# jiaweizzhao/GaLore

GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Repository: https://github.com/jiaweizzhao/GaLore
Canonical: https://ross.abutalabs.com/products/galore
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2024-10-28T18:01:54+00:00

## Health v2 (maintenance only)
Score: 25/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 65
- inputs: {"age_days": 910, "days_push": 674, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1700, forks 167 (observed 2026-08-28T04:05:24.249038+00:00)

## What it is
GaLore is a PyTorch library implementing Gradient Low-Rank Projection for memory-efficient full-parameter training of large language models. It plugs into existing optimizers like AdamW with minimal code changes, reducing memory usage compared to low-rank adaptation methods such as LoRA.

## Use cases
- train LLMs on consumer GPUs with limited memory
- reduce optimizer memory footprint during full-parameter fine-tuning
- fine-tune large language models without LoRA
- pretrain language models memory-efficiently
- apply gradient low-rank projection to existing optimizers
- run 7B model training on a single RTX 4090

## When to choose
- you need full-parameter learning but lack GPU memory for standard optimizers
- you want a drop-in optimizer replacement with two lines of code
- you are fine-tuning or pretraining LLMs on limited hardware

## When to avoid
- you need a battle-tested production training framework rather than a research algorithm
- your model is small enough that memory is not a constraint
- you require LoRA-style adapter export for inference

## Facets
- artifact type: library
- maturity: active
- function: llm-training, machine-learning, gpu-computing
- domain: large-language-models, deep-learning, machine-learning
- platform: python
- tags: gradient-low-rank-projection, memory-efficient-training, optimizer, lora-alternative, pytorch, gpu

## Member repositories
- jiaweizzhao/GaLore (main) score 25

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:24.249038+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:37:46.076010+00:00, confidence not recorded.
  - readme: https://github.com/jiaweizzhao/GaLore (fetched 2026-08-28T04:05:24.249038+00:00, sha 6d2f4645104f)
- Data as of 2026-08-30T08:39:29.467469+00:00.