1-Bit LLM: The Most Efficient LLM Possible?

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I've been planning for a bitnet video for the longest time, and with the release of bitnet b1.58 2B4T gave me the perfect chance to brief you on the history of 1-bit LLM! Fun fact, the major bitnet research is mostly done by the same researchers.

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Quantifying the Capabilities of LLMs across Scale and Precision
[Paper] https://arxiv.org/abs/2405.03146v2

BitNet: Scaling 1-bit Transformers for Large Language Models
[Paper] https://arxiv.org/abs/2310.11453v1

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
[Paper] https://arxiv.org/abs/2402.17764v1

BitNet a4.8: 4-bit Activations for 1-bit LLMs
[Paper] https://arxiv.org/abs/2411.04965v1

Efficient Construction of Model Family through Progressive Training Using Model Expansion
[Paper] https://arxiv.org/abs/2504.00623v1

BitNet b1.58 2B4T Technical Report
[Paper] https://arxiv.org/abs/2504.12285
[Web Demo] https://bitnet-demo.azurewebsites.net/
[HuggingFace] https://huggingface.co/microsoft/bitnet-b1.58-2B-4T
[Code] https://github.com/microsoft/BitNet

[Additional Recs]
T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge
https://arxiv.org/abs/2407.00088v2

FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation
https://arxiv.org/abs/2407.07093v1

Matmul or No Matmul in the Era of 1-bit LLMs
https://arxiv.org/abs/2408.11939v2

1-bit AI Infra: Part 1.1, Fast and Lossless BitNet b1.58 Inference on CPUs
https://arxiv.org/abs/2410.16144v2

Bitnet.cpp: Efficient Edge Inference for Ternary LLMs
https://arxiv.org/abs/2502.11880v1

Continual Quantization-Aware Pre-Training: When to transition from 16-bit to 1.58-bit pre-training for BitNet language models?
https://arxiv.org/abs/2502.11895v1

(NEW!) BitNet v2: Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs
https://arxiv.org/abs/2504.18415

(NEW!) BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation
https://arxiv.org/abs/2506.07530
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