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Optimization story: Bloom inference

Fast Inference on Large Language Models: BLOOMZ on Habana Gaudi2 Accelerator

Making thousands of open LLMs bloom in the Vertex AI Model Garden

Atompack: A Storage and Distribution Layer for Read-Heavy Atomistic ML Training Datasets

arXiv:2606. 29975v1 Announce Type: new Abstract: Atomistic machine learning datasets are increasingly used for training: large immutable snapshots are read repeatedly, shuffled across epochs, staged across clusters' storage systems, and republished as reusable scientific artifacts.

scDataset: Scalable Data Loading for Deep Learning on Large-Scale Single-Cell Omics

arXiv:2506. 01883v3 Announce Type: replace-cross Abstract: Training deep learning models on single-cell datasets with hundreds of millions of cells requires loading data from disk, as these datasets exceed available memory.

Atompack: A Storage and Distribution Layer for Read-Heavy Atomistic ML Training Datasets

Atomistic machine learning datasets are increasingly used for training: large immutable snapshots are read repeatedly, shuffled across epochs, staged across clusters' storage systems, and republished as reusable scientific artifacts. This workload differs from interactive scientific curation, where mutable records and ad hoc inspection are often more important than random indexed throughput.

Up to 3.2x Faster Inference with LFM2.5-DSpark

DARTree: Speculative Diffusion Decoding with Autoregressive Draft Trees

Speculative decoding losslessly accelerates autoregressive language models by verifying multiple draft tokens in parallel. Diffusion-based drafters further reduce proposal latency by predicting an entire token block in parallel, but their position-wise distributions are marginal rather than conditioned on tokens selected along each draft path.

Dion3: Full-Stack Orthogonal Updates

arXiv:2608. 11612v1 Announce Type: cross Abstract: The Muon optimizer incurs a significant overhead cost due to its cubic-time Newton-Schulz orthogonalization step.

Estimating Orbital Parameters of Direct Imaging Exoplanet Using Neural Network

arXiv:2510. 17459v3 Announce Type: replace-cross Abstract: In this work, we propose a flow-matching Markov chain Monte Carlo (FM-MCMC) algorithm for estimating the orbital parameters of exoplanetary systems, especially for those only one exoplanet is involved.

DARTree: Speculative Diffusion Decoding with Autoregressive Draft Trees

arXiv:2608. 13524v1 Announce Type: new Abstract: Speculative decoding losslessly accelerates autoregressive language models by verifying multiple draft tokens in parallel.

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