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[[{“value”:”Running a 27B Model on iPhone: Prism ML’s 1‑Bit Bonsai Quantization Explained
I break down how Prism ML ran a 27B parameter Qwen-based model on an iPhone 17 Pro at about 11 tokens/sec by compressing it from ~54GB to 3.9GB using their 1-bit “Bonsai” approach. I explain quantization basics (16-bit to 8/4-bit, ternary, and 1-bit), why naive post-training quantization fails due to compounding error, and how prior open-source methods (llama.cpp mixed precision, GPTQ calibration-aware, AWQ activation-aware) work well down to ~4-bit but collapse at 2/1-bit. Then I cover quantization-aware training inspired by BitNet, end-to-end 1-bit/ternary variants (including the LM head), multimodal support with a 4-bit vision tower, a 262K context window, and speculative decoding (DeepSeek DGX Spark drafter) for latency. I also discuss benchmarks, intelligence density per GB, tool-calling weaknesses, and my own 1-bit tests on an M2 Max showing ~29–30 tok/s plus looping on harder tasks.
LINKS:
Blog: https://prismml.com/news/bonsai-27b
Whitepaper: https://github.com/PrismML-Eng/Bonsai-demo/blob/main/bonsai-27b-whitepaper.pdf
Huggingface: https://huggingface.co/collections/prism-ml/bonsai-27b
Github: https://github.com/PrismML-Eng/Bonsai-demo/
Demo: https://huggingface.co/spaces/webml-community/bonsai-webgpu-kernels
https://youtu.be/eFgknPFK-g0
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00:00 27B Model on iPhone
00:27 Bonsai Compression Idea
01:32 Quantization Limits Explained
03:52 From PTQ to QAT
06:11 Bonsai End to End Setup
08:36 Speed and Benchmarks
10:55 Bonsai local test”}]] Read More Prompt Engineering
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