self-hosting

Running AI models and infrastructure on your own hardware instead of renting from cloud providers. Content here covers the practical realities of deploying open-source LLMs locally: GPU selection and VRAM budgets, inference optimization, CUDA profiling, and tuning serving stacks for throughput and latency. Expect honest cost comparisons against cloud APIs, hardware utilization tradeoffs, and the operational friction that benchmarks rarely mention.

local-llms, gpu, self-hosting, ai-development

Four GPUs, Two Weeks, and the Uncomfortable Truth About Local LLMs

What happens when you throw 96 GB of VRAM at open-source models, optimize every last flag, profile CUDA kernels, redesign system prompts, and still end up reaching for a cloud API. It started with a simple premise: why pay per token when you have the hardware? I have four