Qwopus MoE 35B-A3B lands in GGUF

This is a clever distillation package, not a new frontier base model: it pairs a sparse Qwen MoE backbone with Opus-style reasoning traces, then ships the result in a format local-stack users can actually deploy. The appeal is obvious for agentic coding and research workflows, but the model card also suggests the usual tradeoff between richer visible output and raw speed.

• –The MoE backbone keeps compute low at inference time, with roughly 3B active parameters out of 35B total.
• –GGUF quantizations make it practical for local use, including a 4-bit build sized for around 24GB VRAM.
• –The model card shows strengths in coding, planning, and research-style output, but also some reasoning and latency regressions versus the earlier Opus-distilled variant.
• –This is best read as an opinionated local reasoning model for power users, not a universal replacement for the underlying closed models.