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# Comparison to alternatives

MaxText is similar to [Nvidia/Megatron-LM](https://github.com/NVIDIA/Megatron-LM), a very well tuned LLM implementation targeting Nvidia GPUs. The two implementations achieve comparable MFUs. The difference in the codebases highlights the different programming strategies. MaxText is pure Python, relying heavily on the XLA compiler to achieve high performance. By contrast, Megatron-LM is a mix of Python and CUDA, relying on well-optimized CUDA kernels to achieve high performance.

MaxText is heavily inspired by [MinGPT](https://github.com/karpathy/minGPT)/[NanoGPT](https://github.com/karpathy/nanoGPT), elegant standalone GPT implementations written in PyTorch and targeting Nvidia GPUs. MaxText is more complex, supporting more industry standard models and scaling to tens of thousands of chips. Ultimately MaxText has an MFU more than three times the [17%](https://twitter.com/karpathy/status/1613250489097027584?cxt=HHwWgIDUhbixteMsAAAA) reported most recently with that codebase, is massively scalable and implements a key-value cache for efficient auto-regressive decoding.