A collection of 7 posts
Learn how to run Qwen3-Coder-Next locally in 2026: hardware requirements, llama.cpp setup, benchmarks, pricing, comparisons, and real coding examples.
Run Qwen3 Next 80B A3B on Windows. Step-by-step setup, optimizations, and deployment guide for fast, private, and cost-effective AI inference.
Run Qwen3 Next 80B A3B on macOS Apple Silicon. Step-by-step setup, optimizations, and deployment guide for fast, private, and cost-effective AI inference.
Last updated April 2026 — refreshed for current model/tool versions. Tencent's Hunyuan-7B and Alibaba's Qwen 3 family were the two highest-signal Chinese open-weight releases of 2025. Eight months later the picture has shifted: Tencent re-released the Hunyuan dense line (0.5B / 1.8B / 4B / 7B) on
Compare Gemma 3 vs Qwen 3 open source LLMs for 2026: performance benchmarks, features, implementation, use cases, and discover which AI model is best for your business and technical needs.
Quick answer. Run Qwen3-8B on Ubuntu via Ollama for a 5-minute setup, vLLM 0.20+ for production serving, or llama.cpp for GGUF flexibility. Hardware floor: 16 GB RAM and an 8 GB+ VRAM GPU (RTX 3060 or better). 4-bit quants cut VRAM to roughly 5-6 GB while keeping near-FP16
Quick answer. Qwen3-8B remains the best local LLM for Windows machines with 8-16 GB of VRAM in 2026: 8.2B parameters, 32K context (131K with YaRN), Apache 2.0, and a thinking-mode toggle. Use Ollama 0.22 for one-command setup (ollama run qwen3:8b, 5.2 GB Q4_K_M)