AI/ML●●Solid
Efficient LLM Architectures for 32GB RAM (Ternary and Sparse Inference)
Native ternary training beats post-training quantization for memory efficiency.
Big BrainBold Bet
fatihturker
214mo ago
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GitHub Repository
BitBop: latent-free ternary training of small language models
0 starsPython
Latent-free ternary LLM training
by Feyd·Jul 13, 2026·1 point·1 comment
AI Analysis
●●●BangerBig BrainWizardry
Fits a 325M model in 6GB VRAM where STE and float baselines crash.
Strengths
- •Eliminates full-precision latent weights, storing only flip momentum for ternary body.
- •Achieves parity with GPT-2-124M on BabyLM using strictly ternary weights.
- •Reference implementation runs on consumer RTX 3060, proving accessibility.
Weaknesses
- •No custom int2 kernel yet, so no actual inference speedup despite smaller storage.
- •Results limited to 125M-325M scale; asymptotic behavior at larger sizes unknown.
Category
Target Audience
ML researchers and engineers working on model quantization
Similar To
BitNet · Microscaling formats (MX)
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