pith. machine review for the scientific record. sign in

arXiv preprint arXiv:2104.07857 , year=

ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning , author= · 2021 · arXiv 2104.07857

2 Pith papers cite this work. Polarity classification is still indexing.

2 Pith papers citing it
read on arXiv browse 2 citing papers

fields

cs.DC 1 cs.LG 1

years

2026 1 2022 1

verdicts

CONDITIONAL 2

representative citing papers

Efficient Training on Multiple Consumer GPUs with RoundPipe

cs.DC · 2026-04-29 · conditional · novelty 8.0

RoundPipe achieves near-zero-bubble pipeline parallelism for LLM training on consumer GPUs by dynamically dispatching computation stages round-robin, yielding 1.48-2.16x speedups and enabling 235B model fine-tuning on 8x RTX 4090.

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

cs.LG · 2022-08-15 · conditional · novelty 7.0

LLM.int8() performs 8-bit inference for transformers up to 175B parameters with no accuracy loss by combining vector-wise quantization for most features with 16-bit mixed-precision handling of systematic outlier dimensions.

citing papers explorer

Showing 2 of 2 citing papers.

  • Efficient Training on Multiple Consumer GPUs with RoundPipe cs.DC · 2026-04-29 · conditional · none · ref 42

    RoundPipe achieves near-zero-bubble pipeline parallelism for LLM training on consumer GPUs by dynamically dispatching computation stages round-robin, yielding 1.48-2.16x speedups and enabling 235B model fine-tuning on 8x RTX 4090.

  • LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale cs.LG · 2022-08-15 · conditional · none · ref 77

    LLM.int8() performs 8-bit inference for transformers up to 175B parameters with no accuracy loss by combining vector-wise quantization for most features with 16-bit mixed-precision handling of systematic outlier dimensions.