LLaMa Adds Maple 20B-A1B MoE Architecture with Ternary Quantization
AlexGabbia, a contributor to the LLaMa project, has added the Maple 20B-A1B ternary Multi-Expert (MoE) architecture to the LLaMa codebase. This update includes support for both q20 and tQ10/TQ20 quantization schemes. The team ran tests on a RTX 5070 Ti GPU, finding that TQ20 performed slightly worse than q20 in perplexity scores across different contexts. They also noted differences between CPU…
Key points
- LLaMa adds support for Maple 20B-A1B ternary MoE architecture
- TQ10/TQ20 quantization scheme included in the update
- CPU and CUDA backends show different performance metrics
llama: add Maple 20B-A1B ternary MoE architecture (CPU) by AlexGabbia · Pull Request #27000 · ggml-org/llama.cpp
github.com · 14 September 2026Loading the full article…
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