--- title: "pytorch-lightning vs 3D-Mem" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lightning-ai-pytorch-lightning-vs-umass-embodied-agi-3d-mem" tools: ["lightning-ai-pytorch-lightning", "umass-embodied-agi-3d-mem"] --- # pytorch-lightning vs 3D-Mem *GraphCanon updated Jul 11, 2026* ## Verdict Pick pytorch-lightning when license: pytorch-lightning is Apache-2.0, 3D-Mem is MIT; pick 3D-Mem when license: 3D-Mem is MIT, pytorch-lightning is Apache-2.0. [pytorch-lightning](https://lightning.ai/pytorch-lightning/?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) reports 31k GitHub stars, 3.8k forks, and 1.0k open issues, last pushed Jul 10, 2026. [3D-Mem](https://umass-embodied-agi.github.io/3D-Mem/) has 264 stars, 17 forks, and 3 open issues, last pushed Oct 2, 2025. Figures are from public GitHub metadata via [pytorch-lightning's repository](https://github.com/Lightning-AI/pytorch-lightning) and [3D-Mem's repository](https://github.com/UMass-Embodied-AGI/3D-Mem). | | [pytorch-lightning](/tools/lightning-ai-pytorch-lightning.md) | [3D-Mem](/tools/umass-embodied-agi-3d-mem.md) | | --- | --- | --- | | Tagline | Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes. | [CVPR 2025] Source codes for the paper "3D-Mem: 3D Scene Memory for Embodied Exploration and Reasoning" | | Stars | 31,233 | 264 | | Forks | 3,756 | 17 | | Open issues | 1,049 | 3 | | Language | Python | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Model Training, Computer Vision | Vector Databases, Model Training, Computer Vision | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [pytorch-lightning](/tools/lightning-ai-pytorch-lightning.md) | [3D-Mem](/tools/umass-embodied-agi-3d-mem.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 1d | 281d | | Open issues (now) | 1.0k | 3 | | Security scan | No criticals | No lockfile | | Full report | [trust report](/tools/lightning-ai-pytorch-lightning/trust.md) | [trust report](/tools/umass-embodied-agi-3d-mem/trust.md) | ## Shared compatibility - **Python**: [pytorch-lightning](/tools/lightning-ai-pytorch-lightning.md) - Python runtime; [3D-Mem](/tools/umass-embodied-agi-3d-mem.md) - Python runtime ## Choose when ### Choose pytorch-lightning if… - License: pytorch-lightning is Apache-2.0, 3D-Mem is MIT. - Tags unique to pytorch-lightning: data-science, deep-learning, artificial-intelligence, machine-learning. - More GitHub stars (31k vs 264) - visibility, not fit. ### Choose 3D-Mem if… - License: 3D-Mem is MIT, pytorch-lightning is Apache-2.0. - Tags unique to 3D-Mem: spatial-intelligence, embodied-ai, computer-vision. - Also covers Vector Databases. ## When NOT to use pytorch-lightning - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use 3D-Mem - Last GitHub push was 282 days ago (slowing maintenance, Oct 2, 2025). Validate activity before betting a new project on 3D-Mem. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between pytorch-lightning and 3D-Mem? pytorch-lightning: Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.. 3D-Mem: [CVPR 2025] Source codes for the paper "3D-Mem: 3D Scene Memory for Embodied Exploration and Reasoning". See the comparison table for live GitHub stats and shared categories. ### When should I choose pytorch-lightning over 3D-Mem? Choose pytorch-lightning over 3D-Mem when License: pytorch-lightning is Apache-2.0, 3D-Mem is MIT; Tags unique to pytorch-lightning: data-science, deep-learning, artificial-intelligence, machine-learning; More GitHub stars (31k vs 264) - visibility, not fit. ### When should I choose 3D-Mem over pytorch-lightning? Choose 3D-Mem over pytorch-lightning when License: 3D-Mem is MIT, pytorch-lightning is Apache-2.0; Tags unique to 3D-Mem: spatial-intelligence, embodied-ai, computer-vision; Also covers Vector Databases. ### When should I avoid pytorch-lightning? Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid 3D-Mem? Last GitHub push was 282 days ago (slowing maintenance, Oct 2, 2025). Validate activity before betting a new project on 3D-Mem. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is pytorch-lightning or 3D-Mem more popular on GitHub? pytorch-lightning has more GitHub stars (31,233 vs 264). Stars measure visibility, not whether either tool fits your constraints. ### Are pytorch-lightning and 3D-Mem open source? Yes - both are open-source projects on GitHub (pytorch-lightning: Apache-2.0, 3D-Mem: MIT). ### Where can I find alternatives to pytorch-lightning or 3D-Mem? GraphCanon lists graph-backed alternatives at [pytorch-lightning alternatives](/tools/lightning-ai-pytorch-lightning/alternatives) and [3D-Mem alternatives](/tools/umass-embodied-agi-3d-mem/alternatives) ([pytorch-lightning markdown twin](/tools/lightning-ai-pytorch-lightning/alternatives.md), [3D-Mem markdown twin](/tools/umass-embodied-agi-3d-mem/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/lightning-ai-pytorch-lightning-vs-umass-embodied-agi-3d-mem.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, pytorch-lightning or 3D-Mem? pytorch-lightning: Very active. 3D-Mem: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for pytorch-lightning and 3D-Mem? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [pytorch-lightning trust report](/tools/lightning-ai-pytorch-lightning/trust); [3D-Mem trust report](/tools/umass-embodied-agi-3d-mem/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=lightning-ai-pytorch-lightning`](/api/graphcanon/graph?tool=lightning-ai-pytorch-lightning) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_