Splash 1.1.0 released, GGUF quants support, MLX import and more

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Summary

Splash 1.1.0 has been released with GGUF quantization support, MLX import, and optimized kernels for Apple Silicon, enabling efficient local AI inference at high speeds.

On my M5 Pro 64GB I can comfortably work in an agentic setup with the Qwen3.8 27B model in good quality (Unsloth UD-Q4_K_XL) at a decent speed of 50 t/s. Splash combines optimized kernels, excellent speculative decoding, a well-implemented prefix cache, and mixed-weight support in a single program. To me, this is a breakthrough in local inference on Apple Silicon. https://github.com/incoai/splash/releases/tag/1.1.0
Original Article

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KFlash: Benchmarks of newly released versions of Apple Silicon optimized engines: 1.4.0 Splash (DFlash2 ~ 50 tok/s) vs 0.2 Kapitsa (KFlash ~65 tok/s), MLX(MTP) & more on Qwen 3.8 27b/4b/35b 4Q on Macbook Pro M4 Pro 48 GB 2024, 48GB & Macbook Pro M1 Pro 16GB 2021

Reddit r/LocalLLaMA

Nace.AI's Kapitsa 0.2 ships KFlash, a best-first tree-based speculative decoding method for Apple Silicon, benchmarked against Splash 1.4.0's DFlash2, mlx-serve, Ollama, llama.cpp and others on Qwen models across M4 Pro and M1 Pro MacBooks, showing top decode throughput (~65-201 tok/s).