DeepSpeed

DeepSpeed is a deep learning optimization library that enables efficient distributed training and inference for large-scale models.

DeepSpeed is an optimization library designed for large-scale deep learning training and inference. It provides system innovations to manage the complexities of distributed computing, enabling the training of models with billions of parameters. Key technologies include ZeRO, 3D-Parallelism, DeepSpeed-MoE, and ZeRO-Infinity to address memory and compute constraints. The library helps users train complex language models such as BLOOM and Megatron-Turing NLG. DeepSpeed operates as an integral component of the Microsoft AI at Scale initiative.

DeepSpeed at a glance

Best for: Software engineer, Researcher
Runs on: open source
Pricing: Free
Released: January 2020
Category: AI coding tool
Website: deepspeed.ai

Key features

ZeRO-based memory optimization
3D-Parallelism support
DeepSpeed-MoE for mixture-of-experts training
ZeRO-Infinity offloading engine
Communication efficient training with 1-bit Adam
Post-training quantization via ZeroQuant
Reinforcement learning from human feedback (RLHF) support

Use it for

Training large-scale language models
Distributed model inference
RLHF training for chatbot models
Optimizing transformer-based model performance

Alternatives to DeepSpeed

TokenSpeed — Optimized performance for agentic workloads and high-throughput LLM serving.
DeepEP Ascend — Optimizes MoE training and inference on Huawei Ascend hardware with low overhead.
AirLLM — Runs massive 70B+ parameter models on small 4GB consumer GPUs.
XTuner — High-efficiency training for 200B+ scale MoE models without expert parallelism.
Dsv41 Flash Offload — Runs a large DeepSeek model locally using consumer hardware and offloaded host RAM.
SGLang — It delivers high-throughput, low-latency serving for large-scale deployments.
LMDeploy — Accelerates inference with high-performance engines and quantization tools.
CoralBricks — Fast inference up to 469 output tokens per second with zero cost for cached input.
NVIDIA Model Optimizer — Compress deep learning models to accelerate inference performance.
PEFT — Reduces computational requirements and storage needs for large model training.
Strata — Runs large AI models locally on consumer gaming PCs without server reliance.
llm-d — Deploys production-grade LLM serving on existing hardware clusters.

All DeepSpeed alternatives