Ternary LLMs: BitNet, 1-Bit Bonsai & lokale CPU-Modelle

Ternary Modelle (1.58 Bit) brauchen 20x weniger Speicher als FP16 und laufen auf CPU. BitNet b1.58 2B4 und 1-Bit Bonsai zeigen das. Aber beide sind Base Models ohne Chat-Feintuning und ohne produktive Inference-Engine. Wer heute lokal testen will, nutzt 2-Bit-quantisierte Chat-Modelle via Llama.cpp.

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Ternary LLMs: BitNet, 1-Bit Bonsai & lokale CPU-Modelle

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