Theme
AI Guide
AI Hardware: what you actually need
Start with the AI you want to run. Cloud tools may need no special hardware at all, while local models depend on the model size, available memory, how much conversation or text they keep in working memory, and how quickly you want them to respond.
Use this as a practical first pass. If the decision affects money, accounts, private data, work, or someone else's trust, check original sources and slow down before acting.
Cloud AI
Your current computer may already be enough.
If the model runs on a provider's servers, a working browser or app and a reliable connection usually matter more than a new graphics card.
Local AI
Memory sets the practical boundary.
The model and its working data need room in system RAM, GPU memory, or both. Larger models and more text kept in working memory usually need more.
Before buying
Compatibility comes before capacity.
A fast part is useless if the computer cannot accept, power, cool, or use it. Check the exact device specifications first.
Start here
First decide: cloud AI or local AI?
Cloud AI runs the heavy model on someone else's infrastructure. Your device sends the request and displays the result. If that is all you want, upgrading RAM or adding a large graphics card may not improve the service itself.
Local AI downloads a model and runs it on your own computer. That can provide more control over where the model runs, but it also makes memory, storage, heat, power, and software support your responsibility.
Some setups mix both paths. A desktop app may run one model locally while still offering optional online models or services. Check the selected model and connection path instead of assuming the whole app is local or cloud-based.
Memory
RAM and VRAM do different jobs
System RAM is shared by the operating system, ordinary apps, and local AI software. A dedicated graphics card has its own VRAM; integrated and unified-memory systems may share memory instead.
When local software loads a model, the model and its working data use memory. The exact amount depends on the model, how much it has been compressed, how much text it keeps in working memory, the software engine running it, and the hardware.
LM Studio currently recommends at least 16GB of RAM for Windows and at least 4GB of dedicated VRAM. Treat those as an entry point for its app, not a promise that every model will run well. More demanding models may need much more memory.
16GB RAM: an entry point for smaller local models and light experiments, with limited headroom once other apps are open.
More system RAM: gives local models and other open apps more room, but the useful amount depends on the intended model and workflow.
High-memory setups: make sense only when the chosen model or measured workload genuinely needs the room; they do not improve ordinary cloud AI by themselves.
Dedicated GPU memory: compare the amount only after confirming that the app supports the graphics hardware and the intended workload needs it.
Optional shopping links
Compare current memory and storage options
Affiliate disclosure: Some links on this page are affiliate links. LifeHubber may earn a commission if you buy through them. As an Amazon Associate, LifeHubber earns from qualifying purchases.
These links open Amazon search results, not a promise that every result will fit. Check the exact RAM generation, form factor, capacity limit, storage interface, physical size, and return terms for your computer before buying.
See 32GB DDR5 desktop RAM options on Amazon See 64GB DDR5 desktop RAM options on Amazon See 2TB NVMe SSD options on Amazon
Check Amazon for the current price, availability, delivery, and any offer. LifeHubber does not display or track those changing details.
Storage
Leave room for model files
Local model files can take several gigabytes each, and keeping several versions adds up quickly. The llama.cpp project shows how making a smaller compressed model version, often called quantization, can shrink a model file substantially while trading size, speed, and accuracy.
Storage holds downloaded model files, but it does not replace the memory needed while a model is loaded. Keep free space for the operating system, updates, temporary files, and the models you actually plan to use.
Graphics
Buy a GPU for a real workload, not just the AI label
Some local AI software can use a supported GPU. Check which kinds of graphics hardware the software supports before choosing a card.
If the selected software uses a dedicated GPU, check the app's published VRAM requirements and compare them with the card specifications instead of relying on the gaming name alone.
A graphics-card upgrade can also require a suitable power supply, the right power connectors, enough physical space, adequate cooling, and a compatible motherboard slot. Small desktops, all-in-one computers, and laptops may not support this kind of upgrade at all.
Optional shopping link
Compare graphics-card options
If your chosen local AI software can use a dedicated GPU, compare its published hardware requirements with the card specifications. Search results may include cards that do not fit your case, power supply, motherboard, software, or budget.
See graphics-card options on Amazon
Affiliate link: LifeHubber may earn a commission. Check compatibility and the current Amazon price before buying.
Three paths
Choose the smallest path that does the job
Hardware advice ages quickly when it starts with a shopping list. A better decision starts with one model, one app, and one real task.
Use what you have: try a small compressed model version, limit how much text it keeps in working memory, close memory-heavy apps, and learn whether local AI is useful to you before spending.
Make a sensible upgrade: add compatible RAM or storage when testing shows a clear limit and the device supports the change.
Build for heavier local AI: choose the model family and how much text it needs to keep in working memory first, then size RAM, VRAM, storage, power, cooling, and budget around that measured need.
Compatibility check
Check these details before buying anything
Use the computer manufacturer's manual or specification page as the starting point. If the device is custom-built, check the motherboard, case, power supply, and operating-system requirements separately.
The exact computer or motherboard model number.
Whether memory is replaceable, soldered, or already using every slot.
Supported RAM generation, form factor, speed, and maximum capacity.
Supported storage interface and physical size, such as the correct M.2 format.
Available graphics-card slot, case clearance, power capacity, connectors, and cooling.
Operating-system, driver, and local-AI software support for the chosen hardware.
Seller, warranty, return terms, current price, delivery, and regional availability.
AI Guide note
How to use this guide
AI Guides are general editorial guidance, not professional advice or guarantees about accuracy, safety, suitability, performance, or outcomes. Tools, terms, prices, features, and laws can change. Check important details against original sources, product terms, reliable references, and qualified help where needed.
Source trail
Check current requirements and terms
Hardware support, software requirements, product availability, and affiliate-program rules can change. Check the current original sources before relying on a specific configuration or purchase.
What to explore next
Match the hardware to a local AI path
Compare local tools and one straightforward access route before choosing the models and workloads your computer needs to support.