Training BitNet on MacBook: Ternary Quantization Challenges & Metal Kernel Optimization
QuixiAI · x · 2026-07-07
QuixiAI shared an experiment log on training a BitNet ternary quantized model from scratch on a MacBook. Direct quantization leads to garbled outputs, requiring a "healing" step to restore usable quality. The training scale was in the billions of tokens (far less than the trillions required by the original version), and highly optimized Metal compute kernels were written specifically for Apple Silicon to support training efficiency. This experiment demonstrates the feasibility and challenges of training extremely compressed models on consumer hardware.
Related event: Developer Replicates BitNet Training Code on MacBook(2 posts)→
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