Running LLMs Locally on Consumer Devices

Abstract

The paper explores practical deployment of large language models (LLMs) on consumer-grade hardware, driven byimprovementsinmodelefficiencyandoptimization. Itreviewsrecentopen-sourceLLMsthatarebothpowerfulandresource- efficient, outlines the hardware and software needed for local execution, and highlights key techniques like quantization and accelerationlibraries. Thestudycompareslocalversuscloud-baseddeploymentintermsofspeed,cost,energyuse,andprivacy. It finds that advanced open models can now run on high-end PCs, while smaller versions work well on mainstream setups, though challenges like hardware limits and energy demands remain.

Introduction

By the end of 2024, large language models (LLMs) have advanced significantly, enabling powerful open-source models to be run locally on consumer-grade hardware, rather than relying solely on costly cloud APIs like GPT-3 and GPT-4. Local deployment offers key benefits: enhanced privacy, cost savings over time, offline capability, reduced latency, and customization flexibility.

Recent models optimized for efficiency—such as Meta’s LLaMA 3 series, Mistral AI’s Mistral-7B, and Alibaba’s Qwen 2.5 series—demonstrate near state-of-the-art performance while being runnable on personal computers with sufficient RAM and GPUs. Smaller variants enable deployment even on low-resource devices like smartphones and Raspberry Pi.

Local hardware tiers vary from low-end (8–16GB RAM, CPU-only) supporting small models (7B parameters), mid-range (16–32GB RAM, GPUs with 6–12GB VRAM) handling medium models (up to 30B), to high-end systems (≥64GB RAM, GPUs with ≥16GB VRAM) capable of running large models (70B parameters) at usable speeds. Software advancements, including libraries like llama.cpp and user-friendly interfaces such as Ollama and LM Studio, have greatly simplified local model use.

Optimization strategies—most notably quantization to 4-bit precision—allow large models to fit into limited memory with minimal loss in performance. Architectural improvements like Grouped-Query Attention further improve efficiency.

However, local deployment faces challenges: high computational demands, power consumption, heat generation, and upfront hardware costs. While local use can be more cost-effective for frequent users, cloud APIs may remain preferable for very large models or infrequent use.

Conclusion

As of late 2024, running large language models (LLMs) locally on consumer devices has become increasingly viable, thanks to advancesinopen-sourcemodelslikeLLaMA3,Mistral,andQwen2.5,alongwithoptimizationtechniquessuchasquantization. High-end consumer PCs can now run 70B parameter models with near-GPT-4 performance, while mid-range systems handle 7B–13Bmodelseffectively.ToolslikeOllama,LMStudio,andllama.cpphavesimplifieddeployment,makingbenefitslikeprivacy, offlineaccess,costsavings,andcustomizationmoreaccessible.However,challengesremain,includinghighmemoryrequirements, varying performance, energy consumption, and greater setup complexity compared to cloud services. Despite this, the growing ecosystemandimprovinghardwaresignalthatlocalLLMsarebecomingamainstream,empoweringalternativeforusersseeking control over their AI experiences.

References

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Copyright

Copyright © 2025 Prof. Pallavi Agrawal, Krutarth Patil, Hitesh Chaudhari. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.