Local LLM laptop budget 2.5-5k
Posted by 0800otto@reddit | LocalLLaMA | View on Reddit | 63 comments
Hello everyone,
I'm looking to purchase a laptop specifically for running local LLM RAG models. My primary use cases/requirements will be:
- General text processing
- University paper review and analysis
- Light to moderate coding
- Good battery life
- Good heat disipation
- Windows OS
Budget: $2500-5000
I know a desktop would provide better performance/dollar, but portability is essential for my workflow. I'm relatively new to running local LLMs, though I follow the LangChain community and plan to experiment with setups similar to what's seen on a video titled: "Reliable, fully local RAG agents with LLaMA3.2-3b" or possibly use AnythingLLM.
Would appreciate recommendations on:
- Minimum/recommended GPU VRAM for running models like Llama 3 70B or similar (I know llama 3.2 3B is much more realistic but maybe my upper budget can get me to a 70B model???)
- Specific laptop models (gaming laptops are all over the place and I can pinpoint the right one)
- CPU/RAM considerations beyond the GPU (I know more ram is better but if the laptop only goes up to 64 is that enough?)
Also interested to hear what models people are successfully running locally on laptops these days and what performance you're getting.
Thanks in advance for your insights!
Claude suggested these machines (while waiting for Reddit's advice):
- High-end gaming laptops with RTX 4090 (24GB VRAM):
- MSI Titan GT77 HX
- ASUS ROG Strix SCAR 17
- Lenovo Legion Pro 7i
- Workstation laptops:
- Dell Precision models with RTX A5500 (16GB)
- Lenovo ThinkPad P-series
Thank you very much!
SomeOrdinaryKangaroo@reddit
If you're going to do laptop then mac is really the only viable option, especially if you want to run bigger models.
Red_Redditor_Reddit@reddit
I would probably agree with this one op. I'm not familiar with macs, but I'm pretty sure they're not going to be anywhere near obtuse as a gaming laptop. You will also be able to run much larger models with any kind of speed. The gaming laptop is going to be much more limited with the vram. The only thing I'm not sure about is prompt processing because I've never used a Mac and I don't know how they do in that regard.
AmazinglyNatural6545@reddit
So why are you talking about all of this? What's the point if you don't have any real experience.....
Red_Redditor_Reddit@reddit
I have experience using a computer....
Batinium@reddit
This, my m1 max wirh 64gb ram can run models that my 3080 can't run because of VRAM.
0800otto@reddit (OP)
Unfortunately it can't be a mac. I would 100% go with an M4 but it has to be a windows laptop.
0xFatWhiteMan@reddit
macbook
Legal_Cupcake9071@reddit
Why and which?
Quiet-Chocolate6407@reddit
OP says "windows OS" though
0xFatWhiteMan@reddit
OP is wrong
Willing_Landscape_61@reddit
I'm cheap so I would buy a used laptop that is light to carry it around. And I would buy a used desktop or server with one or two used 3090. Not what you wanted but do the math on pp for your RAG.
AXYZE8@reddit
70B Llama on M3 Max (400GB/s) does 8tk/s. Windows machine at this price will be like 130GB/s, so 1/3 of memory bandwidth.
Forget about it. CPU+GPU is not a solution for these gaming beasts, after 15seconds you will hear why.... and you'll be forced to hear that jet engine for long time.
Drop to 32B and now you have some options. With 24GB VRAM you can use 32B models at Q4 with nice context size. Here you use just GPU, RTX 5090 Mobile does 896GB/s. Way more comfortable and way quicker.
New 32B models are beasts, that Llama 3 70B you wanted is worse than Qwen3 32B/GLM4 32B/Gemma 3 27B for pretty much anything.
For Windows laptops I can recommend only GPU inference. CPU and RAM doesnt matter. That Ryzen AI 9 that people recommend is nowhere fast enough to give you acceptable speeds at 70B, its a great CPU for little efficient machines to run MoE models or smaller 7+14B models.
RTX 5090 Mobile + 32B model = you are happy
AXYZE8@reddit
And btw GPU-only inference with RTX 5090 wont give you good battery life. Your battery will be dead in hour.
You need better battery life - get MacBook and use VM with Windows if you really need Windows.
