Hugging Face Trending Papers

Scaling Neural Network Verification with Tensor Parallelism and Fully Sharded Data Parallelism

Formal neural network verification -- proving that a network satisfies safety properties for \emph{all} inputs in a specified domain -- is bounded in practice by GPU memory: standard implementations of bound-propagation algorithms (IBP, CROWN, $α$-CROWN) require weight and relaxation-coefficient matrices to reside entirely on one accelerator. We adapt two parallelism techniques originally developed for large-scale model training to the \texttt{auto\_LiRPA}\,/\,$α,β$-CROWN verification framework.

arXiv AI

Jun 9

Scaling Neural Network Verification with Tensor Parallelism and Fully Sharded Data Parallelism

arXiv:2606. 09377v1 Announce Type: cross Abstract: Formal neural network verification -- proving that a network satisfies safety properties for \emph{all} inputs in a specified domain -- is bounded in practice by GPU memory: standard implementations of bound-propagation algorithms (IBP, CROWN, $\alpha$-CROWN) require weight and relaxation-coefficient matrices to reside entirely on one accelerator.

By Sergei Vorobyov, Eugene Ilyushin

benchmarks safety

arXiv AI

Jul 17

A Modern Multimodal Assistant on a 6 GB 2011 GPU: Stage-Validated, All-GPU CUDA Inference for Fermi

arXiv:2607. 14568v1 Announce Type: cross Abstract: A companion study ran a 35B mixture-of-experts model on a 2011 NVIDIA Tesla C2075 (Fermi, sm_20, 6GB) as a GPU-prefill/CPU-decode hybrid, because the 4-bit model did not fit in device memory (arXiv:2606.

By A. C. Opus, J. Q. Lu

rag multimodal benchmarks

arXiv Machine Learning

Jun 3

Demystifying Pipeline Parallelism: First Theory for PipeDream

arXiv:2606. 03498v1 Announce Type: new Abstract: Training modern machine learning models increasingly requires computation to be distributed across many accelerators.

By Ivan Ilin, Peter Richt\'arik

arXiv AI

6d ago

TEMPO: Makespan-Aware Expert-Parallel Load Balancing Across Memory- and Compute-Bound Regimes

arXiv:2608. 13057v1 Announce Type: cross Abstract: In expert-parallel (EP) MoE serving, every layer synchronizes at the slowest GPU.

By Jie Li, Chenxin Jia, Jinliang Shen, Cunzhuang Liu, Ruiyi Ding, Jianwen Xian, Kang He, Chengru Song

efficiency

arXiv AI

Jun 9

TAO: Tolerance-Aware Optimistic Verification for Floating-Point Neural Networks

arXiv:2510. 16028v4 Announce Type: replace-cross Abstract: Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces).

By Jianzhu Yao, Hongxu Su, Taobo Liao, Zerui Cheng, Huan Zhang, Xuechao Wang, Pramod Viswanath

llms rag diffusion efficiency safety

arXiv AI

Jun 8

FP8 is All You Need (Part 1): Debunking Hardware FP64 as the HPC Holy Grail

arXiv:2606. 06510v1 Announce Type: cross Abstract: Conventional HPC dogma holds that native hardware FP64 silicon is the irreducible foundation of scientific computing -- the "holy grail" of double-precision simulation.

By Satoshi Matsuoka

efficiency

arXiv Machine Learning

Jul 31

FORGE: Fused On-Register Gradient Elimination for Memory-Efficient LLM Training

arXiv:2606. 22932v2 Announce Type: replace Abstract: Reverse-mode differentiation computes every weight gradient, writes it to memory, and only then lets the optimizer read it back.

By Dikshant Kukreja, Kritarth Prasad, Avinash Anand, Zhengkui Wang, Erik Cambria, Timothy Liu, Aik Beng Ng, Simon See, Bapi Chatterjee

llms fine-tuning

arXiv AI

Jun 16

Communication-Efficient Verifiable Attention for LLM Inference

arXiv:2606. 16352v1 Announce Type: cross Abstract: Computation integrity of remote large language model (LLM) serving can be questionable.

By Ziqun Chen, Ming Wu, Michael Heinrich, Jason Zeng, Huiying Lan, Tianwei Zhang, Rui Tan

llms

arXiv AI

Jun 16

FP8 is All You Need (Part 1): Debunking Hardware FP64 as the HPC Holy Grail (June 13th version)

arXiv:2606. 06510v2 Announce Type: replace-cross Abstract: Conventional HPC holds that native hardware FP64 is the irreducible foundation of scientific computing.

By Satoshi Matsuoka

efficiency

arXiv Machine Learning

Jul 16

Weight Feedback Computes the Jacobian Transpose Locally in Modern Deep Networks

arXiv:2607. 13380v1 Announce Type: new Abstract: Predictive Coding (PC) offers a biologically motivated alternative to backpropagation via local weight updates, yet routing error between layers still relies on an autograd Jacobian-transpose ($J^\top$) product - the last non-local operation in PC.

By Junlong Shen, Xingyu Li

benchmarks

arXiv AI

Jun 9

APEX4: Efficient Pure W4A4 LLM Inference via Intra-SM Compute Rebalancing

arXiv:2606. 08761v1 Announce Type: cross Abstract: W4A4 quantization promises full utilization of INT4 Tensor Cores, yet group dequantization overhead on CUDA Cores has driven existing systems to mixed-precision fallbacks.

By Hong Guo, Nianhui Guo, Weixing Wang, Jona Otholt, Christoph Meinel, Haojin Yang

llms efficiency benchmarks

arXiv AI

Jul 22

Federated Lightweight Fine-Tuning

arXiv:2607. 18343v1 Announce Type: cross Abstract: Federated fine-tuning is bottlenecked by communication: FedAvg and pseudo-gradient schemes transmit a payload that scales with the model, and gradient compression shrinks it by only a constant factor.

By Radhakrishna Achanta, Will Reed

fine-tuning

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