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Good folks from 
Good folks from 
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@Microsoft
Microsoft
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 Research have just released bitnet.cpp, a game-changing inference framework that achieves remarkable performance gains.
 Research have just released bitnet.cpp, a game-changing inference framework that achieves remarkable performance gains.
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Key Technical Highlights:
Key Technical Highlights:
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- Achieves speedups of up to 6.17x on x86 CPUs and 5.07x on ARM CPUs  
- Achieves speedups of up to 6.17x on x86 CPUs and 5.07x on ARM CPUs  
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- Reduces energy consumption by 55.4–82.2%  
- Reduces energy consumption by 55.4–82.2%  
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- Enables running 100B parameter models at human reading speed (5–7 tokens/second) on a single CPU  
- Enables running 100B parameter models at human reading speed (5–7 tokens/second) on a single CPU  
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Features Three Optimized Kernels:
Features Three Optimized Kernels:
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1. I2_S: Uses 2-bit weight representation  
1. I2_S: Uses 2-bit weight representation  
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2. TL1: Implements 4-bit index lookup tables for every two weights  
2. TL1: Implements 4-bit index lookup tables for every two weights  
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3. TL2: Employs 5-bit compression for every three weights  
3. TL2: Employs 5-bit compression for every three weights  
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Performance Metrics:
Performance Metrics:
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- Lossless inference with 100% accuracy compared to full-precision models  
- Lossless inference with 100% accuracy compared to full-precision models  
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- Tested across model sizes from 125M to 100B parameters  
- Tested across model sizes from 125M to 100B parameters  
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- Evaluated on both Apple M2 Ultra and Intel i7-13700H processors  
- Evaluated on both Apple M2 Ultra and Intel i7-13700H processors  
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This breakthrough makes running large language models locally more accessible than ever, opening new possibilities for edge computing and resource-constrained environments.
This breakthrough makes running large language models locally more accessible than ever, opening new possibilities for edge computing and resource-constrained environments.
Good folks from @Microsoft Research have just released bitnet.cpp, a game-changing inference framework that achieves remarkable performance gains.

Key Technical Highlights:
- Achieves speedups of up to 6.17x on x86 CPUs and 5.07x on ARM CPUs  
- Reduces energy consumption by 55.4–82.2%  
- Enables running 100B parameter models at human reading speed (5–7 tokens/second) on a single CPU  

Features Three Optimized Kernels:
1. I2_S: Uses 2-bit weight representation  
2. TL1: Implements 4-bit index lookup tables for every two weights  
3. TL2: Employs 5-bit compression for every three weights  

Performance Metrics:
- Lossless inference with 100% accuracy compared to full-precision models  
- Tested across model sizes from 125M to 100B parameters  
- Evaluated on both Apple M2 Ultra and Intel i7-13700H processors  

This breakthrough makes running large language models locally more accessible than ever, opening new possibilities for edge computing and resource-constrained environments.
🔥
umair894 John6666 MexIvanov AtAndDev
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Michael Conrad
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EsKa
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