M4 Max has 2x better memory bandwidth than top of the line Ryzen AI 9... and on top of that it eats less power during inference too, so in the end its 3x more efficient.
Now think about that 3x in terms of heat, noise, battery life.
SkyFeistyLlama8@reddit
It's all about the flavor of jet engine that you want. Ryzen 9 AI, M4 Max, Snapdragon X Elite, RTX 50xx, all will give you decent to excellent performance but they will all run very hot and forget about battery life.
Memory bandwidth isn't everything. Vector processing affects prompt processing speed and time to first token, so don't be like the Ryzen fanboy idiot who quoted fast token generation numbers on a 30 token context. Why do people keep ignoring this?
A RTX5080 mobile GPU has up to 4x the prompt processing capability of an M4 Max so your time to first token will be 4x less. If it takes 4 seconds on an M4 Max, it only takes 1 second on the RTX. If you're handling large documents at huge context sizes, say 32k, then what would take the Mac 20 minutes to process would only take 5 minutes. You can get around that by keeping that document context loaded in RAM but on the next cold start, you'll still have to wait 20 minutes.
AXYZE8@reddit
Can you please provide any source on that?
I have difficulty finding proper LLM benchmarks (something better than 'hello' as the whole prompt) for mobile RTX5080, so let's see the GPU performance in other benchmarks,
Here's RTX 5090 Mobile vs M4 Max
https://www.youtube.com/watch?v=qCGo-6fTLPw&t=671s
It's 110k for M4 Max, while RTX 5090 has 183k plugged in and 160k on battery.
It's 30 seconds to fill 5k
https://www.reddit.com/r/LocalLLaMA/comments/1jw9fba/macbook_pro_m4_max_inference_speeds/
There's even your comment there "MBP Max gets close to that which is surprising and it's doing that at half the power draw".
Any source for that 32k = 20 minutes please?
GPU in M4 Max has hard limit of 60W. RTX 5090 laptop from video above has TDP of 165W. This is night and day difference, especially if you cannot fit a model into GPU and you need to do CPU+GPU inference. Looking at gaming power usage (CPU+GPU usage) it's 283W for that RTX 5090 laptop.
Now, MacBook Pro 16 has 99Wh battery so if your average power consumption is 33W then you have 3hrs of battery life. 33W is realistic average if you read the outputs of LLM, so 3h battery life. If you are doing shorter sessions with LLM and primarly sitting in IDE/browser then you can get more. I would say this is okay battery life and okay amount of heat.
283W is a different story, at 155W average usage that 99Wh will last 38 minutes.
CPU+GPU is really "forget about battery". Pure GPU inference with RTX 5090 is doable, CPU can idle and that GPU will process it faster, so it can idle too, but that idle is 2x higher than MacBook idle. In the end MacBook is still king if he wants battery life, I wouldn't put M4 Max in the same basket as RTX50xx like you did
SkyFeistyLlama8@reddit
Someone needs to collate all this long context info into the llama.cpp Github page like what ggerganov does for MacBook inference.
As for the 20 minute thing, that was a figure of speech. I vaguely remember someone with an M3 Max saying it took 20 minutes to process a 100k token prompt on a 32B model, or maybe it was a 70B. Again, all this info needs to be in one place.
NVIDIA GeForce RTX 5070, I assume it's not the mobile version: https://www.localscore.ai/accelerator/168
Apple M4 Max 12P+4E+40GPU: https://www.localscore.ai/accelerator/6
See the time-to-first-token result for Qwen 14B taking 4x longer on the M4 Max.
The MacBook Pro M4 Max is great if you want a good all-around computer that has long battery life and is also good at general LLM usage. An RTX5080 or 5090 laptop would be a better choice if the user wants to focus more on LLMs and AI in general, at the expense of portability and battery life.
Interesting MBP16 power consumption numbers there. I'm getting max 65W on a Snapdragon X Elite for CPU inference while getting stupid amounts of heat and maybe 2 hours battery life. For GPU inference, it's only 25W with much less heat, and I'm getting 75% performance compared to CPU. The best part is being able to load huge models like Drummer Nemotron 49B and Llama Scout on a laptop.
0800otto@reddit (OP)
thank you! so much insight in this answer I appreciate your comment.
