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Open Source Alternatives

Open source alternatives to DeepSpeed

Open source alternatives to DeepSpeed, ranked by GitHub stars and freshness.

12 open-source alternatives in the index, ranked by GitHub stars and freshness.

O OSS Framework medium

Colossal-AI

Community

Making large AI models cheaper, faster and more accessible

Alternative to Deepspeed, Megatron Lm

★ 41,382 updated 3mo ago
open-source Apache-2.0 Python

Best for: Teams training large models who have access to multiple GPUs and need to optimize resource efficiency

O OSS Framework medium

NeMo Framework

Community

A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech

Alternative to Megatron Lm, Deepspeed

★ 17,285 updated 3mo ago
open-source Apache-2.0 Python

Best for: Researchers and ML engineers building custom LLMs, speech systems, or multimodal models who need low-level control and scalability.

O OSS Framework medium

Megatron-LM

Community

Ongoing research training transformer models at scale

Alternative to Deepspeed, Colossal Ai

★ 16,545 updated 3mo ago
open-source Python

Best for: ML engineers training large transformer models who need production-grade distributed training infrastructure

O OSS Obs medium

Horovod

Community

Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.

Alternative to Deepspeed

★ 14,696 updated 9mo ago
open-source Python

Best for: ML engineers training large models who need to scale across multiple GPUs or nodes without rewriting training logic

O OSS Framework medium

Liger-Kernel

Community

Efficient Triton Kernels for LLM Training

Alternative to Deepspeed, Colossal Ai, Unslothai

★ 6,400 updated 3mo ago
open-source BSD-2-Clause Python

Best for: Developers training large language models who want to optimize throughput and memory without rewriting their training loops.

O OSS Framework medium

torchtitan

Community

A PyTorch native platform for training generative AI models

Alternative to Deepspeed, Megatron Lm

★ 5,394 updated 3mo ago
open-source BSD-3-Clause Python

Best for: Teams using PyTorch to train custom generative AI models at scale

O OSS Framework medium

nanotron

Community

Minimalistic large language model 3D-parallelism training

Alternative to Deepspeed, Colossal Ai, Megatron Lm

★ 2,705 updated 3mo ago
open-source Apache-2.0 Python

Best for: Researchers and engineers who need a simple, hackable framework for distributed LLM training experiments.

O OSS Framework medium

maxtext

Community

A simple, performant and scalable Jax LLM!

Alternative to Megatron Lm, Deepspeed, Colossal Ai

★ 2,303 updated 3mo ago
open-source Apache-2.0 Python

Best for: Developers already using Jax who need a streamlined, scalable LLM training framework

O OSS Framework medium

Mesh Tensorflow

Community

Mesh TensorFlow: Model Parallelism Made Easier

Alternative to Deepspeed, Megatron Lm, Colossal Ai

★ 1,625 updated 2y ago
open-source Apache-2.0 Python

Best for: Developers training large transformer models on TPU or GPU clusters

O OSS Framework medium

MInference

Community

[NeurIPS'24 Spotlight, ICLR'25, ICML'25] To speed up Long-context LLMs' inference, approximate and dynamic sparse calculate the attention, which reduces inference latency by up to

Alternative to Vllm, Sglang, Tensorrt Llm +1 more

★ 1,217 updated 5mo ago
open-source MIT Python

Best for: Developers optimizing long-context LLM inference on NVIDIA GPUs

O OSS Framework medium

BMTrain

Community

Efficient Training (including pre-training and fine-tuning) for Big Models

Alternative to Deepspeed, Colossal Ai, Megatron Lm

★ 624 updated 4mo ago
open-source Apache-2.0 Python

Best for: Developers and researchers training or fine-tuning large models who need a specialized, efficiency-focused Python framework

O OSS Obs medium

OneComp

Community

Python package for LLM compression

Alternative to Deepspeed, Colossal Ai

★ 379 updated 3mo ago
open-source MIT Python

Best for: Developers needing to shrink LLMs for deployment on limited hardware