llamafactory vs deepspeed
Two of the top projects in Training and fine-tuning, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
llamafactory
Fine-tuning toolkit for 100+ LLMs with a CLI and Gradio web UI
deepspeed
PyTorch library for distributed training of large models with ZeRO
Verdict
llamafactory ranks higher, 74 to 72. Of the 4 score parts we measure for both, llamafactory leads on 2, deepspeed on 1 and 1 is even.
- Adoptionllamafactory leads by 48 points, 92 to 44: a wide gap.
- FreshnessEven at 100.
- Maintenancedeepspeed leads by 43 points, 81 to 38: a wide gap.
- Agent-readyllamafactory leads by 10 points, 55 to 45.
Pick llamafactory if you are
Engineers fine-tuning open LLMs on their own GPUs, with or without code
- Broad method coverage: SFT, PPO, DPO, KTO, ORPO, LoRA, QLoRA via AQLM/AWQ/GPTQ/HQQ/EETQ
- Supports GaLore, BAdam, APOLLO, Muon, DoRA, PiSSA, plus Unsloth and Liger Kernel speedups
Pick deepspeed if you are
ML engineers training or fine-tuning large models on multi-GPU clusters
- Used to train models such as BLOOM 176B and Megatron-Turing NLG 530B
- Integrates with Hugging Face Transformers/Accelerate, Lightning, Composer, Determined and MMEngine
| What we compare | llamafactory | deepspeed |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 92, widely used | 44, known |
| Freshness | 100, active | 100, active |
| Maintenance | 38, patchy | 81, healthy |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 55, partly | 45, minimal |
| Facts from GitHub and the README | ||
| Stars | 75.5k | 43.2k |
| License | Apache-2.0 (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | May 2026 | Sep 2026 |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Required | Optional |
| arm64 or Apple Silicon | Mentioned | Not stated |
llamafactory
LlamaFactory fine-tunes language and multimodal models through YAML-configured CLI runs or the LLaMA Board Gradio UI. It covers pre-training, SFT, reward modeling, PPO, DPO, KTO and ORPO, with full-tuning, freeze-tuning, LoRA and 2 to 8-bit QLoRA. Trained models can be served through an OpenAI-style API using vLLM or SGLang workers.
Who it is for: Engineers fine-tuning open LLMs on their own GPUs, with or without code
Strengths
- Broad method coverage: SFT, PPO, DPO, KTO, ORPO, LoRA, QLoRA via AQLM/AWQ/GPTQ/HQQ/EETQ
- Supports GaLore, BAdam, APOLLO, Muon, DoRA, PiSSA, plus Unsloth and Liger Kernel speedups
- Covers multimodal and audio models such as Qwen3-VL, LLaVA and Qwen2-Audio
- Runs on NVIDIA, AMD ROCm and Ascend NPU, with hosted Colab and PAI-DSW notebooks
Weaknesses
- Official documentation is marked WIP in the README
- No VRAM or RAM figures in the visible README; hardware requirements unknown here
- Web UI is for training and chat, not a production serving layer
- Many optional backends (vLLM, SGLang, Unsloth, KTransformers) add install complexity
- GPU required
- Docker + Compose
- Needs PyTorch, Hugging Face Transformers, Gradio, vLLM (optional), SGLang (optional)
- Models: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen3
deepspeed
DeepSpeed is a PyTorch optimization library for training large deep learning models across many GPUs. It provides ZeRO memory partitioning with CPU and NVMe offload, 3D parallelism, Ulysses sequence parallelism and MoE support, configured through a JSON file. It integrates with Hugging Face Transformers and Accelerate, Lightning, MosaicML Composer, Determined and MMEngine.
Who it is for: ML engineers training or fine-tuning large models on multi-GPU clusters
Strengths
- Used to train models such as BLOOM 176B and Megatron-Turing NLG 530B
- Integrates with Hugging Face Transformers/Accelerate, Lightning, Composer, Determined and MMEngine
- Supports NVIDIA, AMD, Intel Gaudi/XPU/CPU, Huawei Ascend NPU and Tecorigin accelerators
- Installs from PyPI; C++/CUDA ops are JIT-compiled or pre-built
Weaknesses
- Needs PyTorch installed first and a CUDA or ROCm compiler for JIT ops
- Mainly tested on NVIDIA Pascal to Hopper and AMD MI100/MI200
- Windows lacks async IO and GDS support
- Large JSON config surface; ZeRO and offload tuning requires reading the docs
- GPU optional
- Docker
- Needs PyTorch, CUDA or ROCm compiler (nvcc/hipcc), ninja
- Models: PyTorch models, Hugging Face Transformers
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