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DeeperML
Megan Crouse@techrepublic.com
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Microsoft has unveiled BitNet b1.58 2B4T, a groundbreaking AI model designed for exceptional efficiency. Developed by Microsoft's General Artificial Intelligence group, this model utilizes one-bit neural network weights, representing each weight with only three discrete values (-1, 0, or +1). This approach, called ternary quantization, allows each weight to be stored in just 1.58 bits, drastically reducing memory usage. The result is an AI model that can operate on standard CPUs without the need for specialized, energy-intensive GPUs.
Unlike conventional AI models that rely on 16- or 32-bit floating-point numbers, BitNet's unique architecture allows it to run smoothly on hardware like Apple's M2 chip, requiring only 400MB of memory. To compensate for its low-precision weights, BitNet b1.58 2B4T was trained on a massive dataset of four trillion tokens, the equivalent of approximately 33 million books. This extensive training enables the model to perform on par with, and in some cases even better than, other leading models of similar size, such as Meta's Llama 3.2 1B, Google's Gemma 3 1B, and Alibaba's Qwen 2.5 1.5B.
To facilitate the deployment and adoption of this innovative model, Microsoft has released a custom software framework called bitnet.cpp, optimized to take full advantage of BitNet's ternary weights. This framework is available for both GPU and CPU execution, including a lightweight C++ version. The model has demonstrated strong performance across a variety of tasks including math and common sense reasoning in benchmark tests. Microsoft plans to expand BitNet to support longer texts, additional languages, and multimodal inputs like images, while also working on the Phi series, another family of efficient AI models.
ImgSrc: assets.techrepu
References :
Unlike conventional AI models that rely on 16- or 32-bit floating-point numbers, BitNet's unique architecture allows it to run smoothly on hardware like Apple's M2 chip, requiring only 400MB of memory. To compensate for its low-precision weights, BitNet b1.58 2B4T was trained on a massive dataset of four trillion tokens, the equivalent of approximately 33 million books. This extensive training enables the model to perform on par with, and in some cases even better than, other leading models of similar size, such as Meta's Llama 3.2 1B, Google's Gemma 3 1B, and Alibaba's Qwen 2.5 1.5B.
To facilitate the deployment and adoption of this innovative model, Microsoft has released a custom software framework called bitnet.cpp, optimized to take full advantage of BitNet's ternary weights. This framework is available for both GPU and CPU execution, including a lightweight C++ version. The model has demonstrated strong performance across a variety of tasks including math and common sense reasoning in benchmark tests. Microsoft plans to expand BitNet to support longer texts, additional languages, and multimodal inputs like images, while also working on the Phi series, another family of efficient AI models.
ImgSrc: assets.techrepu
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References :
- the-decoder.com: BitNet: Microsoft shows how to put AI models on a diet The article appeared first on .
- TechSpot: Microsoft's BitNet shows what AI can do with just 400MB and no GPU
- www.techrepublic.com: Microsoft’s model BitNet b1.58 2B4T is available on Hugging Face but doesn’t run on GPU and requires a proprietary framework.
- www.tomshardware.com: Microsoft researchers developed a 1-bit AI model that's efficient enough to run on traditional CPUs without needing specialized chips like NPUs or GPUs.
Classification:
- HashTags: #MicrosoftAI #BitNet #AIefficiency
- Company: Microsoft
- Product: BitNet
- Feature: one-bit neural network weights
- Type: AI
- Severity: Informative