Cover Image

LLMQ: Efficient Lower-Precision Pretraining for Consumer GPUs

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Schultheis, Erik, Alistarh, Dan
Format:	Preprint
Published:	2025
Subjects:	Distributed, Parallel, and Cluster Computing Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Efficient Training on Multiple Consumer GPUs with RoundPipe
by: Luo, Yibin, et al.
Published: (2026)

Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence
by: Ansaripour, Matin, et al.
Published: (2022)

Taming Unbalanced Training Workloads in Deep Learning with Partial Collective Operations
by: Li, Shigang, et al.
Published: (2019)

Simple Opinion Dynamics for No-Regret Learning
by: Lazarsfeld, John, et al.
Published: (2023)

HeterMoE: Efficient Training of Mixture-of-Experts Models on Heterogeneous GPUs
by: Wu, Yongji, et al.
Published: (2025)

GOGH: Correlation-Guided Orchestration of GPUs in Heterogeneous Clusters
by: Raeisi, Ahmad, et al.
Published: (2025)

HetCCL: Accelerating LLM Training with Heterogeneous GPUs
by: Kim, Heehoon, et al.
Published: (2026)

Tessera: Unlocking Heterogeneous GPUs through Kernel-Granularity Disaggregation
by: Hu, Tiancheng, et al.
Published: (2026)

Communication-Avoiding Linear Algebraic Kernel K-Means on GPUs
by: Bellavita, Julian, et al.
Published: (2026)

Scaling State-Space Models on Multiple GPUs with Tensor Parallelism
by: Dutt, Anurag, et al.
Published: (2026)

AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUs
by: Akinwande, Victor, et al.
Published: (2024)

AReaL-Hex: Accommodating Asynchronous RL Training over Heterogeneous GPUs
by: Yan, Ran, et al.
Published: (2025)

FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion
by: Chang, Li-Wen, et al.
Published: (2024)

PipeFill: Using GPUs During Bubbles in Pipeline-parallel LLM Training
by: Arfeen, Daiyaan, et al.
Published: (2024)

When GPUs Fail Quietly: Observability-Aware Early Warning Beyond Numeric Telemetry
by: Bidollahkhani, Michael, et al.
Published: (2026)

FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs
by: Zhou, Yuanchang, et al.
Published: (2024)

MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs
by: Jiang, Ziheng, et al.
Published: (2024)

Nixie: Efficient, Transparent Temporal Multiplexing for Consumer GPUs
by: Xu, Yechen, et al.
Published: (2026)

MoEBlaze: Breaking the Memory Wall for Efficient MoE Training on Modern GPUs
by: Zhang, Jiyuan, et al.
Published: (2026)

BLaST: High Performance Inference and Pretraining using BLock Sparse Transformers
by: Okanovic, Patrik, et al.
Published: (2025)

FineServe: Precision-Aware KV Slab and Two-Level Scheduling for Heterogeneous Precision LLM Serving
by: Bin, Kyungmin, et al.
Published: (2025)

PIPO: Pipelined Offloading for Efficient Inference on Consumer Devices
by: Liu, Yangyijian, et al.
Published: (2025)

FlashMLA-ETAP: Efficient Transpose Attention Pipeline for Accelerating MLA Inference on NVIDIA H20 GPUs
by: Dege, Pengcuo, et al.
Published: (2025)

HOBBIT: A Mixed Precision Expert Offloading System for Fast MoE Inference
by: Tang, Peng, et al.
Published: (2024)

SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs
by: Li, Kaiwei, et al.
Published: (2016)

ByteScale: Efficient Scaling of LLM Training with a 2048K Context Length on More Than 12,000 GPUs
by: Ge, Hao, et al.
Published: (2025)

Helix: Serving Large Language Models over Heterogeneous GPUs and Network via Max-Flow
by: Mei, Yixuan, et al.
Published: (2024)

Harnessing Your DRAM and SSD for Sustainable and Accessible LLM Inference with Mixed-Precision and Multi-level Caching
by: Peng, Jie, et al.
Published: (2024)

Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach
by: Saroliya, Urvij, et al.
Published: (2024)

MIREncoder: Multi-modal IR-based Pretrained Embeddings for Performance Optimizations
by: Dutta, Akash, et al.
Published: (2024)

Coral: Cost-Efficient Multi-LLM Serving over Heterogeneous Cloud GPUs
by: Mei, Yixuan, et al.
Published: (2026)

Communication-Efficient Federated Bilevel Optimization with Local and Global Lower Level Problems
by: Li, Junyi, et al.
Published: (2023)

Efficient AllReduce with Stragglers
by: Devraj, Arjun, et al.
Published: (2025)

MoE-Lightning: High-Throughput MoE Inference on Memory-constrained GPUs
by: Cao, Shiyi, et al.
Published: (2024)

Communication-Efficient Federated Fine-Tuning
by: Theologitis, Michael, et al.
Published: (2025)

Communication Efficient and Provable Federated Unlearning
by: Tao, Youming, et al.
Published: (2024)

FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression
by: Tang, Zhenheng, et al.
Published: (2024)

FedORGP: Guiding Heterogeneous Federated Learning with Orthogonality Regularization on Global Prototypes
by: Guo, Fucheng, et al.
Published: (2025)

Towards Efficient Replay in Federated Incremental Learning
by: Li, Yichen, et al.
Published: (2024)

Federated Communication-Efficient Multi-Objective Optimization
by: Askin, Baris, et al.
Published: (2024)