Today we are releasing our speculative decoding implementation in our inference engine uzu. Initially for Qwen3.6 27B, with support for Qwen3.8 27B and Muse Glimmer coming soon. On Apple M5-series chips, we outperform MTPLX (MLX + speculative decoding) by almost 2x, and llama.cpp by over 3x at comparable quantization levels, with the strongest gains achieved on mathematical reasoning and coding tasks. Run the model: Mirai-M: trymirai.com/local-models/al… Mirai-L: trymirai.com/local-models/al… Explore the benchmarks: trymirai.com/metrics Learn more about our speculative decoding implementation: trymirai.com/blog/speculativ… Our draft model, quantized checkpoint format, verification algorithm, and GPU kernels are co-designed from the ground up around the latest Apple M5 chips to take maximum advantage of GPU Neural Accelerators. Unlike popular speculative decoding architectures such as model-native MTP, which produce small draft chains of 3-4 tokens at a time, we use extremely aggressive speculative budgets of 16-32 tokens. This enables us to use Neural Accelerator-backed GEMM kernels, achieving maximum utilization of hardware arithmetic throughput.