Llama vs Unsloth
A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.
Llama Fine-tuning | Unsloth Fine-tuning | |
|---|---|---|
| Tagline | Meta's open-weight LLM family covering 1B mobile models up to 405B frontier and natively multimodal 10M-context Llama 4 variants. | Open-source LLM fine-tuning toolkit with custom kernels that train 2-30x faster and use up to 90% less VRAM. |
| Category | Fine-tuning | Fine-tuning |
| Pricing | Freemium· Basic: $15 · Pro: $30 · Enterprise: $100 | Freemium· Free open-source; Pro and Enterprise contact sales |
| Model | Llama 4 (Maverick, Scout), Llama 3.3/3.2/3.1 | Llama, Mistral, Gemma, Qwen, GLM (multi-model) |
| Editorial score | 8.3 / 10 | 8.2 / 10 |
| Use cases | self-hosted-llmfine-tuningmultimodal-chatsynthetic-dataedge-inferencerag-backbone | lora-finetuningqloralocal-trainingdpo-orpomodel-quantizationgguf-export |
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| Website | www.llama.com | unsloth.ai |
Pick Llama if
- ✅ Open weights from 1B edge models to 405B frontier with permissive commercial license
- ✅ Natively multimodal Llama 4 with up to 10M-token context
- ✅ Runs anywhere: Ollama, vLLM, llama.cpp, Bedrock, Groq, Together
- ✅ Aggressive inference pricing on partner clouds (~$0.19-$0.49/M tokens)
Pick Unsloth if
- ✅ Real, measurable 2-5x speedups and big VRAM savings on consumer GPUs
- ✅ Open-source core with permissive license and active GitHub
- ✅ Drop-in compatible with Hugging Face TRL, PEFT and transformers
- ✅ Excellent ready-to-run Colab notebooks for most popular models