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Bonsai 1.7B ternary model at 442T/s on M4 Max

Details

External ID
48010204
Source
HN
Company
—
Product
Bonsai 1.7B ternary model at 442T/s on M4 Max
Website domain
agents2agents.ai
Launched
May 4, 2026
Cohort
—
Upvotes
13
Upvotes percentile
0.654281098546042
Tags
—
Fetched at
Sept. 7, 2026, 9:26 p.m.
Updated at
Sept. 7, 2026, 9:26 p.m.

Description

We took a recently released Bonsai 1.7B ternary model from PrismML (https://github.com/PrismML-Eng/Bonsai-demo) and ran our agentic evolution search on it for 6 hours to optimize the Metal kernels. The search was fully autonomous.Measured against unmodified upstream llama.cpp at the same Bonsai/Q2_0 commit, same M4 Max:- tg128: 309.82 → 442.42 t/s (+42.0%)- pp512: 4250.32 → 4622.63 t/s (+8.8%)

Enrichment

Theme
niche developer utilities and guides
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
ternary language model
Manually corrected
False

Could you build this?

No Engineering ultra-fast ternary LLM inference on Apple Silicon involves writing custom low-level Metal Shading Language (MSL) kernels, threadgroup memory management, and SIMD-level matrix multiplication optimizations.

What it would actually take: This requires modifying the llama.cpp or MLX runtime with hand-tuned Metal Compute Shaders (MSL) specifically designed for 1.58-bit / ternary weight packing. The developer needs deep GPU microarchitecture expertise to optimize SIMD-group matrix multiply-accumulate (mma) instructions, utilize Apple Silicon unified memory caching, and minimize bandwidth bottlenecks through register pressure tuning and automated kernel search loops.

Discussion

3 comments analyzed.

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Attention rank: #11 of 31 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 167 days after the earliest competitor.

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