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
213mo ago
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GitHub Repository
Turbo1Bit: Combining 1-bit LLM weights (Bonsai) with TurboQuant KV cache compression for maximum inference efficiency. 4.2x KV cache compression + 16x weight compression = ~10x total memory reduction.
29 starsC
Turbo1Bit – Run Bonsai-8B at 65K context in 3.9 GB RAM
by tetsuto·Apr 2, 2026·1 point·0 comments
AI Analysis
●●●BangerWizardryNiche Gem
Runs 65K context on 8GB RAM by fixing KV cache quantization for Bonsai.
Strengths
- •Validates Flash Attention with KV quantization, enabling 65K context on 8GB MacBook Air.
- •Delivers 2.4x prefill speedup alongside the significant memory reduction benchmarks.
Weaknesses
- •Niche utility limited to developers running local LLMs on constrained consumer hardware.
- •Depends on upstream stability of llama.cpp and PrismML's Bonsai model weights.
Category
Target Audience
Developers running local LLMs on consumer hardware
Similar To
llama.cpp · Ollama · LM Studio
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