Best GPU for running multiple AI models simultaneously?

Question

Hey everyone — I’m trying to figure out the best GPU setup for running multiple AI models at the same time on one machine. Specifically, I’m often running an LLM for chat + a separate embedding model, and sometimes a small vision model in parallel (so 2–3 models active at once). Right now my main pain point is VRAM: once I load more than one model, things either crawl or I start getting out-of-memory errors, even if each model runs “fine” by itself.

I’m not sure whether the smarter move is getting a single GPU with a lot of VRAM (like 24GB+), or going with two cheaper GPUs and splitting workloads. I’m also confused about how much multi-model performance depends on memory bandwidth vs just raw VRAM, and whether consumer cards handle this well compared to workstation cards.

Constraints: I’d like to stay under about $1,500 if possible, and I care more about stable multi-model throughput than max single-model benchmark scores.

For people who’ve actually done this: what GPU (or GPU combo) would you recommend for running multiple AI models simultaneously, and why?

iqtdznwwkp · Accepted Answer

For your situation, I’d go single big-VRAM card: NVIDIA GeForce RTX 4090 24GB if you can snag it near your budget. In my box, juggling an LLM + embeddings + a tiny vision model is mostly a VRAM fragmentation/overhead problem, not compute, and 2x consumer GPUs is kinda annoying (PCIe splits, no NVLink, manual device pinning). Bandwidth matters, but running outta VRAM is the hard stop. gl!

lugdfonwed · Answer

Yo, been there — imo for 2–3 models, one big-VRAM GPU was WAY smoother than 2 cards; multi-GPU added overhead + weird OOM/fragmentation. Biggest win was leaving headroom + pinning models, not chasing max TFLOPs.

jvhgtsjyds · Answer

Helpful thread 👍