Bonsai-27B-mlx-1bit

Prism ML Website  |  Whitepaper  |  Demo & Examples  |  Discord 1-bit Bonsai 27B Full 27B-class reasoning in binary transformer weights — the first 27B-class model to run on a phone **~14.2x** smaller than FP16 | **~90%** of FP16 intelligence retained | **~11 tok/s** on iPhone 17 Pro Max Highlights **~3.9 GB** deployed footprint (down from ~54 GB FP16) — fits within the per-app memory budget of a high-end phone such as the iPhone 17 Pro Max **Retains thinking, reasoning, and agentic behavior** deep in the sub-4-bit regime, where conventional low-bit representations collapse — 76.11 average across 15 thinking-mode benchmarks (89.5% of FP16), including math at 91.66 and coding at 81.88 **End-to-end binary language weights** across embeddings, attention projections, MLP projections, and LM head, at a *true* 1.125 bits per weight — no high-precision escape hatches behind a low-bit label; the vision tower ships in compact 4-bit HQQ **262K-token context** on-device, kept practical by the Qwen3.6-27B hybrid-attention backbone (~75% linear attention) and 4-bit KV-cache quantization **First interactive 27B-class generation on a phone**: ~11 tok/s on iPhone 17 Pro Max; ~44 tok/s on an Apple M5 Pro laptop **Custom 1-bit hybrid-attention kernels** on Apple MLX (Python, Swift) and CUDA — packed weights are consumed directly, never expanded back to FP16 **Ships with a DSpark speculative-decoding drafter layer** trained against the Bonsai 27B target — a lossless **1.37x** decode speedup on the CUDA serving path **Ternary companion**: also available as Ternary Bonsai 27B, the quality-oriented operating point (~7.2 GB, 95% of FP16) for laptops and GPUs Resources **Whitepaper** — full methodology, benchmarks, and measurement notes **Demo & examples** — serving, benchmarking, and integrating Bonsai **Low-bit kernels**: MLX fork (Apple Silicon) · mlx-swift fork (iOS/macOS) · llama.cpp fork (CUDA) **Discord** — join the community for support, discussion