Microsoft BitNet: Shocking 100B Param Model on a Single CPU

🔥 Run Large Language Models Locally on CPU with BitNet!
Discover how to run powerful language models like ChatGPT on your local CPU with incredible performance improvements!
✨ Key Highlights:

5x speed boost on ARM CPUs
6x speed boost on x86 CPUs
80% reduction in energy consumption
GitHub repository with 9,000+ stars
Single CPU operation
Compatible with Windows, Mac, and Linux

⚡ Energy Savings:

Apple M2 Ultra: 55% energy saved vs LLama.cpp
70B parameter model: 70% energy saving
Intel CPU: 71% energy saving
Higher inference tokens per second compared to LLama.cpp

🛠️ Requirements:

Python
CMake
CLang
Windows: Visual Studio 2022
Linux/Mac: Standard build tools

📝 Step-by-Step Guide:

Install required packages
Create virtual environment
Clone repository (with recursive flag)
Install requirements and CMAKE
Download model from Hugging Face
Run inference



💡 Model Details:

Based on LLama 3 8B parameter model
Optimized through quantization
Runs on single CPU core
Compatible with popular tools like Olama and LM Studio

🎯 Use Cases:

Local development
Privacy-focused applications
Resource-constrained environments
Educational purposes

🔗 Links:
Patreon:   / mervinpraison  
Ko-fi: https://ko-fi.com/mervinpraison
Discord:   / discord  
Twitter / X :   / mervinpraison  
GPU for 50% of it's cost: https://bit.ly/mervin-praison Coupon: MervinPraison (A6000, A5000)
https://github.com/microsoft/BitNet
1BIT Paper https://arxiv.org/abs/2310.11453
code: https://mer.vin/2024/10/bitnet-instal...

#1bit #microsoft #LLM #BitNet #LocalAI #TechTutorial #Programming #artificialintelligence #ai

Timestamp:
0:00 - Introduction to BitNet CPU Performance
1:10 - Local Setup Introduction
1:22 - Installation Requirements Overview
2:13 - Setup Process Step 1: Installing Packages
2:20 - Setup Process Step 2: Virtual Environment
2:37 - Setup Process Step 3: Cloning Repository
3:00 - Setup Process Step 4: Installing Dependencies
3:19 - Setup Process Step 5: Model Download
3:52 - Model Testing & Demonstration
4:27 - CPU Performance Monitoring
5:11 - Conclusion & Future Implications

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