**TripoSplat** is an open-source feed-forward model from **VAST-AI Research** (the lab behind TripoSR and TripoSG, and the team operating Tripo3D) that converts a **single 2D image into 3D Gaussians** in one forward pass. There is no per-scene optimization loop: the image goes in, a set of Gaussians comes out, and the result exports as `.ply` or `.splat` for any standard 3D Gaussian viewer such as SparkJS or SuperSplat. The distinguishing design choice is the **variable Gaussian count**. Most single-image-to-3D-Gaussian models emit a fixed budget of primitives, which forces the same rendering cost onto a simple prop and a detailed character alike. TripoSplat lets the caller request an arbitrary number **up to 262,144**, making the quality-versus-rendering-cost tradeoff an explicit runtime parameter rather than a property baked into the checkpoint. A game asset destined for a mobile target and a hero asset for a cinematic can come from the same model at different budgets. The engineering posture is unusually restrained for a research release. The core is **two files — `triposplat.py` and `model.py` — totaling roughly 2,000 lines**, and the runtime dependencies are `numpy`, `safetensors`, `pillow`, and `tqdm` on top of PyTorch. There is no `transformers` and no `diffusers` in the path, which sidesteps the version-pinning conflicts that make many 3D generation repos difficult to install alongside anything else. That compactness is a deliberate integration argument: the code is small enough to read end to end and graft into an existing pipeline. Distribution is broad for a project of this size. Weights are published on **Hugging Face** (`VAST-AI/TripoSplat`) and mirrored on **ModelScope**, there is a Gradio demo (`run_gradio.py`) and a hosted Hugging Face Space, and **ComfyUI ships an official workflow template** for image-to-Gaussian-splat, so ComfyUI users can run it without writing glue code. The accompanying paper is on arXiv, with a technical blog on the Tripo3D site. Two caveats are worth stating. The repository has seen **no commits since early June 2026**, so this is a stable drop rather than an actively iterated project — fine for a self-contained inference model, less reassuring if issues accumulate. And the output is a **Gaussian splat, not a mesh**: splats render beautifully and suit AR/VR, previsualization, and simulation backdrops, but a conventional game or DCC pipeline that needs watertight topology, UVs, and PBR materials still requires a separate meshing step. Everything is **MIT licensed**.