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Tachyum Successfully Quantized DeepSeek LLM to its 2-bit TAI2

  • Jun 3, 2025 Date of publishing
  • 13 Pages

Introduction

In the rapidly evolving field of artificial intelligence, model efficiency and scalability are paramount.

Recent research and practices have empirically demonstrated that, with sufficient training data available, scaling language models with increased parameters and computational budgets can yield remarkably stronger models.

These large models, leveraging their extensive training data, provide versatile solutions for a wide range of downstream tasks. However, modern datasets become increasingly diverse and complex.

The development of large language models faces two major challenges:

  1. The enormous consumption of computational resources and deployment difficulties
  2. Difficulty in fitting heterogeneous and complex data, which limits the usability of the models.

Mixture of Experts (MoE) models have recently attracted much attention in addressing these challenges, by dynamically selecting and activating the most relevant sub models to process input data.

It has been shown that MoEs can significantly improve model performance and efficiency with fewer resources, particularly excelling in handling large-scale, multimodal data.