Is the RTX 4090 the best choice for large language models?

Seconding the recommendation above. The NVIDIA GeForce RTX 4090 24GB GDDR6X is a beast, but honestly, dropping $1,700+ is a hard pill to swallow, especially when you consider the safety risks of running such a power-hungry card in a standard home setup. I actually had some issues with my first unit because of the 12VHPWR connector melting—it's a real concern that people don't talk about enough when they're chasing those inference speeds.

If you're on a budget, you could look for a used NVIDIA GeForce RTX 3090 24GB GDDR6X. You still get the 24GB VRAM for those Llama 3 models, and you can usually find them for under $800 now. It’s not as fast as the 4090, but it avoids the premium price and that sketchy new power connector. Just make sure your PSU is top-tier because those spikes are no joke. tbh, the 4090 is great but for hobbyist work, the value just isn't there for me personally anymore. Good luck!

In my experience, the jump to a NVIDIA GeForce RTX 4090 24GB GDDR6X is basically the only way to go if you want serious speed for Llama 3 or Mistral. I've tried many setups over the years, and honestly, the memory bandwidth on the 4090 is what makes long context windows actually usable without it feeling like a slide show.

Here is how I see the options right now:
* **The Gold Standard:** NVIDIA GeForce RTX 4090 24GB - Best performance, but expensive as hell.
* **The Budget Alternative:** 2x NVIDIA GeForce RTX 3090 24GB - You get 48GB of VRAM for roughly the same price, which lets you run way bigger models, but it's slower and uses massive power.
* **The Middle Ground:** NVIDIA GeForce RTX 4080 Super 16GB - It's faster than the 30-series, but that 16GB VRAM limit is gonna give you OOM errors again real quick.

If you can swing the cash, the 4090 is highkey worth it for the 4-bit quantization speeds alone. It's literally night and day compared to older cards... gl with the build! 👍

Seconding the recommendation above. I've been tinkering with local LLMs for a few years now, and the NVIDIA GeForce RTX 4090 24GB GDDR6X is honestly in a league of its own when it comes to consumer gear. The 1,008 GB/s memory bandwidth is the secret sauce—it makes a massive difference for those long context windows that usually crawl on slower cards.

But if the $1,700 price tag is too steep, I've seen a lot of people go the dual NVIDIA GeForce RTX 3090 24GB GDDR6X route. You can usually find them used for around $700-800. Putting two of those together gives you 48GB of VRAM, which lets you run much bigger models (like Llama 3 70B) at higher precision than a single 4090 ever could. It's definitely a bit of a power hog and runs hot, but for value-for-money, it's a solid alternative. If you just want raw speed and efficiency tho, stick with the 4090. It's basically the gold standard for a reason lol. gl!

tbh I tried the DIY route by bridging two cheaper cards to save cash... bad move. It was a total headache with driver issues and my power supply almost melted!! It's realy not as good as expected for the price. I eventually got my current setup and it stops the OOM errors, but the cost is just painful. If you're gonna do it, dont forget to get a pro to check your power setup cuz the risk is high.

In my experience, comparing brands, my current card's CUDA performance is just better than the competition for LLMs. The memory bandwidth literally saved my workflow. Definitely worth it tho!

100% agree

TL;DR from this thread: the consensus is that bandwidth makes the top-tier consumer card the king for avoiding OOM issues. honestly, it's a beast for Llama 3... but before u drop that much cash, i gotta ask: are u mainly just running inference or actually doing heavy training? also, what's ur power supply situation? these things pull serious juice and run hot, so you definately gotta plan for the heat... right?

Can confirm

Saving this whole thread. So much good info here you guys are awesome.