--- title: "torchtune vs octoml-profile" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/meta-pytorch-torchtune-vs-octoml-octoml-profile" tools: ["meta-pytorch-torchtune", "octoml-octoml-profile"] --- # torchtune vs octoml-profile *GraphCanon updated Aug 7, 2026* ## Verdict Pick torchtune if a PyTorch-native post-training library focused on finetuning multimodal LLMs using state-of-the-art quantization techniques; pick octoml-profile if octoML PyTorch Profiler provides profiling and acceleration tools for PyTorch models with remote execution capabilities. [torchtune](https://pytorch.org/torchtune/main/) reports 5.8k GitHub stars, 743 forks, and 455 open issues, last pushed Aug 6, 2026. [octoml-profile](https://github.com/octoml/octoml-profile) has 113 stars, 10 forks, and 0 open issues, last pushed Apr 24, 2023. Figures are from public GitHub metadata via [torchtune's repository](https://github.com/meta-pytorch/torchtune) and [octoml-profile's repository](https://github.com/octoml/octoml-profile). | | [torchtune](/tools/meta-pytorch-torchtune.md) | [octoml-profile](/tools/octoml-octoml-profile.md) | | --- | --- | --- | | Tagline | PyTorch native post-training library | Home for OctoML PyTorch Profiler | | Stars | 5,793 | 113 | | Forks | 743 | 10 | | Open issues | 455 | 0 | | Language | Python | - | | Adopt for | A PyTorch-native post-training library focused on finetuning multimodal LLMs using state-of-the-art quantization techniques. | OctoML PyTorch Profiler provides profiling and acceleration tools for PyTorch models with remote execution capabilities. | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause | Apache-2.0 | | Categories | Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [torchtune](/tools/meta-pytorch-torchtune.md) | [octoml-profile](/tools/octoml-octoml-profile.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 1197d | | Open issues (now) | 455 | 0 | | Full report | [trust report](/tools/meta-pytorch-torchtune/trust.md) | [trust report](/tools/octoml-octoml-profile/trust.md) | ## Shared compatibility - **Python**: [torchtune](/tools/meta-pytorch-torchtune.md) - Python runtime; [octoml-profile](/tools/octoml-octoml-profile.md) - Python runtime ## Decision facts: torchtune - **Adopt for:** A PyTorch-native post-training library focused on finetuning multimodal LLMs using state-of-the-art quantization techniques. ## Decision facts: octoml-profile - **Adopt for:** OctoML PyTorch Profiler provides profiling and acceleration tools for PyTorch models with remote execution capabilities. ## Choose when ### Choose torchtune if… - License: torchtune is BSD-3-Clause, octoml-profile is Apache-2.0. - Tags unique to torchtune: multimodal-llms, post-training, quantization techniques. - - When you are working with the latest stable or preview nightly versions of PyTorch and need advanced finetuning for multimodal large language models (LLMs). ### Choose octoml-profile if… - License: octoml-profile is Apache-2.0, torchtune is BSD-3-Clause. - Tags unique to octoml-profile: acceleration, performance optimization, profiling. - Need precise performance metrics on different backend architectures like CPU, GPU in cloud environments ## When NOT to use torchtune - - If you rely on a fixed, older version of PyTorch as Torchtune only supports the latest stable and preview nightly versions. - - For scenarios where custom or non-PyTorch-native optimization methods are preferred over torchao’s quantization techniques. ## When NOT to use octoml-profile - Development for local, offline usage only without remote profiling needs - Working with PyTorch versions below 2.0 or incompatible with specific CUDA/Apple silicon versions outlined in installation guide ## Common questions ### What is the difference between torchtune and octoml-profile? torchtune: PyTorch native post-training library. octoml-profile: Home for OctoML PyTorch Profiler. See the comparison table for live GitHub stats and shared categories. ### When should I choose torchtune over octoml-profile? Choose torchtune over octoml-profile when License: torchtune is BSD-3-Clause, octoml-profile is Apache-2.0; Tags unique to torchtune: multimodal-llms, post-training, quantization techniques; - When you are working with the latest stable or preview nightly versions of PyTorch and need advanced finetuning for multimodal large language models (LLMs). ### When should I choose octoml-profile over torchtune? Choose octoml-profile over torchtune when License: octoml-profile is Apache-2.0, torchtune is BSD-3-Clause; Tags unique to octoml-profile: acceleration, performance optimization, profiling; Need precise performance metrics on different backend architectures like CPU, GPU in cloud environments. ### When should I avoid torchtune? - If you rely on a fixed, older version of PyTorch as Torchtune only supports the latest stable and preview nightly versions. - For scenarios where custom or non-PyTorch-native optimization methods are preferred over torchao’s quantization techniques. ### When should I avoid octoml-profile? Development for local, offline usage only without remote profiling needs Working with PyTorch versions below 2.0 or incompatible with specific CUDA/Apple silicon versions outlined in installation guide ### Is torchtune or octoml-profile more popular on GitHub? torchtune has more GitHub stars (5,793 vs 113). Stars measure visibility, not whether either tool fits your constraints. ### Are torchtune and octoml-profile open source? Yes - both are open-source projects on GitHub (torchtune: BSD-3-Clause, octoml-profile: Apache-2.0). ### Where can I find alternatives to torchtune or octoml-profile? GraphCanon lists graph-backed alternatives at [torchtune alternatives](/tools/meta-pytorch-torchtune/alternatives) and [octoml-profile alternatives](/tools/octoml-octoml-profile/alternatives) ([torchtune markdown twin](/tools/meta-pytorch-torchtune/alternatives.md), [octoml-profile markdown twin](/tools/octoml-octoml-profile/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/meta-pytorch-torchtune-vs-octoml-octoml-profile.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, torchtune or octoml-profile? torchtune: Very active. octoml-profile: Dormant. 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 torchtune and octoml-profile? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [torchtune trust report](/tools/meta-pytorch-torchtune/trust); [octoml-profile trust report](/tools/octoml-octoml-profile/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=meta-pytorch-torchtune`](/api/graphcanon/graph?tool=meta-pytorch-torchtune) - 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/_