Unsloth
Open-source library that makes LLM fine-tuning and reinforcement learning about 2x faster with far less VRAM.
Overview
Unsloth is an open-source (Apache-2.0) library for fine-tuning and reinforcement learning on LLMs, rewriting attention and training kernels to run roughly 2x faster while using around 70-80% less VRAM with no loss in accuracy. Founded in 2023 by brothers Daniel and Michael Han (San Francisco, Y Combinator), it has over 65K GitHub stars and tens of millions of Hugging Face model downloads, and it distributes ready-to-use quantized models. The core library is free, with paid tiers for higher-performance and enterprise fine-tuning.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 24 Jul 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
ASEAN Perspective
Unsloth in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
What this is for: Fine-tuning and running RL on open LLMs faster and on cheaper hardware by cutting memory use.
Who this is for: Developers and researchers who want to customize open models without large GPU budgets.
Availability: Free open-source library (Apache-2.0); paid tiers for higher-throughput and enterprise training.
What people say
Unsloth is a bona fide open-source hit: 65K+ GitHub stars, tens of millions of Hugging Face downloads, and Y Combinator backing. The core value proposition — roughly 2-5x faster fine-tuning with ~70-80% less VRAM by rewriting attention and training kernels — is widely corroborated by practitioners, and its ready-to-use quantized model uploads are a genuine convenience for the local-LLM crowd.
The dominant and well-documented limitation is multi-GPU. Historically the open-source library was single-GPU only, and GitHub issues are full of users finding that only one GPU gets utilized despite having several. Documentation now advertises multi-GPU via Accelerate/DeepSpeed (DDP/FSDP), but in practice reviewers report manual sharding, launcher-specific flags, and fiddly FSDP config — so it's far from drop-in. Head-to-head guides (Axolotl vs Unsloth vs TorchTune) conclude Unsloth is the clear winner for single-GPU efficiency, while Axolotl with FSDP2 or DeepSpeed is faster once you need to parallelize across GPUs., and some of the smoothest multi-GPU paths sit behind paid tiers.
Honest read: superb for cost-conscious single-GPU fine-tuning; if your workload is inherently distributed, it's not the tool that shines.
Summary of public user & expert reviews, compiled by RECATOOLS.
About this listing
This entry was compiled from publicly available data including Unsloth's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with Unsloth unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
For the latest details, please refer to Unsloth directly →
Spotted something out of date? Suggest an update →
Alternatives to Unsloth
More in Code & Dev Tools
