🚀 RELEASES & LAUNCHES
- Convai Innovations open-sources Vega, an 800M-parameter decision model
Convai Innovations open-sourced Vega, an 800-million-parameter typed decision model.- For developers building local AI decision systems and structured classifiers.
- Vega supports 73,000-token contexts, image inputs, and typed decisions; a 4B-parameter version is also available.
- The model uses an engine-and-adapter test-time-training architecture.
- Its open release gives builders a compact model for structured decisions rather than free-form text generation.
📊 BENCHMARKS & EVALS
- Qwen3.6 35B MoE Runs Locally on a 6GB RTX 2060
A user ran Qwen3.6-35B-A3B with vision and 131K context on a 6GB RTX 2060.- The setup targets low-VRAM local users running Qwen3.6-35B-A3B with llama.cpp.
- The Q4_K_M model delivered about 600 tok/s prefill and 23 tok/s decode initially, settling near 485 tok/s and 15 tok/s at 90K context.
- CPU-offloaded vision and MoE experts kept VRAM usage below 5.2GB on an RTX 2060 with 32GB system RAM.
- The demonstration shows that a 35B-parameter MoE model can support long-context multimodal inference on consumer hardware with 6GB VRAM.
Sources 📎 Coverage: reddit.com · 👁 via r/LocalLLaMA