Anemll reports running Qwen 3.8-27B on the iPhone 18’s Apple Neural Engine (A20) with a confirmed 24K token context window by placing both attention keys and values in int8 while keeping the delta network in FP16. Decoding throughput is reported at 15-60 tokens/second in typical conditions and can peak around 70 tokens/second on very predictable contexts, demonstrating that the A20 ANE can handle large-context, high-parameter models with practical on-device speeds.
The announcement includes concrete quantization details: a draft model called dflash-2 uses mixed 2-4 vector quantization at roughly 2.8 bits per weight (bpw) with only a small drop in acceptance rate, while the base model derived from PrismML Bonsai-2 was requantized from 1.585 bpw to 1.5 bpw using VQ configured as 6 bits per 4 weights. Those choices - K/V int8, selective FP16 for delta computations, and mixed-vector quantization - enable the 27B model to run locally with aggressive compression and maintain usable decoding performance.
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