Comms@reddit
Ok, hear me out: separate the two functions.
Buy a decent, efficient laptop with the primary laptop qualities you want.
Build a headless desktop with the best GPU(s) you can afford.
Use tailscale
You'll get access to more powerful hardware but your laptop will no bear the brunt of the processing. I say this as someone who has this at home. I have an unraid server with dual GPUs. I have tailscale set up and, as long as I have a connection, I can run anything I want off my laptop through my server.
0800otto@reddit (OP)
Thank you for your input, this sounds like the best path forward I don't want to overpay for a laptop but I can run 13b models on much cheaper system that can become very powerful when talking to the "mainframe". I'll look up tailscale, thank you!
If you have other resources you think could help me get this set up, im all ears!
Comms@reddit
Unraid is a really easy server OS for people who don't want to fuck around with setting up their own linux box. Install in a thumb drive, plug it into a box, and it sets up your server. Has a nice app database for deploying dockers. Ollama, llama.cpp, etc. are all present. Also has front ends like Open webui or AnythingLLM ready to go. Also has a plugin for Tailscale. Very easy to run and deploy. It's not free but the license is reasonable.
netixc1@reddit
Why not buy a desktop workstation with a few 3090's. Slap proxmox and tailscale on it and get a cheap but decent laptop to acces everything anywhere as long there is a internet connection.
TheSpartaGod@reddit
portability, man
Cannavor@reddit
I'm not sure you're understanding what he's suggesting. You can still run the AI on the laptop using this method so it's perfectly portable and would be waaay more capable than any laptop on its own. It's actually more portable when you think about it because any time the laptop is unplugged you won't be able to get good performance out of it. This option avoids that and will let you run the AI on the laptop at full speed even on battery.
LevianMcBirdo@reddit
Also power draw
netixc1@reddit
Whatever, man
Evening_Ad6637@reddit
I hate those comments so much. Someone asking for A and there ALWAYS are guys asking back „why not X?“ instead of giving a helpful response.
If one asks for a book please for gods sake don’t recommend a kindle. If one asks for spaghetti, don’t recommend a cheeseburger. If one asks for a laptop don’t recommend a fucking gigantic workstation.
Cannavor@reddit
IMO it would make more sense to build a dedicated AI server with a 3090 cluster then just connect to that using your laptop. Get a cheap laptop with good battery life for note taking and stuff. It doesn't make sense to try and cram a high end GPU in a laptop which will never be able to be upgraded.
LastikPlastic@reddit
If you need power - thinkpad P series will be a good solution, but like gaming laptops it is a pumping only one branch of development, I would look in the area of less productive, but lightweight solutions.
If you are considering a macbook, look at what you can buy for your money with
1) more memory (unified),
2) macbook model pro
3) pro model processor
yes, it won't produce 30tk/s in models that fit fully into memory, but it will be versatile and lightweight, although hot (btw I recommend use custom cooling plans, because stock temperatures are a nightmare)
nostriluu@reddit
Maybe just get any laptop you like with USB4 and an eGPU, you could do that for $2k. But I don't think you'll be able to run 70b with large contexts well on any system unless you have lots of fast VRAM.
Herr_Drosselmeyer@reddit
At Q4 with a decent size context, about 50-60GB. Runs fine on my dual 5090 system, won't run on a laptop with a 4090. You'd have to go down to Q2 or thereabouts, at which point it's not really worth running, at least imho.
Your sweet spot for any setup with 24GB of VRAM are 20-30b models.
InterstellarReddit@reddit
I would go RTX 5090 laptop and you will still have $1000 left over.
HistorianPotential48@reddit
do these have burning issue like the desktop ones? i am looking for a good device for generating anime women images too.
InterstellarReddit@reddit
They do not
random-tomato@reddit
To add to this, with a 5090 (32GB VRAM) you could run 27/30/32B models at Q4 or Q5 with still a lot of room for context. Might be good for RAG.
AXYZE8@reddit
Laptop RTX 5090 has 24GB with 50% of memory bandwidth.
Its more like desktop RTX 5080, but with 3GB modules instead of 2GB
random-tomato@reddit
Oops! I didn't know they did that, thanks for correcting me!
