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For running AI models locally, memory matters more than anything else, because the whole model has to fit in it. For the biggest models, the MacBook Pro with M5 Max leads, with up to 128GB of unified memory the GPU can use. On Windows, a laptop with an RTX 5090 (24GB of video memory) or RTX 5080 (16GB), such as the Lenovo Legion Pro 7i Gen 10, is the fastest for models that fit. The ASUS ProArt PX13 brings 128GB of shared memory to Windows.
Check MacBook Pro M5 Max models on Amazon →
How we put this together: this guide is based on manufacturers’ specifications, NVIDIA’s and LM Studio’s published requirements, and published reviews (linked below). It is not based on hands-on testing by iTech Level, and the model-size figures are rough rules of thumb, not benchmarks. See how we choose products.
How much memory do you need to run an LLM locally?
Apps like LM Studio and Ollama load the model into memory: the graphics card’s video memory (VRAM) on a Windows gaming laptop, or the shared unified memory on a Mac or an AMD Ryzen AI Max laptop. If the model doesn’t fit, it either won’t load or spills into slower system memory. Most people run “quantized” models compressed to around 4 bits per weight, which as a rough rule needs about half a gigabyte per billion parameters, plus a few gigabytes for the conversation context:
| Memory for the model | Roughly fits (4-bit) | Example laptops |
|---|---|---|
| 8GB | Models up to about 7–8B parameters | RTX 5050/5060/5070 (8GB) laptops |
| 12GB | Up to about 12–14B | RTX 5070 Ti laptops |
| 16GB | Up to about 20B | RTX 5080 laptops |
| 24GB | Up to about 30–32B | RTX 5090 laptops; Macs with 32GB+ |
| 64–128GB unified | 70B-class models and larger | MacBook Pro M5 Max; Ryzen AI Max+ 395 laptops |
Two more things matter. Memory bandwidth sets how fast a model answers once it’s loaded, which is why an NVIDIA RTX card with its own fast GDDR7 memory is quick for models that fit. And software support: LM Studio supports Apple silicon Macs and Windows PCs, recommending 16GB+ of RAM and at least 4GB of dedicated VRAM on Windows (LM Studio).
Best laptops for local LLMs compared
| Laptop | Best for | Memory for models | Platform | Price | Buy |
|---|---|---|---|---|---|
| MacBook Pro 14/16 (M5 Max) | Largest models | Up to 128GB unified, 460–614GB/s | macOS | Premium | Check price |
| Lenovo Legion Pro 7i Gen 10 | Fastest on Windows | 16GB (RTX 5080) or 24GB (RTX 5090) VRAM | Windows | RTX 5080 has sold for $2,599.99 | Check price |
| ASUS ProArt PX13 GoPro Edition | Big models on Windows | 128GB unified, up to 96GB for the GPU | Windows | $3,999 | Check price |
| Lenovo Legion Pro 5i Gen 10 (RTX 5070 Ti) | Best value | 12GB VRAM | Windows | Check price | Check price |
| MacBook Air 13 (M5), 24 or 32GB | Small models, light and silent | Up to 32GB unified, 153GB/s | macOS | From $1,299 (16GB) | Check price |
The best laptops for running LLMs locally in 2026
1. MacBook Pro with M5 Max: best for large models
Apple’s unified memory lets the GPU use most of the system’s memory, and the M5 Max supports up to 128GB of it, with 460–614GB/s of bandwidth depending on the GPU configuration (Apple). That’s far more memory for a model than any laptop graphics card, so 70B-class models that won’t fit on a 24GB RTX 5090 can run here. It’s also quiet and lasts much longer on battery than a gaming laptop, and the M5 Max models start with 2TB of storage, enough for several large models. Good to know: memory can’t be upgraded, so buy the amount you’ll need, and an RTX 5090 is faster for models small enough to fit in its 24GB.
Check MacBook Pro M5 Max models on Amazon →
2. Lenovo Legion Pro 7i Gen 10: fastest on Windows
NVIDIA’s RTX 5090 laptop GPU has 24GB of GDDR7 and the RTX 5080 has 16GB (NVIDIA), and the Legion Pro 7i offers both. It runs the RTX 5080 at the full 175 W next to an Intel Core Ultra 9 275HX; Notebookcheck rates it 88% and calls the Gen 10 the most refined model in the series (Notebookcheck). In May 2026, Best Buy had the RTX 5080 version at $2,599.99. The RTX 5090 version costs considerably more; Notebookcheck lists it at about €4,100 (Notebookcheck). NVIDIA’s CUDA is also the best-supported platform for AI tools. Good to know: it’s a heavy desk machine with a 400 W power brick, and 16–24GB caps the model size well below a 128GB Mac.
