AI/ML●●●Banger
Glq LLM quantization using E8 lattice
E8 lattice codebooks beat GPTQ at 2-4 bpw with fused CUDA kernel skipping weight materialization.
WizardryBig Brain
acd
2013d ago
Back to browse
7MB binary-weight LLM running in the browser, no FPU needed
by onebitmodel·Mar 24, 2026·1 point·0 comments
AI Analysis
●●●●GemWizardryZero to One
7MB binary-weight LLM runs entirely on integer math with no floating point unit.
Strengths
- •Extreme model compression allows inference on hardware without FPUs, expanding edge.
- •Pure integer inference reduces power consumption and increases speed on.
Weaknesses
- •57M parameter limit restricts complex reasoning capabilities compared to modern.
Category
Target Audience
Embedded Developers, Edge AI Engineers, Hobbyists
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TinyLLM · WebLLM
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