Rich_Repeat_22@reddit
AMD AI 395 based laptop with 128GB RAM.
In your budget the Asus Z13 hybrid is the cheapest option and goes for around $2700. There is also the HP Zbook which is classed as workstation, however that's way more expensive for the same product. Though if you can find the 128GB version for the same money as the Z13 get it. (around $2700).,
Using Windows can allocate up to 96GB to VRAM and on Linux 110GB. Make sure you do that, Windows cannot auto-allocate the VRAM like Macs.
SkyFeistyLlama8@reddit
I have to say no.
If you don't wait to wait minutes for prompt processing to finish on a long document, then avoid anything with an APU or integrated GPU. That means crossing out Intel, AMD, Apple and Qualcomm from the list. Those are all fine with short contexts but they fail miserably once you start using long contexts.
You need lots of RAM bandwidth and lots of high-performance matrix processing to handle documents like scientific papers and large code bases, which only means one thing: a discrete GPU. And that means Nvidia, preferably with the latest 50xx GPU with as much VRAM as you can afford, because you want to run a smarter model like a 14B or 32B instead of a lobotomized idiot 3B that's barely good enough for classification.
I don't mean to be discouraging. I'm a big fan of laptop inference and I use Snapdragon and Apple Silicon laptops for this, but I know what their limitations are.
Rich_Repeat_22@reddit
What? Have you seen the speed of the likes of GMK X2 in real time?
And with Vulkan. ROCm support was released yesterday.
SkyFeistyLlama8@reddit
What's the long context performance on a 32B model, like using 16k or 32k tokens?
Rich_Repeat_22@reddit
Ask him
https://youtu.be/UXjg6Iew9lg
SkyFeistyLlama8@reddit
You have to be kidding me. Try harder.
The reviewer uses a 4096 token default context but his inputs are tiny: "请模仿辛弃疾的青玉案再写两首,表达同样的意境"
That's less than 30 tokens! Try doing document summarizing and reasoning on the Google AlphaEvolve PDF which has *52,000 tokens.
Rich_Repeat_22@reddit
Dude, the guy wants a laptop, not to setup an AI sever.
No_Conversation9561@reddit
I have an Asus Z13 not the AI 395 one. I would say this laptop is a pain in the ass if you’re a college student who like things on the go.
It is too heavy to use it as tablet and too clumsy as a laptop. It tries to be both and achieves at being neither.
itis_whatit-is@reddit
What’s the largest models you can run and how fast?
Rich_Repeat_22@reddit
I am intrigued now, since all 13" tablets have the same weight, around 1.2kg, how this one is heavy? 🤔
AXYZE8@reddit
Huh? iPad Pro 13" is 582g, bigass Samsung S10 Ultra 14.6 is 723g
What are these 'all 13" tablets'?
SkyFeistyLlama8@reddit
The Surface Pro 11 is around 850g and it's a large tablet with a fan. With the type cover, it's less than 1.2 kg. I'd say it's at the upper limit of weight and size for a tablet.
Rich_Repeat_22@reddit
Dell, Asus, are around 1.2kg because.
No_Conversation9561@reddit
call me weak but holding 1.2 kg tablet with sharp edges gets old real fast
Important-Novel1546@reddit
damn, went through one with 2.5kg. And yes, it was a major pain in the back. My dumbass bought a gaming laptop, with 1 hour battery life. So it was also a major pain in the ass as well
MDT-49@reddit
If you're looking for good battery life and AI performance (i.e. efficiency), the MacBook with its unified memory is probably your best option.
However, since you want to use Windows, you could consider laptops with a Snapdragon X Series CPU or AMD Ryzen AI APUs.
I'm no expert, but I'd say that a 'legacy laptop' with a separate CPU and GPU is going to be less efficient, resulting in more heat and power consumption per token. I think a major issue right now, though, is software (inference engine) support. They all seem to be compliant with Microsoft Copilot+ and create their own software stack for AI applications, which doesn't offer much flexibility. For example, I don't think llama.cpp can currently use those CPUs/APUs optimally (i.e. using both the CPU, iGPU and NPU). I'm not entirely sure, though!
