unsloth 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.
unsloth
Desktop and web app to run, fine-tune and export open models
deepspeed
PyTorch library for distributed training of large models with ZeRO
Verdict
unsloth ranks higher, 87 to 74. Of the 4 score parts we measure for both, unsloth leads on 2, deepspeed on 1 and 1 is even.
- Adoptionunsloth leads by 44 points, 93 to 49: a wide gap.
- FreshnessEven at 100.
- Maintenanceunsloth leads by 16 points, 97 to 81: a wide gap.
- Agent-readydeepspeed leads by 45 points, 45 to 0: a wide gap.
Pick unsloth if you are
Engineers fine-tuning and locally running open LLMs on their own GPUs
- Fine-tuning covers LoRA, QLoRA, full fine-tuning, GRPO, DPO and FP8 pretraining
- Claims 2x faster training with up to 70% less VRAM, per README
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 | unsloth | deepspeed |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 93, widely used | 49, known |
| Freshness | 100, active | 100, active |
| Maintenance | 97, healthy | 81, healthy |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 0, none | 45, minimal |
| Facts from GitHub and the README | ||
| Stars | 78k | 43.2k |
| License | Apache-2.0 (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Sep 2026 |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Mentioned | Not stated |
unsloth
Unsloth ships as a desktop app, a web UI (Studio) and a Python library for running and fine-tuning models. It runs GGUF and other open models locally with an OpenAI-compatible API, and trains with LoRA, QLoRA, full fine-tuning and RL methods such as GRPO and DPO. The README claims 2x faster training with up to 70% less VRAM, and exports to GGUF, FP8 and NVFP4.
Who it is for: Engineers fine-tuning and locally running open LLMs on their own GPUs
Strengths
- Fine-tuning covers LoRA, QLoRA, full fine-tuning, GRPO, DPO and FP8 pretraining
- Claims 2x faster training with up to 70% less VRAM, per README
- Exports to GGUF, FP8 and NVFP4 and serves an OpenAI-compatible API
- Runs on NVIDIA, AMD, Intel, CPU and Vulkan across Windows, macOS and Linux
Weaknesses
- Scope is broad: desktop app, Studio web UI and Core library each install differently
- Server-side tools are on by default; exposing it needs --disable-tools or a strong password
- Install relies on curl or irm piped to shell scripts
- Speed and VRAM figures are the project's own claims; hardware requirements are not listed
- GPU optional
- Docker
- Needs PyTorch, llama.cpp, audio.cpp, Cloudflare (optional remote access)
- Models: GGUF, MLX, LoRA, QLoRA, diffusion
- port 8000
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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