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How to calculate LLM memory and speed for local hardware

By PulseAugur Editorial · [1 sources] ·

This article provides a technical guide for users to estimate the memory and speed requirements for running Large Language Models (LLMs) locally. It details how to calculate the necessary memory for model weights and KV cache, and how to determine the theoretical maximum generation speed based on memory bandwidth. The author emphasizes performing these calculations before downloading models, using tools like llama.cpp, Ollama, and LM Studio, and explains the impact of quantization and grouped-query attention on performance. AI

IMPACT Enables users to better estimate local LLM performance and hardware needs, potentially increasing adoption of local models.

RANK_REASON The article provides a technical guide and calculation method for running LLMs locally, focusing on tools and hardware.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

How to calculate LLM memory and speed for local hardware

How we ranked this

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36 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
The article provides a technical guide and calculation method for running LLMs locally, focusing on tools and hardware.
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Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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High
Clearly on-topic for AI-industry coverage.
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Breaking (< 6h)
Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

Full methodology in our editorial standards.

COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Ahmad Dogar ·

    Will this LLM run on my laptop? The memory math to do before you download

    <p>The usual way people pick a local model goes like this. A model is trending, you pull 18 GB of weights over a slow connection, you start it, and it types four words a second while your fans spin up. You decide local LLMs are for people with a 4090 and go back to the API.</p> <…

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