PrismML

Artificial intelligence
$16.3M raised

PrismML built the first ever commercial 1-bit LLM. Why does that matter? Most models store each weight as a 16-bit floating-point number; 1-bit models store them as simply +1 or –1. The takeaway that matters is that 1-bit models can be smaller, faster, and massively more eco-friendly with a relatively small loss in performance. In other words, the intelligence per gigabyte or per watt blows traditional models out of the park. Aside from the obvious environmental benefits, 1-bit models can run locally on edge devices like phones.

I worked with PrismML to design their visual identity, marketing site, and data visualizations, which are central to explaining advantages over competitors.

¹ Visual identity
² Web design
³ Web development
⁴ Motion design
⁵ Data visualization

Project highlights

Benchmark palette

Intelligence density

Negative log of the model's error rate divided by the model size

Model benchmark comparison

Average score (IFEval, GSM8K, HumanEval+, BFCL, MuSR, MMLU-Redux)

Throughput

Tokens per second across hardware platforms (higher is better)

Performance vs. size

Average score (IFEval, GSM8K, HumanEval+, BFCL, MuSR, MMLU-Redux)

Bonsai 8B canopy

Average score (IFEval, GSM8K, HumanEval+, BFCL, MuSR, MMLU-Redux)

Energy consumption

Milliwatt-hours per token across hardware (lower is better)

Other projects

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