DeepSpeed
Microsoft library for extreme-scale model training: ZeRO optimizer states, pipeline parallelism, and inference kernels.
Why it is included
Core OSS component behind many large-model training recipes complementing PyTorch.
Best for
Teams training or fine-tuning large models where memory fragmentation is the bottleneck.
Strengths
- ZeRO stages
- Large-model recipes
- Inference optimizations
Limitations
- Primarily NVIDIA CUDA ecosystems; integration testing burden
Good alternatives
FSDP · Megatron-LM · Horovod
Related tools
AI & Machine Learning
PyTorch
Deep learning framework with strong research-to-production paths.
AI & Machine Learning
Ray
Distributed compute framework for Python: scale data loading, training, hyperparameter search, and online serving (Ray Serve).
AI & Machine Learning
Accelerate
Hugging Face library to run PyTorch training on CPU, single GPU, multi-GPU, or TPU with minimal code changes.
AI & Machine Learning
GPT-NeoX
EleutherAI framework and 20B-class models for training large autoregressive LMs with 3D parallelism—Apache-2.0 training stack.
AI & Machine Learning
Axolotl
YAML-configured fine-tuning for LLMs: LoRA, QLoRA, FSDP, and many architectures on top of Hugging Face trainers.
AI & Machine Learning
Unsloth
Optimized fine-tuning library claiming 2× faster LoRA/QLoRA with less VRAM via custom kernels and Hugging Face compatibility.