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description: A personal reflection on running local LLMs across two very different machines: an RTX 5070 desktop and a laptop with only Ryzen integrated Radeon graphics....
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> ## Documentation Index
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> Use this file to discover all available pages before exploring further.

# My RTX 5070 sits idle while integrated graphics handle my everyday AI work

**[XDA Developers](https://daily.dev/sources/xda-developers)** · 5 min read · 0 upvotes · 0 comments

## Summary

A personal reflection on running local LLMs across two very different machines: an RTX 5070 desktop and a laptop with only Ryzen integrated Radeon graphics. Most everyday AI tasks, like short prompts, summarization, and rewriting, run acceptably on the integrated GPU via LM Studio without noticeable slowdown. Heavier workloads and larger models still clearly benefit from the RTX 5070's dedicated power. The takeaway is to match hardware to the model and workflow rather than defaulting to the most powerful GPU available.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.xda-developers.com/rtx-5070-sits-idle-while-integrated-graphics-handle-my-everyday-ai-work>

## Questions this post answers

### Can integrated graphics like AMD Radeon handle running local LLMs reasonably well?

Yes, for lightweight everyday tasks. A Ryzen 7 5825U laptop with 16GB RAM and only integrated Radeon graphics ran smaller local models through LM Studio well enough for short prompts, rewrites, summaries, and brainstorming, without making the system unusable, though response times were slower than on a dedicated GPU.

_Comparing hardware setups for local AI work gets easier with real experience reports on daily.dev._

### When does a dedicated GPU like the RTX 5070 actually matter for local LLMs?

A dedicated GPU like the RTX 5070 matters most for larger models and heavier workloads, where it delivers noticeably faster responses and lets you interact with models that would be impractical to run on integrated graphics. For short, lightweight prompts, the performance gap is much less noticeable.

_Developers deciding between GPU tiers for local LLM work can track these tradeoffs on daily.dev._

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- [I ran a local LLM on integrated graphics instead of buying a GPU, and the results surprised me](https://daily.dev/posts/i-ran-a-local-llm-on-integrated-graphics-instead-of-buying-a-gpu-and-the-results-surprised-me-me3jdvjxy) · XDA Developers · 0 upvotes · 0 comments
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---

Tags: [#gpu](https://daily.dev/tags/gpu), [#self-hosting](https://daily.dev/tags/self-hosting), [#local-ai](https://daily.dev/tags/local-ai), [#lm-studio](https://daily.dev/tags/lm-studio)

[View this post on daily.dev](https://daily.dev/posts/my-rtx-5070-sits-idle-while-integrated-graphics-handle-my-everyday-ai-work-utlnpezrm)

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