u/TgoAI

A few people asked how VPipe compares with h3.c, so I ran them side by side on the same machine with the same settings.

Machine: base 15” M5 MacBook Air, 16GB RAM

MiniMax H3 settings:

* 960×544

* 124 frames

* 6 DiT steps

Results:

* VPipe: 12m 15s

* h3.c: 16m 22s

So on this particular matched workload, VPipe finished in about 25% less wall-clock time.

The video shows both the generation process and the final outputs side by side, so you can also compare the resulting quality rather than just the timing.

VPipe is not an MinimaxH3-specific implementation — it’s an Apache-2.0 open-source multimodal pipeline/runtime with a native Metal inference backend for Apple Silicon. MiniMax H3 is just one of the workloads I’ve been optimizing recently.

GitHub: https://github.com/tgo-app-dev/vpipe

Interested in feedback on both the performance comparison and the output differences.

I’m building VPIPE, an open-source C++/Metal runtime for running AI locally on Macs.

It supports LLMs/VLMs, image & video generation, ASR, quantization, and multimodal pipelines — without depending on PyTorch/MLX for model execution, the speed is top tier.

The whole runtime package is only ~25MB.

A recent milestone: VPIPE can now run MiniMax H3 video generation on a 16GB base M5 MacBook Air (and M4 Macs too).

The goal is to make it easier to build local/private AI products without relying on cloud GPUs or a heavy Python stack.

Would love feedback from other builders.

https://github.com/tgo-app-dev/vpipe