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AI Infrastructure · Top 10

The 10 Best Distributed Training Frameworks in 2027

By Kory White · Updated Oct 2026 · 5 min read
PyTorch FSDP
Direct answer

PyTorch FSDP is the best distributed training framework: it shards parameters, gradients and optimizer states across GPUs and is built into PyTorch. DeepSpeed is #2 and Megatron-LM is #3. The ranking is based on what each does for multi-GPU training, then on how recently each project shipped a release.

At a glance

#PickPriceBest for
1PyTorch FSDPFree; part of the open-source PyTorch repository on GitHubTeams that want sharded training built into PyTorch itself
2DeepSpeedFree; Apache-2.0 licenseTraining very large models with ZeRO memory savings
3Megatron-LMFree; open-source code on GitHubNVIDIA GPU teams building custom transformer training at scale
4Hugging Face AccelerateFree; Apache-2.0 licenseMoving an existing PyTorch loop to many GPUs with minimal edits
5Ray TrainFree; Apache-2.0 licenseRunning other frameworks’ training jobs on a cluster you manage with Ray
6PyTorch LightningFree; Apache-2.0 licenseResearchers who want less boilerplate and a switchable strategy
7NVIDIA NeMo AutomodelFree; Apache-2.0 licenseFine-tuning Hugging Face models with PyTorch-native parallelism on NVIDIA GPUs
8Colossal-AIFree; Apache-2.0 licenseExperimenting with many parallelism strategies in one library
9MosaicML ComposerFree; Apache-2.0 licenseFSDP training with elastic sharded checkpoints and streaming data
10HorovodFree; Apache-2.0 licenseMaintaining an existing Horovod job that already works

Pick by what you want

Best overall
PyTorch FSDP
Best for giant models
DeepSpeed
Best for NVIDIA GPU teams
Megatron-LM
Easiest to adopt
Hugging Face Accelerate
Best for clusters
Ray Train

The ranking

1

PyTorch FSDP

PyTorch FSDP
Answrs VerdictThe built-in standard for sharded PyTorch training.
Price: Free; part of the open-source PyTorch repository on GitHubHow it works: Shards model parameters, gradients and optimizer states across GPUs, so models that cannot fit on a single GPU can be trainedCurrent API: FSDP2, built on DTensor; the tutorial says FSDP1 is deprecatedLatest PyTorch release: v2.14.1, published Sep 30, 2026

PyTorch FSDP shards model parameters, gradients and optimizer states across GPUs, so models that cannot fit on one GPU can be trained. The current version, FSDP2, is built on DTensor, and the PyTorch tutorial says FSDP1 is deprecated. It is free and part of the open-source PyTorch repository; the latest release is v2.14.1, published Sep 30, 2026.

Go ifTeams that want sharded training built into PyTorch itself
Skip ifYou need ready-made tensor and pipeline parallelism
Why #1 over #2: It is built into PyTorch itself, while DeepSpeed is a separate library you add on top.
2

DeepSpeed

DeepSpeed
Answrs VerdictThe most proven memory-saving trainer for giant models.
Price: Free; Apache-2.0 licenseTrack record: Its README says DeepSpeed enabled language models such as MT-530B and BLOOMGitHub stars: 43,245 (as of Oct 20, 2026)Latest release: v0.19.7, published Sep 16, 2026

DeepSpeed is free under the Apache-2.0 license. Its README says it enabled language models such as MT-530B and BLOOM. The project has 43,245 GitHub stars as of Oct 20, 2026, and its latest release is v0.19.7, published Sep 16, 2026.

Go ifTraining very large models with ZeRO memory savings
Skip ifYou want the smallest possible setup
Why #2 over #3: It has a longer record on very large models than Megatron-LM’s GPU-specific building blocks.
3

Megatron-LM

Megatron-LM
Answrs VerdictNVIDIA’s GPU-tuned building blocks for transformer scale.
Price: Free; open-source code on GitHubParallelism: Megatron Core supports TP, PP, DP, EP and CP parallelism strategiesPrecision: FP16, BF16, FP8 and FP4 mixed precisionLatest release: core_v0.19.2, published Sep 18, 2026

Megatron-LM, from NVIDIA, includes Megatron Core, a composable library with TP, PP, DP, EP and CP parallelism strategies and FP16, BF16, FP8 and FP4 mixed precision. The code is free on GitHub; the latest release is core_v0.19.2, published Sep 18, 2026.

