# Flextron: Many-in-One Flexible Large Language Model

arXiv: https://arxiv.org/abs/2406.10260 · HTML: https://mlsanity.com/paper/2406.10260/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 53 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 30 of 42)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [PIQA: Reasoning about Physical Commonsense in Natural Language](https://mlsanity.com/paper/1911.11641/) (2019)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers](https://mlsanity.com/paper/2210.17323/) (2022)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://mlsanity.com/paper/2304.01373/) (2023)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [The LAMBADA dataset: Word prediction requiring a broad discourse context](https://mlsanity.com/paper/1606.06031/) (2016)
- [DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification](https://mlsanity.com/paper/2106.02034/) (2021)
- [H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models](https://mlsanity.com/paper/2306.14048/) (2023)
- [Once-for-All: Train One Network and Specialize it for Efficient Deployment](https://mlsanity.com/paper/1908.09791/) (2019)
- [Scaling Vision with Sparse Mixture of Experts](https://mlsanity.com/paper/2106.05974/) (2021)
- [LLM-Pruner: On the Structural Pruning of Large Language Models](https://mlsanity.com/paper/2305.11627/) (2023)
- [Mixture-of-Experts with Expert Choice Routing](https://mlsanity.com/paper/2202.09368/) (2022)
- [Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning](https://mlsanity.com/paper/2310.06694/) (2023)
- [SliceGPT: Compress Large Language Models by Deleting Rows and Columns](https://mlsanity.com/paper/2401.15024/) (2024)
- [AdaViT: Adaptive Tokens for Efficient Vision Transformer](https://mlsanity.com/paper/2112.07658/) (2021)
- [Slimmable Neural Networks](https://mlsanity.com/paper/1812.08928/) (2018)
- [Matryoshka Representation Learning](https://mlsanity.com/paper/2205.13147/) (2022)
- [NeMo: a toolkit for building AI applications using Neural Modules](https://mlsanity.com/paper/1909.09577/) (2019)
- [Universally Slimmable Networks and Improved Training Techniques](https://mlsanity.com/paper/1903.05134/) (2019)

## Built on by (most cited first, 30 of 45)

- [Small Language Models are the Future of Agentic AI](https://mlsanity.com/paper/2506.02153/) (2025)
- [Universal Model Routing for Efficient LLM Inference](https://mlsanity.com/paper/2502.08773/) (2025)
- [Jet-Nemotron: Efficient Language Model with Post Neural Architecture Search](https://mlsanity.com/paper/2508.15884/) (2025)
- [Mixture of Nested Experts: Adaptive Processing of Visual Tokens](https://mlsanity.com/paper/2407.19985/) (2024)
- [Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding](https://mlsanity.com/paper/2607.05722/) (2026)
- [DarwinLM: Evolutionary Structured Pruning of Large Language Models](https://mlsanity.com/paper/2502.07780/) (2025)
- [Cost-Aware Contrastive Routing for LLMs](https://mlsanity.com/paper/2508.12491/) (2025)
- [AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment](https://mlsanity.com/paper/2411.10606/) (2024)
- [Learning to Inference Adaptively for Multimodal Large Language Models](https://mlsanity.com/paper/2503.10905/) (2025)
- [ERNIE 5.0 Technical Report](https://mlsanity.com/paper/2602.04705/) (2026)
- [When Continue Learning Meets Multimodal Large Language Model: A Survey](https://mlsanity.com/paper/2503.01887/) (2025)
- [MatMamba: A Matryoshka State Space Model](https://mlsanity.com/paper/2410.06718/) (2024)
- [Duo-LLM: A Framework for Studying Adaptive Computation in Large Language Models](https://mlsanity.com/paper/2410.10846/) (2024)
- [MAVERIX: Multimodal Audio-Visual Evaluation and Recognition IndeX](https://mlsanity.com/paper/2503.21699/) (2025)
- [ThinkingViT: Matryoshka Thinking Vision Transformer for Elastic Inference](https://mlsanity.com/paper/2507.10800/) (2025)
- [EA-ViT: Efficient Adaptation for Elastic Vision Transformer](https://mlsanity.com/paper/2507.19360/) (2025)
- [Training Matryoshka Mixture-of-Experts for Elastic Inference-Time Expert Utilization](https://mlsanity.com/paper/2509.26520/) (2025)
- [DLP: Dynamic Layerwise Pruning in Large Language Models](https://mlsanity.com/paper/2505.23807/) (2025)
- [Nemotron Elastic: Towards Efficient Many-in-One Reasoning LLMs](https://mlsanity.com/paper/2511.16664/) (2025)
- [UniQL: Unified Quantization and Low-rank Compression for Adaptive Edge LLMs](https://mlsanity.com/paper/2512.03383/) (2025)
- [Deep Hierarchical Learning with Nested Subspace Networks for Large Language Models](https://mlsanity.com/paper/2509.17874/) (2025)
- [Masked Generative Nested Transformers with Decode Time Scaling](https://mlsanity.com/paper/2502.00382/) (2025)
- [FlexGS: Train Once, Deploy Everywhere with Many-in-One Flexible 3D Gaussian Splatting](https://mlsanity.com/paper/2506.04174/) (2025)
- [Star Elastic: Many-in-One Reasoning LLMs with Efficient Budget Control](https://mlsanity.com/paper/2605.07182/) (2026)
- [Mixture of Hidden-Dimensions Transformer](https://mlsanity.com/paper/2412.05644/) (2024)
- [DTRNet: Dynamic Token Routing Network to Reduce Quadratic Costs in Transformers](https://mlsanity.com/paper/2509.00925/) (2025)
- [FlexRank: Nested Low-Rank Knowledge Decomposition for Adaptive Model Deployment](https://mlsanity.com/paper/2602.02680/) (2026)
- [AdaPerceiver: Transformers with Adaptive Width, Depth, and Tokens](https://mlsanity.com/paper/2511.18105/) (2025)
- [SALAAD: Sparse And Low-Rank Adaptation via ADMM for Large Language Model Inference](https://mlsanity.com/paper/2602.00942/) (2026)
- [TraceNAS: Zero-shot LLM Pruning via Gradient Trace Correlation](https://mlsanity.com/paper/2602.02891/) (2026)

## Abstract (arXiv)

Training modern LLMs is extremely resource intensive, and customizing them for various deployment scenarios characterized by limited compute and memory resources through repeated training is impractical. In this paper, we introduce Flextron, a network architecture and post-training model optimization framework supporting flexible model deployment. The Flextron architecture utilizes a nested elastic structure to rapidly adapt to specific user-defined latency and accuracy targets during inference with no additional fine-tuning required. It is also input-adaptive, and can automatically route tokens through its sub-networks for improved performance and efficiency. We present a sample-efficient training method and associated routing algorithms for systematically transforming an existing trained LLM into a Flextron model. We evaluate Flextron on the GPT-3 and LLama-2 family of LLMs, and demonstrate superior performance over multiple end-to-end trained variants and other state-of-the-art elastic networks, all with a single pretraining run that consumes a mere 7.63% tokens compared to original pretraining.