llm.c ported to Mojo, runs 1.72x faster than PyTorch on Metal

The developer took llm.c, a minimalist C-based LLM inference framework designed for simplicity and efficiency, and reimplemented it in Mojo with custom Metal GPU kernels. The Mojo version achieved 1.72x faster performance compared to PyTorch implementations running on Metal hardware. The code is available on GitHub under the repository ulmentflam/llm.mojo for public use and further development. This effort highlights Mojo's potential to accelerate AI workloads on Apple's ecosystem, where Metal is the native GPU programming interface. For developers working with LLMs on macOS, iPad, or other Apple platforms, this result suggests Mojo could provide an alternative path to better performance without moving away from higher-level languages entirely.

This port represents a practical test of Mojo's claim to deliver high-performance AI code. Mojo is a newer programming language designed to bridge the gap between Python's ease of use and low-level performance optimization. By porting llm.c—a well-known lightweight LLM inference framework—to Mojo and pairing it with Metal kernels (Apple's GPU programming framework), the developer showed a concrete 1.72x speedup over PyTorch on the same hardware. This matters because PyTorch is the industry standard for ML development, and a substantial performance gap suggests Mojo could offer a meaningful alternative for developers targeting Apple Silicon devices, particularly in latency-sensitive applications like on-device inference.

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