If you’re planning to run large language models locally, the first question you need to answer isn’t about CPU speed or storage—it’s about VRAM. Video memory determines what models you can run, at what quality level, and how responsive they’ll be. Get this wrong and you’ll either overspend on hardware you don’t need or build … Read more
Running large language models locally has become increasingly practical in 2026, but choosing the right GPU can make or break your experience. If you’re weighing the RTX 3060, 4060, or 4090 for local LLM inference, you’re asking the right question—but the answer isn’t straightforward. VRAM capacity, not just raw compute power, determines what models you … Read more
You finish training a binary classification model. Two numbers stare back at you: ROC AUC 0.91, Log Loss 0.34. Is that good? Which one should you care about? You tune the model, ROC AUC climbs to 0.93 but Log Loss barely moves. A colleague’s model has AUC 0.88 and Log Loss 0.21. Who has the … Read more
AI agents promise autonomy—systems that can reason about tasks, select tools, and execute multi-step workflows with minimal supervision. Demos show impressive capabilities: agents booking flights, debugging code, researching topics, and managing complex processes. Yet when deployed in production, most agents fail spectacularly and unpredictably. An agent that successfully completes tasks 95% of the time in … Read more
Your model achieves 98% accuracy during validation—far better than expected. You deploy to production and performance collapses to barely above random. This frustrating scenario plays out repeatedly across ML projects, and the culprit is usually data leakage: information from outside the training dataset inadvertently influencing the model in ways that don’t generalize. Data leakage is … Read more
You’ve set up a local LLM and it’s generating at 15 tokens per second. Is that good? Should you be happy, or is your setup underperforming? Unlike cloud services where you simply accept whatever speed you get, local LLMs put performance optimization in your hands—but that requires knowing what benchmarks to target. The answer isn’t … Read more
The narrative around large language models has long fixated on size: bigger models, more parameters, greater capabilities. GPT-4’s 1.7 trillion parameters, Claude’s massive context windows, and ever-expanding frontier models dominate headlines. Yet in production environments where businesses deploy AI at scale, a counterintuitive trend emerges: smaller language models—those with 1B to 13B parameters—are winning where … Read more
The rise of large language models has given users two distinct paths: cloud-based services like ChatGPT or locally-run models on your own hardware. This choice affects everything from privacy and costs to performance and capabilities. Understanding the fundamental differences between ChatGPT and local LLMs helps you make informed decisions about which approach suits your needs. … Read more
Local large language models have evolved from experimental curiosities to practical productivity tools. Running LLMs on your own hardware offers privacy, control, and unlimited usage—but the real value emerges when you integrate them into actual workflows. Rather than treating local LLMs as mere chatbots, you can build automated pipelines that handle repetitive tasks, process information … Read more
Running large language models locally promises privacy, control, and independence from cloud services. The appeal is obvious—no API costs, no data leaving your infrastructure, and the freedom to experiment without limitations. But the excitement of setting up your first local LLM often crashes against a frustrating reality: the model is painfully slow. Responses that cloud … Read more
