3588, 32, 8262, 44, 41147, 17819, 121, 14028, 41908, 39823, 16716, 15433, 1546, 41908, 39823, 1543, 51740], "temperature": 0, "avg_logprob": -0.1041700437471464, "compression_ratio": 1.250909090909091, "no_speech_prob": 7.316204066187826e-12}, {"id": 1, "seek": 2750, "start": 27.5, "end": 37.82, "text": "它会切分快取权重,预取再重叠,磁叠载入与运算。新版支援FP8与西数Mod。官方称KIMI-K3可低于4GB显存运行。", "tokens": [50365, 11284, 12949, 23632, 6627, 10251, 29436, 4422, 225, 12624, 11, 12501, 226, 29436, 8623, 12624, 2129, 254, 11, 163, 96, 223, 2129, 254, 17819, 121, 14028, 940, 236, 3316, 238, 19497, 1543, 12560, 42096, 24400, 11673, 112, 45882, 23, 940, 236, 16220, 33188, 44, 378, 1543, 31929, 9249, 8204, 108, 42, 6324, 40, 12, 42, 18, 4429, 41377, 37732, 19, 8769, 1431, 122, 39781, 3316, 238, 8082, 1543, 50881], "temperature": 0, "avg_logprob": -0.09269036528884723, "compression_ratio": 1.1914893617021276, "no_speech_prob": 1.029920593254019e-11}, {"id": 2, "seek": 2750, "start": 38.6, "end": 47.56, "text": "今天增加1716颗星,低显存推论重新受到关注。代价是速度受磁叠吞吐影响,少次切分需足够储存空间。", "tokens": [50920, 12074, 24228, 252, 9990, 7773, 6866, 12501, 245, 20682, 11, 41377, 1431, 122, 39781, 33597, 7422, 118, 12624, 12560, 23151, 4511, 28053, 26432, 1543, 19105, 1550, 115, 1541, 31217, 13127, 23151, 163, 96, 223, 2129, 254, 2392, 252, 2392, 238, 16820, 3858, 235, 11, 15686, 9487, 23632, 6627, 32535, 37236, 1787, 253, 11518, 101, 39781, 23322, 31685, 1543, 51368], "temperature": 0, "avg_logprob": -0.09269036528884723, "compression_ratio": 1.1914893617021276, "no_speech_prob": 1.029920593254019e-11}, {"id": 3, "seek": 2750, "start": 48.24, "end": 55.36, "text": "想在CUDA或Apple晶片探索超大模型,可先用小模型验证品质与速度,再把本地实验推向更大尺度。", "tokens": [51402, 7093, 3581, 25864, 7509, 19780, 9132, 306, 5094, 114, 16668, 6900, 95, 7732, 95, 19869, 3582, 41908, 39823, 11, 4429, 10108, 9254, 7322, 41908, 39823, 49657, 234, 5233, 223, 30246, 18464, 101, 940, 236, 31217, 13127, 11, 8623, 16075, 8802, 10928, 24726, 49657, 234, 33597, 24282, 19002, 3582, 1530, 118, 13127, 1543, 51758], "temperature": 0, "avg_logprob": -0.09269036528884723, "compression_ratio": 1.1914893617021276, "no_speech_prob": 1.029920593254019e-11}, {"id": 4, "seek": 5536, "start": 55.36, "end": 62.54, "text": "以上就是今天的Github开源焦点,喜欢这类工具介绍,记得按赞订阅频道,我们明天见。", "tokens": [50365, 29497, 5620, 34947, 38, 355, 836, 18937, 47402, 4971, 99, 12579, 11, 41606, 5562, 22113, 119, 23323, 39806, 30312, 10115, 235, 11, 34756, 5916, 26613, 5266, 252, 7422, 95, 10034, 227, 39752, 6025, 11, 15003, 11100, 6135, 23813, 1543, 50724], "temperature": 0, "avg_logprob": -0.07217325483049665, "compression_ratio": 0.907563025210084, "no_speech_prob": 1.645524341786775e-11}], "language": "zh"}