AI/ML●●●Banger
Msplat – 3D Gaussian Splatting training in ~90s on M4 Max, native Metal
3DGS training in 90 seconds on M4 via fused Metal kernels, no PyTorch overhead—unprecedented Apple Silicon story.
WizardryZero to OneNiche Gem
rayanht
204mo ago
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Metal Quantized Attention on M5 Max
by liuliu·Apr 1, 2026·4 points·0 comments
AI Analysis
●●SolidWizardryBig Brain
Custom Metal shaders deliver 1.87× speedup over FP16 baseline with Int8 matrix multiplication.
Strengths
- •Row-group-wise scale quantization for queries/keys is a clever optimization choice.
- •Fused kernel handles quantization, multiplication, and dequantization in single pass.
- •Real benchmarks with specific TFLOP measurements, not vague performance claims.
Weaknesses
- •Feature release for existing Draw Things app, not a standalone new project.
- •Apple Silicon only — RTX 5080 Mobile users can't benefit from this work.
Category
Target Audience
ML engineers optimizing inference on Apple Silicon
Similar To
MLX · llama.cpp · Stable Diffusion optimizations
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