Lamini vs Tinker
A side-by-side comparison of Lamini and Tinker, two Fine-tuning tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Lamini | Tinker |
|---|---|---|
| Category | Fine-tuning | Fine-tuning |
| Pricing | PAID | PAID |
| License | Proprietary | Proprietary |
| Deployment (differs) | Hybrid | Cloud |
| Platforms (differs) | Web, API | API |
| Model support | Multi-model | Multi-model |
| Vendor (differs) | Lamini | Thinking Machines Lab |
The honest brief
Lamini
One of the few tuning platforms that runs on AMD GPUs and fully inside your own VPC or on-prem, keeping regulated data off third-party clouds.
- Keeps models and data fully in-house
- Supports AMD GPUs, not just NVIDIA
- Memory tuning to cut hallucinations
- Founded by an MLPerf and ex-NVIDIA team
- Enterprise-focused pricing
- Open models only
- Smaller ecosystem than hyperscalers
Tinker
Hands you the training loop itself — forward_backward and sample primitives — rather than a one-click fine-tune form, with distributed GPUs managed for you.
- Exposes forward_backward, optim_step, sample
- Large MoE models supported
- Downloadable trained weights
- No GPU infra to manage
- LoRA-based only (no full fine-tune)
- Usage-based, no free tier
- Python SDK only