The first release of bitnet.cpp is to support inference on CPUs. bitnet.cpp achieves speedups of 1.37x to 5.07x on ARM CPUs, with larger models experiencing greater performance gains. Additionally, it reduces energy consumption by 55.4% to 70.0%, further boosting overall efficiency. On x86 CPUs, speedups range from 2.37x to 6.17x with energy reductions between 71.9% to 82.2%. Furthermore, bitnet.cpp can run a 100B BitNet b1.58 model on a single CPU, achieving speeds comparable to human reading (5-7 tokens per second), significantly enhancing the potential for running LLMs on local devices. Please refer to the technical report for more details.
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写到这里，我试着让 Gemini 生成了一段有关某 Kali 技术动作的描述，并且真的试图照着做一遍。结果刚到第二个动作步骤，我的两条手臂就缠在一起、拧成了麻花，下一个动作就没办法做下去。我把这个问题交给 Gemini，它提出了两个可能的原因诊断，其中一个指出我左手可能过于紧张、以至于左手的棍子「被紧紧夹在右侧腋下拔不出来」。但，同样根据 Gemini 的描述，我在第一个动作结束时，右手应该收在左侧的腋下。……
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Testing with a view distance of 16 shows the total memory being used at 47 megabytes and chunks taking 34 megabytes of memory.