What is Unsloth?
Unsloth is a free, open-source desktop app that lets users run, customize, and train AI models on their own hardware with optimizations for speed and memory usage. It supports multiple GPU setups and offers paid Pro and Enterprise tiers with enhanced performance and deployment capabilities.
Unsloth pricing
Plans, per-tier features and add-ons, dated and linked to live pricing. Pricing changes often; always verify at source before you rely on it.
Free open-source tier; Pro and Enterprise plans available
Free
FreeOpen-source version with core model training capabilities
- Supports Mistral, Gemma, Llama 1/2/3 models
- 4-bit and 16-bit LoRA fine-tuning
- Single GPU support
- 2x speed improvement
- 60% VRAM reduction
- Fully local and offline operation
Pro
Contact salesEnhanced performance with multi-GPU support
- 2.5x faster training
- 20% less VRAM usage
- Multi-GPU support up to 8 GPUs
- 80% VRAM reduction
- Support for any use case
Enterprise
Contact salesMaximum performance with multi-node deployment and accuracy improvements
- 30x faster training
- Up to 30% accuracy improvement
- 5x faster inference
- Multi-node support
- 90% VRAM reduction
- Full training support
- Customer support included
What Unsloth does
The capabilities that matter for fine tuning platforms, normalised so it lines up with every alternative. “-” means we haven't confirmed it, not that it's missing.
- Architecture model
- Open source library local GPU
- Peft lora and qlora parameter efficient tuning
- ✓
- Rlhf and dpo preference alignment optimization
- ✓
- Serverless hosting of fine tuned adapters lorax
- -
- Four bit and eight bit memory quantization
- ✓
- Flash attention and xformers compilation
- ✓
- Distributed multi GPU orchestration fsdp deepspeed
- ✓
- Synthetic data generation and evaluation pipeline
- -
- Native huggingface hub push pull integration
- ✓
- Weights and biases wandb experiment tracking
- -
- SOC2 type ii
- -
- Mit or apache permissive oss license
- ✓
- Pricing model
- Free open source
Platform & deployment
Independently observed- macOS
- Web
- Linux
- Windows
- Cloud / SaaS
- Self-hosted
Integrations (18)
Independently observed- Claude Code
- Codex
- OpenAI API
- Hugging Face
- Model Context Protocol
- Cloudflare
- FLUX
- MiniMax-H3
- Stable Diffusion
- Qwen3.8
- DeepSeek-V4
- Gemma 2
- Gemma 4
- Mistral
- Llama 1
- Llama 2
- Llama 3
Security & compliance
Known vulnerabilities: 0 (0 in the last 12 months) sourcea count reflects scale & disclosure, not quality
Unsloth alternatives
Other fine tuning platforms we track, ranked by the same independent score.
Compare Unsloth
Side by side against other fine tuning platforms, attribute by attribute, with a source on every value.
The Vioscale score: one lens on the evidence
Not user reviews and not a paid placement: a confidence-weighted blend of the independent signals below (adoption, activity, security posture, and more), which you can sort and re-weight yourself. Vendors can correct their listing but can never move their rank, and stars are weighted low as a vanity metric. It is one way to read the evidence for Unsloth, not the verdict.
| Signal | Score | Weight | Contribution | Evidence |
|---|---|---|---|---|
| Capabilities | 87 | 0.08 | 7.3 | ✓ |
| Development activity | 63 | 0.09 | 5.9 | ✓ |
| Release cadence | 98 | 0.05 | 5.1 | ✓ |
| Price level | 80 | 0.05 | 4.2 | ✓ |
| Integrations | 37 | 0.09 | 3.4 | ✓ |
| Stars | 92 | 0.03 | 2.4 | ✓ |
| Security score | 49 | 0.04 | 2.1 | ✓ |
| Pricing transparency | 25 | 0.08 | 2.1 | ✓ |
| Reliability | 0 | 0.07 | 0.0 | - |
| Dependent projects | 0 | 0.06 | 0.0 | ✓ |
| Security posture | 0 | 0.07 | 0.0 | - |
| Package downloads | 0 | 0.14 | 0.0 | - |
| Developer Q&A activity | 0 | 0.06 | 0.0 | - |
Computed . Re-weight it by intent, or see the full method.
All data & sourcesshow ↓
Every value we hold, with its source, retrieval date, and confidence. This is the evidence behind the score: don't trust it, verify it.
Activity
| Attribute | Value | Evidence |
|---|---|---|
| Commits last 30d | 100 | mediumsource · 2026-08-26 · 65% |
Adoption
Features
| Attribute | Value | Evidence |
|---|---|---|
| Capabilities | Pricing model: free_open_source · Architecture model: open_source_library_local_gpu · Mit or apache permissive oss license: Yes · Flash attention and xformers compilation: Yes · Four bit and eight bit memory quantization: Yes · Native huggingface hub push pull integration: Yes | mediumsource · 2026-08-21 · 60% |
Integrations
| Attribute | Value | Evidence |
|---|---|---|
| Count | 18 | mediumsource · 2026-08-21 · 60% |
Language
| Attribute | Value | Evidence |
|---|---|---|
| Primary | Python | highsource · 2026-08-26 · 90% |
License
| Attribute | Value | Evidence |
|---|---|---|
| Spdx | Apache-2.0 | highsource · 2026-08-26 · 95% |