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MicroGPT in pure C achieves 10M tokens per second on Apple M5 with NEON
A dependency-free C implementation of a character-level transformer, microGPT-C, reaches 10M tokens per second on Apple M5 hardware using NEON acceleration
This demonstrates that small, self-contained transformer models can achieve high throughput on consumer hardware without external dependencies. For engineers, it highlights the potential for lightweight, portable AI inference in resource-constrained environments. The performance gap between ARM and x86 backends may influence hardware selection for similar workloads
Written by elseif from the cluster below · every claim links back to a sourceThe three things worth knowing
MicroGPT-C is a single-file C implementation of a GPT model with no dependencies beyond libc
It achieves 10M tokens per second on Apple M5 using NEON, outperforming an AMD Ryzen 5 5600H with AVX2 by nearly 50%
The model generalizes with only 4192 parameters, scoring similarly on seen and unseen data
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What the cluster adds up to.
MicroGPT-C shows that transformer inference can be implemented in minimal C with no external libraries. The entire model fits in one file, including forward pass, backpropagation, Adam optimizer, and sampling. This approach eliminates dependency management and simplifies deployment to diverse platforms. The tradeoff is that engineers must implement all components manually, including hardware-specific optimizations like NEON and AVX2
The 10M tokens per second performance on Apple M5 with NEON acceleration demonstrates that consumer ARM hardware can outperform x86 in this workload. The AMD Ryzen 5 5600H achieves only 6.9M tokens per second with AVX2, suggesting that NEON may be more efficient for this specific implementation. Engineers should note that these results are for a tiny model and may not scale linearly to larger transformers
The model's 4192 parameters achieve generalization rather than memorization, scoring similarly on training and validation data. This suggests that even extremely small transformers can learn useful patterns. The specialized inference path, gpt_forward_infer, matches the training forward pass to within floating-point rounding, indicating that the optimization doesn't compromise accuracy. This approach could be valuable for edge devices where memory and compute are limited
The implementation's portability across macOS, Linux, and Windows with MSYS2 shows that pure C can target diverse platforms without modification. The Makefile automatically selects appropriate flags for the host architecture. However, engineers should note that performance will vary significantly between backends, as shown by the nearly 50% difference between Apple M5 and AMD Ryzen 5 5600H results
Written by elseif from the cluster below · checked for specifics the sources never containedTHE CLUSTER