MiniMax-M2.5 230B MoE Quantized for Apple Silicon: Breakthrough in Local AI Performance
A groundbreaking GGUF quantization of MiniMax-M2.5, a 230B Mixture of Experts model, has been optimized for Apple’s M3 Max with 128GB RAM, delivering unprecedented local inference speeds without swap usage. The Q3_K_L variant achieves 28.7 tokens per second with minimal perplexity degradation, setting a new standard for high-RAM Mac AI deployment.
MiniMax-M2.5 230B MoE Quantized for Apple Silicon: Breakthrough in Local AI Performance
A remarkable advancement in local large language model deployment has emerged from the open-source AI community, as a highly optimized GGUF quantization of MiniMax-M2.5 — a 230-billion-parameter Mixture of Experts (MoE) architecture — has been successfully adapted for Apple’s M3 Max chip with 128GB of unified memory. According to a detailed technical report published on Reddit’s r/LocalLLaMA, the Q3_K_L quantized version achieves 28.7 tokens per second during text generation while maintaining native RAM usage and zero swap activity, a feat previously unattainable with larger Q4 quantizations on the same hardware.
The model, originally developed by Chinese AI firm MiniMax, is now accessible in GGUF format via Hugging Face, enabling Mac users to run one of the world’s most powerful MoE models locally without cloud dependency. The quantization workflow, meticulously engineered by community contributor u/Remarkable_Jicama775, avoided direct FP8-to-quant conversion, instead using an F16 master checkpoint as an intermediary to preserve reasoning fidelity. This approach resulted in a 110.22 GiB Q3_K_L file that fits comfortably within 128GB RAM, leaving ample headroom for extended context windows up to 196k tokens — a critical advantage for long-document analysis and multi-turn dialogue applications.
Benchmarking using the WikiText-2 dataset yielded a perplexity score of 8.2213 ± 0.09, demonstrating that the Q3_K_L quantization retains exceptional logical coherence despite a significant reduction in model size compared to its Q4 counterpart. In direct comparison with custom IQ4_XS mixes — which achieve slightly better perplexity (~8.57) — the Q3_K_L variant trades a marginal 0.22 PPL difference for dramatically improved throughput and system stability. The Q4_K_M variant, by contrast, exceeded available RAM and triggered swap usage, causing severe latency spikes and rendering real-time interaction impractical.
Performance metrics reveal a compelling trade-off: prompt processing occurs at an impressive 99.2 tokens per second, while generation maintains 28.7 t/s — among the highest speeds recorded for a 230B-class model on consumer hardware. This efficiency stems from reduced memory bandwidth pressure: Q3_K_L’s smaller attention tensors allow faster tensor transfers between the Neural Engine and unified memory, a key bottleneck in Apple Silicon’s architecture. The model’s Jinja chat template also functions flawlessly, with
For developers and researchers, this release represents a paradigm shift. High-end Macs, once considered underpowered for billion-parameter models, are now viable platforms for running state-of-the-art MoE architectures locally. The Q3_K_L variant is ideal for users constrained to 128GB RAM who prioritize speed, stability, and context length over marginal gains in reasoning accuracy. Those with 192GB+ systems may still opt for IQ4_XS mixes for peak performance, but for the majority of professional Mac users, this Q3_K_L release offers the best balance of capability and practicality.
Community interest is already mounting, with requests for even smaller quantizations (IQ2_XXS, IQ3_XS) for 64GB and 96GB systems. The success of this project underscores the growing power of open collaboration in AI model optimization — turning enterprise-grade models into accessible, local tools. With the model now live on Hugging Face, the era of high-performance, offline AI on consumer hardware has arrived.
