@AlmustyFX: Mixture-of-Experts lightweight solutions are becoming increasingly mature. Ling-3.0-tiny has a total scale of 7.9B para…
Summary
Ling-3.0-tiny uses a Mixture-of-Experts architecture with 7.9B total parameters but only activates 1.3B per run, reducing computational overhead to enable smoother local execution on Mac.
Similar Articles
@AdinaYakup: Ling 3.0 tiny a 7.9B/1.3B hybrid reasoning MoE https://huggingface.co/inclusionAI/Ling-3.0-tiny…
InclusionAI introduces Ling-3.0-tiny, a 7.9B-parameter hybrid reasoning MoE model with only 1.3B active parameters per token, optimized for efficient local and edge deployment.
Inkling-Small (4 minute read)
Thinking Machines released Inkling-Small, an efficient open-weights Mixture-of-Experts model with 276B total and 12B active parameters, achieving comparable performance to its larger sibling Inkling at a quarter of the size. It features native reasoning over audio and images, variable thinking effort, and a 1M-token context window.
Medium sized MoE LLM models
A user asks for recommendations on medium-sized Mixture-of-Experts LLMs (up to 60B params in float8/110B in mxfp4), listing Qwen 3.5 35B, Gemma 4 A4B, and Nemotron 3 Nano, and inquiring about niche options beyond these.
@nicebabycat: https://x.com/nicebabycat/status/2091726637155103126
This article provides a detailed test of the local deployment and performance of the Ling-3.0-tiny model on an Apple M5 chip Mac, demonstrating the feasibility of running a 7.9B parameter model at 47 tokens per second without a discrete GPU.
@runsonai: https://x.com/runsonai/status/2079919970209681734
A detailed comparison of the new open-weights Laguna-S-2.1 mixture-of-experts model against Qwen 3.6-35B-A3B, highlighting how Laguna's larger active parameter count and long-horizon focus make it superior for extended tasks despite slower speed.