This might be a bit of an out-of-the-box idea and not exactly what you're looking for. But if I were in your position, I would wait until the market and support for those new APUs with fast memory bandwidth and NPUs has become more mature.
Instead for now, I would probably buy a refurbished laptop with > 32 GB RAM and the fastest DDR4 speed I can find (2666–3200 MHz). On this laptop I would run the largest MoE that's available at the moment and fits into the RAM. Right now, that would be Qwen3-30B-A3B.
SkyFeistyLlama8@reddit
Only get a Snapdragon X or MacBook if you know what you're getting into. I use both platforms for LLM inferencing and they're great for short contexts in ultralight laptops with long battery life, but they're absolutely miserable for querying long documents. You do not want to wait fifteen freaking minutes for a PDF to be tokenized and processed.
Snapdragon X: - CPU: use llama.cpp for the best performance but the laptop will get very hot - GPU: Adreno OpenCL works on llama.cpp, 50% less power consumption at about 75% of CPU performance - NPU: only works with ONNX models provided by Microsoft which have been modified to run using QNN (Phi Silica and DeepSeek Distill for now)
Apple Silicon: - CPU: don't bother - GPU: excellent performance using MLX, comparable to Snapdragon X CPU inference, but the laptop will get very hot - NPU: only Apple models for now
I agree with getting the most RAM you can get. These unified memory laptops are great for running larger, smarter models but at a slower speed compared to a discrete GPU. I'm running Nemotron 49B, GLM-4 32B and Qwen 3 30B MOE on my Snapdragon laptop with 64 GB RAM. On my MacBook Air with only 16 GB RAM, I can only use smaller models like Qwen 14B or Gemma 12B.
0800otto@reddit (OP)
thank you for your feedback these are good ideas!
prosetheus@reddit
anything with ryzen Ai 395, like the new Asus Flow x13 tablets, with 64 or even 128gb of unified RAM
Expensive-Paint-9490@reddit
You need to load both a text generation model and an embedding model at the same time for RAG.
The best available current option is GPU + CPU inference with recent MoE models. With 128 GB system RAM and 16 GB VRAM you can use Qwen3-235B-A22B at q4, which has a performance better than a 70B dense model. Factorying some context and the embedding model you would be better off with 24GB RAM, offerend only by the recent RTX 5090. 24GB RAM would give you the opportunity to run models in the 32B 4-bit category as well, fully on VRAM.
In Europe there is Santech.eu selling a Clevo with windows 11, RTX 5090, 128GB RAM, and even 2 thunderbolt 5 for 5,000 EUR. It's a 18" beast weighting almost 4 kg with just 5 hours of battery time.
A way better option is whatever laptop connecting through a tunnel to your home server, possibly with BMC. It gives you performence and portability. But of course is a very different solution.
Vaddieg@reddit
There's no windows laptop matching all your needs. Buy a separate box for AI things
Economy-Occasion-489@reddit
bro system 76 hands downs
Only-Letterhead-3411@reddit
Only logical option is macbook pro
A_R_A_N_F@reddit
Could you or anyone else please elaborate why Mac products are suggeted for local LLMs?
Baldur-Norddahl@reddit
It needs to be said that nothing compares to Macbook for local LLM currently. Maybe consider running Windows in a VM on a Macbook? Unless you simply have a strong dislike for Mac OS - however that is going to cost you in this instance.
It is not that I am a Apple fanboy. It is in fact a sad state and Apple really needs some real competition here.
You could consider splitting the workload and get a separate "personal AI server". There are a few options coming now, that would make this strategy a good one. AMD Strix Halo, Nvidia DGX Spark and of course Mac Mini/Mac studio. That way you could make better choices for the laptop (windows, weight, battery live, etc).
Tenzu9@reddit
Nothing with less than 48 gb of vram can realstically run a 70B model, not unless you are looking to get a suboptimal quant for it.
So either wait for Nvidia's GB10 workstations, Intel's 48gb graphic cards, run a dual 3090s with resource sharing or get a M4 pro laptop with either 64 or 128 ram.
No_Draft_8756@reddit
A Laptop with rtx 3090/4090 would let you run llama3.3 70b at 2.4bpw with exllama V2 pretty good. I am using also a normal desktop 3090 and get like 15t/s with tabby API.