20B total parameters with only 1B active per token (A1B sparse architecture)?

20B total parameters with only 1B active per token (A1B sparse architecture)

Ternary weight quantization (-1, 0, 1) reduces memory footprint and enables faster inference?

Ternary weight quantization (-1, 0, 1) reduces memory footprint and enables faster inference

math, logic, and code generation

Open Source

r/LocalLLaMA August 05, 2026

⚡A 20B-parameter LLM with 1B active and ternary weights hits open-source…

Deep Dive

The original submission contains no article text—only a Reddit post footer crediting /u/cafedude, with a link and comments section.

Key Points

20B total parameters with only 1B active per token (A1B sparse architecture)
Ternary weight quantization (-1, 0, 1) reduces memory footprint and enables faster inference
Open-weight license focused on reasoning tasks: math, logic, and code generation

Maple-Preview makes efficient reasoning AI accessible on modest hardware, cutting costs while enabling private, customizable deployments.