BitNet b1.58 2B4T : The 1st 1-Bit LLM is here
The Era of 1-bit LLMs with BitNet b1.58
Amongst all the hype around big LLMS like o3, o4 Mini, Gemini 2.5 Pro, the biggest release of the year has come —
the first-ever one-bit LLM is out now
https://medium.com/media/c9fa3c45a6b22483b14517d20d1c6543/href
If you don’t recollect, try remembering the Blockbuster paper from last year.
The Era of 1-bit LLMs with BitNet b1.58
https://medium.com/media/9059b8aa917513c560b96a1feab02200/href
In case you have missed this paper,
What is 1-Bit LLM?
https://medium.com/media/f45a1e15be6b12e895fcc6b4c36c1f22/href
A 1-bit LLM is a large language model where weights are stored as binary values (0 or 1), drastically reducing memory usage (e.g., ~0.8GB for 7B params vs. ~14GB in 16-bit). BitNet b1.58 extends this with ternary values (-1, 0, +1) (1.58-bit), balancing efficiency and performance.
Key Differences vs. Quantisation:
Quantisation: Reduces precision (e.g., 32-bit → 8-bit) but keeps numerical values.
1-bit LLM: Replaces weights with discrete binary/ternary values, requiring architectural changes to maintain performance.
Data Science in Your Pocket – No Rocket Science
Coming back to BitNet b1.58 2B4T LLM
And now, after a long wait, the first-ever one-bit LLM is live — BitNet b1.58 2B4T by Microsoft
Key Features Bitnet b1.58 2B4T Model
General Overview
Developer: Microsoft Research
Model Type: Native 1-bit Large Language Model (LLM)
License: MIT
Release Status: Open-source
Parameter Size: ~2 Billion
Training Tokens: 4 Trillion
Context Length: 4096 tokens
Architecture & Quantization
- Framework: BitNet (Transformer-based with BitLinear layers)
- Quantization:
Weights: Ternary values {-1, 0, +1} (1.58-bit) using absmean quantization
Activations: 8-bit integers (per-token absmax quantization)
- Key Modifications:
Rotary Position Embeddings (RoPE)
Squared ReLU (ReLU²) activation in FFN layers
Subln normalization (no bias in linear/norm layers)
Training & Fine-Tuning
Pre-training:
- Large-scale training on public text, code, and synthetic math data.
- Two-stage learning rate & weight decay schedule.
Supervised Fine-Tuning (SFT): Instruction-following & conversational datasets.
Direct Preference Optimization (DPO): Aligned with human preferences.
Performance & Efficiency
- Memory Efficiency: 0.4GB (non-embedding weights) vs. 1.4–4.8GB in comparable models.
- Latency (CPU Decoding): 29ms (faster than LLaMA 3.2 1B, Gemma-3 1B, etc.).
- Energy Efficiency: 0.028J per inference (significantly lower than competitors).
- Benchmarks:
Outperforms similar-sized models in ARC-Challenge, GSM8K, MMLU, and CommonsenseQA.
Competitive in HellaSwag, PIQA, WinoGrande, and BoolQ.
Model Variants
microsoft/bitnet-b1.58-2B-4T: Packed 1.58-bit weights (optimized for inference).
microsoft/bitnet-b1.58-2B-4T-bf16: BF16 master weights (for training/fine-tuning).
microsoft/bitnet-b1.58-2B-4T-gguf: GGUF format (CPU inference via bitnet.cpp).
Usage Notes
For Efficiency Gains: Must use bitnet.cpp (C++ implementation) instead of standard transformers.
Current transformers Limitation: No optimized kernels → no speed/energy benefits yet.
Tokenizer: LLaMA 3 Tokenizer (vocab size: 128,256).
Key Advantages
✅ Extremely memory-efficient (0.4GB vs. 2–4.8GB in competitors).
✅ Lower latency & energy consumption (ideal for edge/CPU deployment).
✅ Competitive performance despite 1.58-bit quantization.
Limitations
⚠ Not optimized for transformers yet (requires bitnet.cpp for efficiency).
⚠ May still produce biased/inaccurate outputs (research use recommended).
Applications
- Efficient LLM deployment (edge devices, low-power systems).
- Research in 1-bit LLMs.
- Conversational AI & instruction-following tasks.
Benchmarks and metrics
BitNet b1.58 outperforms comparable small LLMs (1B–2B params) in efficiency, speed, and accuracy, despite using 1.58-bit weights (vs. full-precision competitors).
Why BitNet b1.58 Stands Out:
✅ Most Efficient: 0.4GB memory (vs. 1.4–4.8GB in others).
✅ Fastest Inference: 29ms latency (beats all competitors).
✅ Lowest Energy Use: 0.028J per inference (6× better than Gemma-3).
✅ Strong Accuracy: Top 2 in average benchmark scores, despite 1.58-bit weights.
✅ Best in Math & Reasoning: Leads in GSM8K (58.38) and WinoGrande (71.90).
How to use BitNet b1.58 2B4T ?
The model weights are open-sourced and are hosted on Hugging Face.
microsoft/bitnet-b1.58-2B-4T · Hugging Face
The same page comprises the code on how to use the model using Python.
It can even be used with bitnet.cpp
GitHub – microsoft/BitNet: Official inference framework for 1-bit LLMs
Conclusion,
The era of 1-bit LMS is finally here, and it really looks promising given the benchmarks and ease of use. Given the hype around big LLMs like O3 or O4 Mini or Beats Gemini 2.5 Pro, we must remember that it’s the small models that would be going into production and being open source is their key advantage. This is one of the biggest releases of the year, and I hope you try out BitNet b1.58.
BitNet b1.58 2B4T : The 1st 1-Bit LLM is here was originally published in Data Science in Your Pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
