unsloth vs verl
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
verl
Reinforcement learning training library for LLM post-training with PPO and GRPO
Verdict
unsloth ranks higher, 88 to 68. Of the 3 score parts we measure for both, unsloth leads on 1, verl on 1 and 1 is even.
- Adoptionunsloth leads by 59 points, 95 to 36: a wide gap.
- FreshnessEven at 100.
- MaintenanceWe do not score verl on this; unsloth has 97.
- Agent-readyverl leads by 70 points, 70 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 verl if you are
ML engineers running RL post-training of LLMs on GPU clusters
- Pluggable backends: FSDP/FSDP2/Megatron-LM for training, vLLM/SGLang/HF for rollout
- Many RL algorithms built in: PPO, GRPO, ReMax, RLOO, REINFORCE++
| What we compare | unsloth | verl |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 95, widely used | 36, known |
| Freshness | 100, active | 100, active |
| Maintenance | 97, healthy | Not scored for this kind |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 0, none | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 78k | 23.8k |
| 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 | Required |
| 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
verl
verl is the open-source implementation of the HybridFlow paper from ByteDance Seed. It runs RL and supervised fine-tuning for LLMs using FSDP, FSDP2 or Megatron-LM for training and vLLM, SGLang or HF Transformers for rollout generation. It covers PPO, GRPO, RLOO, ReMax and others, with multi-turn tool calling, VLM support and scaling to 671B models.
Who it is for: ML engineers running RL post-training of LLMs on GPU clusters
Strengths
- Pluggable backends: FSDP/FSDP2/Megatron-LM for training, vLLM/SGLang/HF for rollout
- Many RL algorithms built in: PPO, GRPO, ReMax, RLOO, REINFORCE++
- Scales to 671B models and hundreds of GPUs via expert parallelism
- Supports NVIDIA, AMD and Ascend hardware; LoRA RL to save memory
Weaknesses
- Needs multi-GPU clusters in practice; minimum hardware not stated in README
- Many algorithm recipes live in a separate verl-recipe repository and submodule
- Version-sensitive: README warns against vLLM 0.7.x and requires >=0.8.2
- Several features remain under verl/experimental until merged into the main library
- GPU required
- Docker
- Needs Ray, PyTorch, vLLM, SGLang, Megatron-LM
- Models: Qwen3.5, Qwen3, Qwen2.5, Qwen2.5-VL, Kimi-VL
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