Check the Legion Pro 7i on Amazon →
3. ASUS ProArt PX13 GoPro Edition: big models on Windows
AMD’s Ryzen AI Max+ 395 works like Apple’s chips: the CPU and Radeon 8060S graphics share one pool of memory. In the ProArt PX13 GoPro Edition, that’s 128GB, and up to 96GB can go to the graphics, which is enough for much bigger models than any laptop RTX card. It’s a 3.1 lb, 13.3-inch convertible with a 3K OLED touchscreen, at $3,999 (The Gadgeteer). Good to know: AMD’s software support for AI tools is less mature than NVIDIA’s CUDA, so check that your apps support it, and Tom’s Guide notes the screen is only 60Hz.
Check the ProArt PX13 GoPro Edition on Amazon →
4. Lenovo Legion Pro 5i Gen 10 (RTX 5070 Ti): best value
An RTX 5070 Ti’s 12GB of video memory is the sweet spot for 7–14B models, which cover a lot of everyday chat and coding use. Lenovo runs it at the full 140 W in the Legion Pro 5i, with a 16-inch 2.5K OLED screen, and in Notebookcheck’s testing the 5070 Ti version was up to 25% faster in games than the RTX 5070. See our best RTX 5070 Ti and 5080 laptops guide for more options. Good to know: avoid 8GB RTX 5070 models if local AI is the main reason you’re buying.
Check the Legion Pro 5i (RTX 5070 Ti) on Amazon →
5. MacBook Air 13 (M5) with 24 or 32GB: for small models
If you want to try small local models on an everyday laptop, a MacBook Air M5 with 24GB or 32GB of memory runs them silently and lasts all day. It starts at $1,299 with 16GB; configure more memory if AI is part of the plan, since it can’t be upgraded later (Apple). Good to know: it has no fan, so long generations slow it down, and the base M5’s 153GB/s of memory bandwidth makes it much slower than an M5 Max or RTX card. See our MacBook Air M5 page.
Check the MacBook Air (M5) on Amazon →
Tips before you buy
- Decide on the model size first, then buy the memory to fit it with room to spare for longer conversations.
- Check the graphics wattage on Windows. The same RTX chip runs much slower in thin laptops; our laptop GPU power database lists the wattage of 200+ configurations.
- Buy plenty of storage. Models are large files; 2TB is a sensible minimum if you’ll keep several.
- Don’t count on the NPU. The small AI accelerators in “Copilot+” laptops are built for low-power features, while most local LLM apps run on the GPU or CPU.
Our verdict
Buy for memory. The MacBook Pro with M5 Max and up to 128GB of unified memory runs the largest models of any laptop. On Windows, an RTX 5090 (24GB) or RTX 5080 (16GB) laptop such as the Legion Pro 7i is fastest for models that fit, the ASUS ProArt PX13’s 128GB of shared memory takes much bigger ones, and an RTX 5070 Ti (12GB) laptop is the value pick. A MacBook Air with 24–32GB is fine for experimenting with small models.
Frequently asked questions
What is the best laptop for running local LLMs?
For the largest models, a MacBook Pro with M5 Max and up to 128GB of unified memory. On Windows, an RTX 5090 (24GB) or RTX 5080 (16GB) laptop such as the Lenovo Legion Pro 7i is fastest for models that fit in its video memory.
How much RAM do I need to run an LLM on a laptop?
As a rough rule, a 4-bit model needs about half a gigabyte per billion parameters plus a few gigabytes for context: about 8GB for 7–8B models, 12GB for up to 14B, 24GB for around 30B, and 64GB or more for 70B-class models.
Is a Mac or a Windows laptop better for local AI?
A Mac can give a model far more memory, up to 128GB on the M5 Max. A Windows laptop with an NVIDIA RTX card is faster for models that fit in its 8–24GB and has the widest software support through CUDA.
Can a gaming laptop run LLMs?
Yes. Gaming laptops with NVIDIA RTX graphics run local models well; the video memory decides how big a model fits. A 12GB RTX 5070 Ti or 16GB RTX 5080 is a good target.
Do I need an NPU to run AI locally?
No. Most local LLM apps such as LM Studio and Ollama run on the graphics card or CPU. The NPUs in Copilot+ laptops are aimed at smaller, low-power AI features.