Go ifNVIDIA GPU teams building custom transformer training at scale
Skip ifYou want a one-line wrapper around your existing loop
Why #3 over #4: It gives you finer control over parallelism than Hugging Face Accelerate’s wrapper.
4

Hugging Face Accelerate

Hugging Face Accelerate
Answrs VerdictThe easiest on-ramp to multi-GPU training.
Price: Free; Apache-2.0 licenseSetup effort: Adding 5 lines to a standard PyTorch training script runs it on single or distributed setupsBackends: Easy-to-configure FSDP and DeepSpeed supportLatest release: v1.15.0, published Sep 9, 2026

Hugging Face Accelerate says adding 5 lines to a standard PyTorch training script runs it on single or distributed setups, with easy-to-configure FSDP and DeepSpeed support. It is free under Apache-2.0; the latest release is v1.15.0, published Sep 9, 2026.

Go ifMoving an existing PyTorch loop to many GPUs with minimal edits
Skip ifYou need to design the parallelism layout yourself
Why #4 over #5: It needs far fewer code changes than Ray Train’s cluster setup.
5

Ray Train

Ray Train
Answrs VerdictThe best cluster layer for running other trainers.
Price: Free; Apache-2.0 licenseScale: Scales training code from a single machine to a cluster of machines in the cloudFrameworks: Supports PyTorch, TensorFlow, PyTorch Lightning, Keras, Hugging Face Transformers, Horovod, Hugging Face Accelerate, XGBoost, DeepSpeed and LightGBMLatest release: Ray 2.59.0, published Oct 2, 2026

Ray Train scales training code from a single machine to a cluster of machines in the cloud. It supports PyTorch, TensorFlow, PyTorch Lightning, Keras, Hugging Face Transformers, Horovod, Hugging Face Accelerate, XGBoost, DeepSpeed and LightGBM. It is free under Apache-2.0; Ray 2.59.0 was published Oct 2, 2026.

Go ifRunning other frameworks’ training jobs on a cluster you manage with Ray
Skip ifYou only train on one machine
Why #5 over #6: It runs other trainers across a cluster, while PyTorch Lightning focuses on the training loop itself.
6

PyTorch Lightning

PyTorch Lightning
Answrs VerdictThe cleanest trainer for removing boilerplate.
Price: Free; Apache-2.0 licenseScale claim: Pretrain and finetune AI models on 1 or 10,000+ GPUs with zero code changesStrategies: Lightning Fabric lets you pick ddp, fsdp or deepspeed strategiesGitHub stars: 31,418 (as of Oct 20, 2026)

PyTorch Lightning says it can pretrain and finetune AI models on 1 or 10,000+ GPUs with zero code changes. Lightning Fabric lets you pick ddp, fsdp or deepspeed strategies. It is free under Apache-2.0 and has 31,418 GitHub stars as of Oct 20, 2026.

Go ifResearchers who want less boilerplate and a switchable strategy
Skip ifYou prefer to write the whole loop yourself
Why #6 over #7: It is a mature, widely starred trainer, ahead of the newer NVIDIA NeMo Automodel.
7

NVIDIA NeMo Automodel

NVIDIA NeMo Automodel
Answrs VerdictThe current NeMo home for distributed LLM training.
Price: Free; Apache-2.0 licenseWhat it is: A PyTorch distributed-native training library for LLMs and VLMs with out-of-the-box Hugging Face supportParallelism: Its news items cite FSDP2, expert, pipeline and context parallelismNeMo today: The NeMo repository says it has pivoted to audio, speech and multimodal LLMs

NVIDIA NeMo Automodel is a PyTorch distributed-native training library for LLMs and VLMs with out-of-the-box Hugging Face support. Its news items cite FSDP2, expert, pipeline and context parallelism. The older NeMo repository says it has pivoted to audio, speech and multimodal LLMs, so Automodel is the NeMo project to pick here. It is free under Apache-2.0.

