Pithy Cyborg | AI News Made Simple
Pithy Cyborg | AI News Made Simple
Running AI locally gives you more control, more privacy, and more freedom, but it also introduces serious tradeoffs. In this category, I cover local LLMs, self-hosting, quantization, VRAM limits, inference speed, memory overhead, hardware constraints, private deployment, and what it really takes to run models on your own machines.
This is where the dream of private AI collides with engineering reality. If you want to understand local inference, self-hosted AI, and the practical cost of running models outside the cloud, this category is for you.
Browse the articles below to explore local LLMs and self-hosted AI.
Local LLM inference is sequential by default. When one user sends a request, the model generates tokens one at a time until the response is complete. When three users send requests simultaneously, …
Continue Reading about Why Does a Self-Hosted Model Feel Fast for One User but Collapse for Three? →
Yes, with realistic expectations. A used NVIDIA RTX 3090 or 4090 available for $400 to $700 in 2026 can run Qwen2.5-Coder 14B or DeepSeek-Coder-V2 Lite at 4-bit quantization with competitive …
Continue Reading about Can You Run a Useful Private Coding Model on a Used GPU in 2026? →
Quantization reduces the number of bits used to represent each model weight. A model quantized to 4-bit uses roughly half the memory of its 8-bit version and a quarter of its 32-bit version. But …
Continue Reading about Why Does My Local LLM Use So Much RAM Even After Heavy Quantization? →
