Feb 19, 2026
MUHAMMAD GHIFARY
3D Gaussian Splatting (3DGS) has emerged as a transformative breakthrough in the field of 3D computer vision and radiance field reconstruction. By representing scenes as a collection of millions of differentiable 3D Gaussians, this technique enables unprecedented visual fidelity and real-time rendering speeds. However, the path to production-ready performance—the kind required for rapid training and high-frame-rate interaction—has historically been paved with NVIDIA GPUs and highly specialized CUDA kernels. This perceived hardware barrier has often relegated the Mac to a "viewer-only" role or a platform for slow prototyping.
This article demonstrates how to achieve production-grade performance directly on Apple Silicon by leveraging the power of native C++ and Metal implementations. By utilizing the Metal Performance Shaders (MPS) backend and deep learning framework integration with MLX, the heavy lifting of the Gaussian Splatting pipeline can be ported to run natively on the Mac.
It turns out that with the right optimization strategy—moving from high-level Python references to fully GPU-resident C++ Metal kernels—the Apple M4 hardware is not just a capable device, but a definitive powerhouse for 3D Gaussian Splatting. These optimizations deliver up to a 48x speedup, bringing the training loop from several seconds per iteration to over 38 iterations per second.
All the results discussed in this article can be produced through the following code repository: https://github.com/ghif/splat-apple.
While high-level frameworks like MLX and PyTorch offer excellent vectorized operations, custom algorithms like 3D Gaussian Splatting hit several architectural limitations when implemented in pure Python. Moving to native C++ and Metal kernels is not just about raw speed; it is about achieving the architectural freedom required for production-quality results.
Python JIT compilers perform best when array shapes and loop bounds are predictable. To maintain performance and avoid excessive re-compilation, many pure Python implementations resort to a fixed-grid expansion, such as assuming a Gaussian touches at most an 8x8 tile area. This approach creates significant visual artifacts, as large or extremely close Gaussians are artificially "clipped" into squares at tile boundaries.
In contrast, native C++ kernels calculate exact bounding boxes for every Gaussian dynamically. They can expand a single splat across the entire screen if necessary, ensuring perfect visual continuity with no performance penalty.
High-level Python execution frequently requires "sync-points" where the CPU must wait for the GPU to complete a task—such as sorting Gaussians by depth—before it can schedule the next operation. This constant hand-off creates a "ping-pong" effect that leaves the GPU idle for significant portions of the training loop.
By moving the entire rendering pipeline into a single, continuous sequence of Metal kernels, we eliminate these synchronization gaps. The GPU remains fully saturated throughout the process, which is the primary driver behind the leap from ~1 it/s to over 38 it/s.
Vectorized Python implementations typically process a fixed number of Gaussians per tile to keep memory usage constant for the compiler. However, dense real-world scenes often require blending hundreds or even thousands of Gaussians to reach full opacity. When this count is capped, it results in "black holes" where the background leaks through under-rendered regions.
Metal kernels overcome this by using dynamic while-loops to iterate through the entire sorted interaction list. They only stop when the pixel transmittance is truly exhausted, providing flawless visual fidelity regardless of the scene's complexity.
During the backward pass of training, gradients must be accumulated from millions of pixels back into the original Gaussian parameters. While Python's vectorized scatter_add is functional, it can be extremely memory-intensive at high resolutions.
The native implementation utilizes thread-safe atomic operations (atomic_add) directly on the GPU hardware. This allows thousands of concurrent threads to update the same parameter buffers simultaneously with minimal overhead, providing a massively more efficient path for high-throughput backpropagation.