The Developer's Guide to Qwen 3.5 0.8B: A Hands-On Tutorial for Local Deployment
When running open-source LLMs in production, you probably hit GPU limits faster than expected. VRAM fills up quickly, the KV cache grows with every request, and latency spikes as soon as concurrency increases. A model that works fine in a demo actually needs multiple high-end GPUs for production. For many developers and indie hackers, the ultimate goal is running models locally—on a cheap VPS, a laptop, or an edge device. The good news is that you no longer need large models to get strong results. Over the past year, advances in distillation, hybrid architectures, and reinforcement learning have made small language models (SLMs) far more capable than their parameter counts suggest. They now deliver solid reasoning, coding, and agentic performance, fitting comfortably on a single consumer GPU or even CPU-only environments. ...