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Show HN: Bonsai 1.7B ternary model at 442T/s on M4 Max
Demonstrating significant performance improvements (+42.0% for tg128, +8.8% for pp512) for the Bonsai 1.7B ternary model on M4 Max hardware through autonomous agentic evolution search for Metal kernel optimization.
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AI Executive Synthesis
Demonstrating significant performance improvements (+42.0% for tg128, +8.8% for pp512) for the Bonsai 1.7B ternary model on M4 Max hardware through autonomous agentic evolution search for Metal kernel optimization.
This submission highlights a critical advancement in on-device AI model performance. Optimizing the Bonsai 1.7B ternary model on M4 Max hardware, achieving a 42% speed increase for token generation, directly addresses the demand for efficient, low-latency AI inference at the edge. For B2B SaaS, this translates into more powerful local AI applications, reduced cloud inference costs, and enhanced data privacy by keeping processing on-device. The "agentic evolution search" for kernel optimization represents a significant trend: automated performance engineering for specialized hardware. This capability is crucial for deploying performant AI in embedded systems, mobile applications, and enterprise endpoints, driving down operational costs and improving user experience.
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%)
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What is Bonsai 1.7B ternary model at 442T/s on M4 Max?
Bonsai 1.7B ternary model at 442T/s on M4 Max is analyzed by our AI as: Demonstrating significant performance improvements (+42.0% for tg128, +8.8% for pp512) for the Bonsai 1.7B ternary model on M4 Max hardware through autonomous agentic evolution search for Metal kernel optimization.. It focuses on This submission highlights a critical advancement in on-device AI model performance. Optimizing the Bonsai 1.7B ternary model on M4 Max hardware, a...
Where did Bonsai 1.7B ternary model at 442T/s on M4 Max originate?
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When was Bonsai 1.7B ternary model at 442T/s on M4 Max publicly launched?
The initial public indexing or launch date for Bonsai 1.7B ternary model at 442T/s on M4 Max within our tracked developer communities was recorded on May 5, 2026.
How popular is Bonsai 1.7B ternary model at 442T/s on M4 Max?
Bonsai 1.7B ternary model at 442T/s on M4 Max has achieved measurable traction, logging over 13 traction score and facilitating 3 recorded discussions or engagements.
Which technical categories define Bonsai 1.7B ternary model at 442T/s on M4 Max?
Based on metadata extraction, Bonsai 1.7B ternary model at 442T/s on M4 Max is categorized under topics such as: Bonsai 1.7B ternary model, 442T/s, M4 Max, PrismML.
What are some commercial alternatives to Bonsai 1.7B ternary model at 442T/s on M4 Max?
Our semantic intelligence engine identifies potential commercial alternatives in the SaaS space, such as Bluedot 2.1, which offers overlapping value propositions.
How does the creator describe Bonsai 1.7B ternary model at 442T/s on M4 Max?
The original author or development team describes the product as follows: "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...."
Community Voice & Feedback
Nice work, that throughput is wild.
That performance jump is incredible. Curious to know if the evolution search found any specific optimizations that were counter-intuitive to how we normally write Metal kernels?
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