Reading
Explore how the Cider project enables W8A8/W4A8 quantized inference on Apple Silicon chips via MLX custom primitives, significantly boosting the prefill speed of large language models.
Section 01
Cider is an MLX extension project for Apple Silicon chips. It unlocks underutilized INT8 tensor operation capabilities through custom primitives, enabling W8A8/W4A8 quantized inference. This significantly boosts the prefill speed of large language models (1.2-1.9x) and fully leverages the hardware potential of Apple Silicon.
Section 02
Quantization is a technique that converts model weights and activation values into low-precision integers (W8A8: 8-bit weights/activations; W4A8: 4-bit weights/8-bit activations). It reduces memory usage and bandwidth requirements while accelerating inference. Apple Silicon M5 chips include dedicated INT8 tensor operation units, but the standard MLX framework does not fully expose this capability—Cider fills this gap.
Section 03
As an MLX extension, Cider supports two quantization modes: W8A8 and W4A8. Its core innovation is the deep optimization of Apple Silicon matrix multiplication units, encapsulating quantized matrix multiplication into MLX-recognizable custom primitives. This balances MLX's ease of use with performance close to hardware limits.
Section 04
Cider achieves a 1.2-1.9x speedup in the prefill phase of LLMs, resulting in faster first response times and support for running larger models. The energy efficiency advantage of INT8 operations extends laptop battery life and enhances the user experience of edge-side interactive applications.
Section 05
Cider is released as an MLX extension, deeply integrated with Apple's machine learning ecosystem. It can seamlessly work with MLX features like automatic differentiation and device management. Its design pattern provides a reference for other quantization solutions and can be combined with techniques such as speculative decoding and paged attention.
Section 06
Cider is suitable for scenarios where LLMs run locally on Mac (developer AI assistants, offline document processing, privacy-sensitive enterprise applications). As Apple Silicon chips iterate, the performance of INT8 operation units will further improve, and Cider's role in edge-side AI deployment will become more important.
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