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TACO: Ternary Absolute-max Column-wise One-sparse Optimizer for LLM Fine-Tuning | AI Learning Platform

Fine-tuningCost Optimization

TACO: Ternary Absolute-max Column-wise One-sparse Optimizer for LLM Fine-Tuning

Jichao Jiang, Cristian McGee, El Houcine Bergou et al.1 min readarXivOct 1, 2026

Key Takeaway for Students

Full-parameter fine-tuning of large language models (LLMs) incurs substantial optimizer state memory overhead, limiting the model sizes that fit on modern GPUs. Existing approaches either compress optimizer state, abandon first-order gradients, or change the update geometry while retaining dense state. The recently introduced Muon optimizer reduces optimizer memory through matr

100 GPU

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Optimizing optimizer memory is crucial for fine-tuning larger LLMs on existing hardware, directly impacting the scale and accessibility of advanced models. This research offers a novel approach to reduce memory overhead without sacrificing performance, which is a common challenge in resource-constrained environments.

Read original on arXiv

Career Insight

high demand

Why it matters

Skills to learn

Implement and evaluate different optimizer types (e.g., AdamW, Muon, TACO)Profile GPU memory usage during LLM fine-tuningApply memory-efficient fine-tuning techniques (e.g., parameter-efficient fine-tuning, optimizer state compression)Understand the mathematical underpinnings of different optimization algorithmsBenchmark fine-tuning performance and memory footprint across various optimizers

Interview questions

Explain the memory overhead associated with full-parameter fine-tuning of LLMs, specifically focusing on optimizer states. How do optimizers like AdamW contribute to this?
Compare and contrast TACO with existing optimizer memory reduction techniques like Muon or gradient compression. What are the potential trade-offs in terms of performance, convergence, and computational efficiency?
Design a system for fine-tuning a 70B parameter LLM on a cluster of GPUs, where each GPU has limited memory. How would you incorporate memory-efficient optimizers and other techniques to maximize the model size and training throughput?

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