Go ifFine-tuning Hugging Face models with PyTorch-native parallelism on NVIDIA GPUs
Skip ifYou need a mature, widely adopted project
Why #7 over #8: It is under active development, while Colossal-AI’s last tagged release is older.
8

Colossal-AI

Colossal-AI
Answrs VerdictThe widest menu of parallelism strategies.
Price: Free; Apache-2.0 licenseParallelism: Data, pipeline, 1D, 2D, 2.5D and 3D tensor, and sequence parallelismGitHub stars: 41,431 (as of Oct 20, 2026)Latest release: v0.5.0, published Jun 4, 2025

Colossal-AI offers data, pipeline, 1D, 2D, 2.5D and 3D tensor, and sequence parallelism. It is free under Apache-2.0 and has 41,431 GitHub stars as of Oct 20, 2026, but its latest release, v0.5.0, was published Jun 4, 2025.

Go ifExperimenting with many parallelism strategies in one library
Skip ifYou need frequent tagged releases
Why #8 over #9: It offers more parallelism options than MosaicML Composer.
9

MosaicML Composer

MosaicML Composer
Answrs VerdictA solid FSDP trainer with a slower release pace.
Price: Free; Apache-2.0 licenseWhat it does: An open-source library on top of PyTorch that makes distributed training workflows on large-scale clusters easierCheckpoints: Elastic sharded checkpointing lets you save on eight GPUs and resume on sixteenLatest release: v0.32.1, published Jul 26, 2025

MosaicML Composer is an open-source library on top of PyTorch that makes distributed training workflows on large-scale clusters easier. Elastic sharded checkpointing lets you save on eight GPUs and resume on sixteen. It is free under Apache-2.0; the latest release is v0.32.1, published Jul 26, 2025.

Go ifFSDP training with elastic sharded checkpoints and streaming data
Skip ifYou want the most active release cadence
Why #9 over #10: It is still maintained for new projects, unlike Horovod.
10

Horovod

Horovod
Answrs VerdictHistoric and now archived; keep it for legacy jobs only.
Price: Free; Apache-2.0 licenseStatus: Its README says that due to inactivity the project was archived in September 2026Frameworks: Distributed training for TensorFlow, Keras, PyTorch and Apache MXNetGitHub stars: 14,678 (as of Oct 20, 2026)Latest release: v0.28.1, published Jun 12, 2023

Horovod is distributed training for TensorFlow, Keras, PyTorch and Apache MXNet. Its README says that due to inactivity the project was archived in September 2026, and its latest release, v0.28.1, was published Jun 12, 2023. It is free under Apache-2.0 and has 14,678 GitHub stars as of Oct 20, 2026.

Go ifMaintaining an existing Horovod job that already works
Skip ifYou are starting a new project

How we ranked these

We ranked the open-source frameworks on how much each does for training across many GPUs, using each project’s own docs and README, then on how recently each shipped a release. Horovod comes last because its README says it was archived in September 2026. NVIDIA’s NeMo entry is Automodel, because the main NeMo repository now says it has pivoted to speech. All 10 picks are free open-source code; photos are official diagrams and figures from each project’s docs or repository. GitHub star counts are as of Oct 20, 2026. Facts are taken from each maker's own published information; check the official site before you buy.

Questions people ask

What is the best distributed training framework?

PyTorch FSDP is our #1 because sharding parameters, gradients and optimizer states is built into PyTorch itself; DeepSpeed is #2 and Megatron-LM is #3.

Are these frameworks free?

Yes. Every pick is open-source code on GitHub, and most state an Apache-2.0 license.

Is Horovod still maintained?

No. Its README says the project was archived in September 2026 due to inactivity, so it is #10 and best kept for existing jobs.

What is the easiest way to train on multiple GPUs?

Hugging Face Accelerate: its README says adding 5 lines to a standard PyTorch training script runs it on single or distributed setups.

GitHub stars on each project’s main repository, as of Oct 20, 2026 (PyTorch FSDP lives inside the PyTorch repo, so it is left out) at a glance

NVIDIA NeMo Automodel1,028 stars
MosaicML Composer5,507 stars
Hugging Face Accelerate9,921 stars
Horovod14,678 stars
Megatron-LM18,205 stars
PyTorch Lightning31,418 stars
Colossal-AI41,431 stars
DeepSpeed43,245 stars
Ray Train44,086 stars
GitHub stars on each project’s main repository, as of Oct 20, 2026 (PyTorch FSDP lives inside the PyTorch repo, so it is left out), as published by each maker. Picks without a published figure are left off.

Official sites

Sources

Prices, specs and availability change; confirm on the official site before you buy.