shivampkumar got Microsoft’s 4B-parameter TRELLIS.2 running on Apple Silicon, and an M4 Pro with 24GB reportedly produces a roughly 400K-vertex mesh in about 3.5 minutes. My read: this is less a fun “Mac can do 3D” demo and more a clean proof that a lot of CUDA lock-in is engineering debt, not absolute model necessity.
The article is thin. We only have an RSS-style snippet, not a full writeup with memory traces, failure cases, quality comparisons, or side-by-side outputs against the original implementation. So I can’t say whether the port preserves the original TRELLIS.2 quality on geometry fidelity, topology stability, or texture consistency. The title establishes that it runs on Mac. The body does not disclose whether output quality is near parity with the CUDA path. That gap matters.
What makes this interesting is the substitution pattern. flash_attn was replaced with SDPA. nvdiffrast and CUDA hashmap-heavy mesh extraction were replaced with a Python path. Custom sparse convolution kernels were replaced with pure PyTorch gather-scatter sparse 3D convolution. That tells you something broader: many “requires Nvidia” claims in open-source multimodal projects are partly shorthand for “nobody has paid the rewrite cost yet.” Once someone absorbs that cost, a project often moves from impossible to merely slower. For researchers, indie builders, and local-first toolchains, that distinction is huge.
I’ve always thought Apple Silicon gets framed too dramatically in AI. One camp says Macs are irrelevant for serious work. The other says local inference is about to erase cloud dependence. I don’t buy either. This result sits in the middle. Three and a half minutes is slow for a consumer-facing interactive product, and “seconds on H100” is obviously a different class of throughput. But for offline asset generation, prototyping, classroom use, or workstation-side experimentation, it has crossed the threshold from novelty to usable. A lot of 3D workflows already tolerate minute-scale steps.
There’s useful precedent here. Over the last year, a bunch of open-source projects became viable on Apple Silicon not by perfectly reproducing CUDA kernels, but by accepting performance loss in exchange for installability, reproducibility, and offline use. Stable Diffusion workflows, Whisper variants, and local Llama stacks all followed that path. First you get “it runs.” Then someone else adds quantization, fused ops, graph lowering, and backend-specific optimizations. Without the first step, the optimization cycle never starts. TRELLIS.2 feels like 3D generation entering that same phase.
I do have pushback on the framing. First, “H100 takes seconds” is too vague to carry much weight. Seconds under what resolution, what batch size, what preprocessing, and what exact TRELLIS.2 config? None of that is disclosed. Second, Python-based mesh extraction often hides ugly tail latency on harder examples; we don’t know the long-tail behavior here. Third, sparse 3D conv in pure PyTorch on MPS may work for this demo, but the body says nothing about scaling to multi-view inputs, larger scenes, or production-grade throughput. My guess is the first walls will be memory pressure and operator inefficiency, but that’s my inference, not something the snippet proves.
So I’d file this as a meaningful ecosystem signal, not a performance story. Apple Silicon is nowhere near replacing Nvidia for serious 3D model serving or high-throughput research loops. But the old assumption that no Nvidia means no meaningful 3D generation work just lost another chunk of credibility. There’s also a lesson for model publishers like Microsoft: once a model is out, the community will do the platform adaptation work you didn’t prioritize. Nvidia’s moat is still real. The mythology that every useful 3D stack must remain CUDA-only looks less solid every month.