Hardware Guide: What Do You Actually Need to Run Local LLMs?
The article provides a comprehensive guide on the hardware requirements for running local large language models (LLMs). It emphasizes that VRAM is the critical factor for performance, with various models being compatible with different GPU specifications. Additionally, it offers budget-friendly options for users looking to get started with local AI setups.
- ▪VRAM is identified as the bottleneck for running local LLMs, rather than compute power.
- ▪A model on an RTX 3060 can achieve 96% of the quality of an A100 model, albeit at a slower speed.
- ▪The article suggests that users can run LLMs on a variety of systems, including older gaming PCs and laptops.
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| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/lingdas1/hardware-guide-what-do-you-actually-need-to-run-local-llms-1eik |
| Publication time | Sat, 23 May 2026 18:57:27 +0000 |
| Retrieval time | 2026-05-23T19:07:27.603Z |
| Last seen | 2026-05-23T19:07:27.603Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | j_1Ufz4bfkhE |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3946584) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Lingdas1 Posted on May 23 • Originally published at github.com Hardware Guide: What Do You Actually Need to Run Local LLMs? #hardware #llm #opensource #guide 02 — Hardware Guide: What Do You Actually Need? 🟢 Beginner — No matter what computer you have, there's a model that will run on it. The Most Important Thing to Know VRAM is the bottleneck, not compute.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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