In this video we dive into a recent research paper by Microsoft: "The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits".
This paper introduce an interesting and exciting architecture for large language models, called BitNet b1.58, which significantly reduces LLMs memory consumption, and speeds-up LLMs inference latency. All of that, while showing promising results, that do not fall from a comparable LLaMA model!
Large language models quantization is already tackling the same problem, and we'll explain the benefits of BitNet b1.58 comparing to common quantization techniques.

BitNet b1.58 is an improvement for the BitNet model presented few months ago.

BitNet b1.58 paper - https://arxiv.org/abs/2402.17764
BitNet paper - https://arxiv.org/abs/2310.11453
Blog post - https://aipapersacademy.com/the-era-of-1-bit-llms/

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Chapters:
0:00 Paper Introduction
0:55 Quantization
1:31 Introducing BitNet b1.58
2:55 BitNet b1.58 Benefits
4:01 BitNet b1.58 Architecture
4:46 Results

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Microsoft's BitNet b1.58 paper proposes a novel LLM architecture where every model weight is constrained to ternary values (-1, 0, or 1), requiring only 1.58 bits per weight. Unlike post-training quantization, the model is trained from scratch using absolute mean quantization via a BitLinear layer. This eliminates matrix multiplications in favor of simple additions, enabling significant memory and latency reductions. Benchmarks against reproduced LLaMA models show BitNet b1.58 uses 3.3x less memory and runs 2.7–4.1x faster at 3B–70B scales, while matching or slightly exceeding LLaMA's accuracy. The efficiency gains grow as model size increases, making this approach particularly promising for large-scale deployment.

The Era of 1-bit LLMs by Microsoft | AI Paper Explained