Microsofts 1 Bit AI model runs on a CPU and matches larger systems
At some point AI's will have to run using alot less compute using alot less energy and this is a nothing step in that direction.
Key takeaways
- Called BitNet b1.58 2B4T, it’s openly available under an MIT license and can run on CPUs, including Apple’s M2.
- BitNet b1.58 2B4T doesn’t sweep the floor with rival 2 billion-parameter models, to be clear, but it seemingly holds its own.
- Perhaps more impressively, BitNet b1.58 2B4T is speedier than other models of its size — in some cases, twice the speed — while using a fraction of the memory.
Cite or link to this article
Griffin, M. (2025) 'Microsofts 1 Bit AI model runs on a CPU and matches larger systems', 311 Institute, 8 June. Available at: https://www.311institute.com/microsofts-1-bit-ai-model-runs-on-a-cpu-and-matches-larger-systems/ (Accessed: 1 October 2026).
Microsoft researchers claim they’ve developed the largest-scale 1-bit AI model, also known as a “bitnet,” to date. Called BitNet b1.58 2B4T, it’s openly available under an MIT license and can run on CPUs, including Apple’s M2.
Bitnets are essentially compressed models designed to run on lightweight hardware. In standard models, weights, the values that define the internal structure of a model, are often quantized so the models perform well on a wide range of machines. Quantizing the weights lowers the number of bits — the smallest units a computer can process — needed to represent those weights, enabling models to run on chips with less memory, faster.
[youtube https://www.youtube.com/watch?v=jdV2icf5Pik?si=vi15RCMce7p3ZHDK]
The Future of AI, by Keynote Matthew Griffin
Bitnets quantize weights into just three values: -1, 0, and 1. In theory, that makes them far more memory- and computing-efficient than most models today.
The Microsoft researchers say that BitNet b1.58 2B4T is the first bitnet with 2 billion parameters, “parameters” being largely synonymous with “weights.” Trained on a dataset of 4 trillion tokens — equivalent to about 33 million books, by one estimate — BitNet b1.58 2B4T outperforms traditional models of similar sizes, the researchers claim.
BitNet b1.58 2B4T doesn’t sweep the floor with rival 2 billion-parameter models, to be clear, but it seemingly holds its own. According to the researchers’ testing, the model surpasses Meta’s Llama 3.2 1B, Google’s Gemma 3 1B, and Alibaba’s Qwen 2.5 1.5B on benchmarks including GSM8K (a collection of grade-school-level math problems) and PIQA (which tests physical common sense reasoning skills).
Perhaps more impressively, BitNet b1.58 2B4T is speedier than other models of its size — in some cases, twice the speed — while using a fraction of the memory.
There is a catch, however.
Achieving that performance requires using Microsoft’s custom framework, bitnet.cpp, which only works with certain hardware at the moment. Absent from the list of supported chips are GPUs, which dominate the AI infrastructure landscape.
That’s all to say that bitnets may hold promise, particularly for resource-constrained devices. But compatibility is — and will likely remain — a big sticking point.
FAQ
Why does this matter?
At some point AI's will have to run using alot less compute using alot less energy and this is a nothing step in that direction.
About the author
Matthew Griffin Founder, 311 Institute
Matthew Griffin is a multi-award winning Futurist and expert in Disruption and Innovation, Geopolitics, Leadership, and Technology, who NASA have described as a "walking encyclopaedia of the future" and a "futurist Polymath."
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Matthew Griffin is a multi-award winning Futurist and expert in Disruption and Innovation, Geopolitics, Leadership, and Technology, who NASA have described as a "walking encyclopaedia of the future" and a "futurist Polymath." 15-time best selling author of the "Codex of the Future" series, Matthew is the Founder and Futurist in Chief of the 311 Institute, a global Futures and Deep Futures advisory firm working with royal households, world leaders, G7, G20, and G77 governments, NGOs, and multi-national mid and mega cap firms to help them explore, shape, and lead the next 50 years of business and society.
An award-winning YouTube creator with over a million followers, with an unrivalled global reach and impact, Matthew is a highly sought-after international keynote speaker, lecturer, and mentor who collaborates with global leaders through the United Nations Alliance of Civilizations (UNAOC) and United Nations General Assembly (UNGA) to shape pivotal initiatives such as the UN’s AI for Humanity program, the United Nations Conference of the Parties (UN COP), and the World Economic Forum in Davos.
As the former Global Head of Cloud, National Security, and Enterprise Sales for companies including Atos, Dell-EMC, and IBM, Matthew has a proven track record of building multi-billion dollar business units and turning failing divisions into market leaders. His ability to identify, analyse, and communicate the implications of hundreds of emerging technologies and trends is unparalleled, and his insights are trusted by many of the world’s most respected organisations, including ABB, Accenture, Adidas, AON, ARM, BCG, Centrica, Citi, Coca-Cola, Dentons, Deloitte, Dow Jones, EY, Google, KPMG, Lego, Legal & General, LinkedIn, Microsoft, PepsiCo, Qualcomm, RWE, Samsung, Siemens AG and Siemens Energy, T-Mobile, UBS, VISA, Walmart, Workday, Worldpay and many others.
Regularly featured in the global media including the AP, BBC, Bloomberg, CNBC, Discovery, Forbes, Khaleej Times, Telegraph, TIME, ViacomCBS, WIRED, and the WSJ, Matthews mission is to help organisations create a fair and sustainable future whose benefits are shared by everyone irrespective of their ability, background, or circumstances.
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Sources and further reading
- Bitnet b1.58 2B 4T huggingface.co
- by one estimate everdome.io
Source: first published by the 311 Institute on 8 June 2025. Cite as: Griffin, M. (2025). Microsofts 1 Bit AI model runs on a CPU and matches larger systems. 311 Institute. https://www.311institute.com/microsofts-1-bit-ai-model-runs-on-a-cpu-and-matches-larger-systems/
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