DeepSeek R1 on a Budget? Our XGBoost Model Predicts 84% Accuracy and 30–40% RAM Savings via Quantization

Author(s): R. Thompson (PhD)

Originally published on Towards AI.

(Credit : Developed using AI)

“Deploying AI locally is no longer a constraint — it’s a prediction challenge.”

Much of today’s generative AI narrative is fixated on benchmarks, token counts, and transformer tweaks. But in the background, a quiet transformation is unfolding. Developers, researchers, and tinkerers are trying to bring AI to their own machines — not just because of cost or privacy, but because they want control. This transition toward local inference brings an underappreciated complexity: how do you know your machine will handle it?

This article repositions local LLM deployment, specifically with DeepSeek R1, as a predictive modeling problem. Rather than spinning up trial-and-error scripts, we use a systematic approach grounded in data science to anticipate performance outcomes based on your machine specs and configuration choices.

Let’s walk through a complete pipeline: architecture interpretation, dataset generation, regression modeling, feature insights, and toolkit design — all aimed at demystifying and predicting whether DeepSeek R1 will run efficiently on your hardware.

The architecture image illustrates a modular structure comprising Core Services, the Model Layer, and Data Storage. Each component handles a distinct function — request orchestration, token embedding, memory persistence — making this LLM architecture both analyzable and adaptable.

As shown in the given image, incoming… Read the full blog for free on Medium.

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Published via Towards AI