Best Budget GPU for Local LLMs: Spend Less, Buy the Right VRAM Tier

The RTX 3090 is the cheapest way to get 24 GB of VRAM with CUDA support. On the used market it costs a fraction of the 4090 while offering the same VRAM capacity. For builders who want to run larger models and cannot justify the cost of a new GPU, the 3090 is the entry ticket to 24 GB inference.

At 936 GB/s bandwidth it is slightly slower than the 4090 and 7900 XTX, but the difference in token generation speed is modest - typically 10-15% slower for the same model. You still get CUDA, you still get 24 GB, and the Ampere architecture supports Flash Attention and most quantization formats through llama.cpp and ExLlamaV2.

The main compromises are generational. Ampere lacks FP8 support (that is an Ada Lovelace and Blackwell feature), so you lose one potential speedup for quantized inference. The 3090 also draws 350 W and runs warm, especially on reference coolers. An aftermarket model with a good cooler is worth the small price premium on the used market.

If you are experimenting with local LLMs and want to see what 24 GB VRAM unlocks without spending GPU-launch money, the used 3090 is the lowest-risk option. It handles everything from 7B to 35B models on GPU, and even 70B models with partial offloading. Just make sure the card you buy has been tested and has clean VRAM.

The RTX 4070 Ti Super is the cheapest new NVIDIA GPU that makes sense for local LLMs. At 16 GB GDDR6X with 672 GB/s bandwidth, it targets the same model range as the RTX 5080 (7B-13B models) but at a significantly lower price. If you are building a new system for local LLMs and your budget does not stretch to $999, this is where you land.

The 4070 Ti Super gets you into the Ada Lovelace generation with FP8 support, DLSS 3, and good power efficiency at 285 W. For inference specifically, FP8 is the feature that matters - it allows certain quantized models to run faster than they would on Ampere cards like the 3090, even though the 3090 has more VRAM.

Bandwidth is the limitation. At 672 GB/s it is noticeably slower than the 5080 (960 GB/s) or 4090 (1,008 GB/s). Token generation speeds for the same model will be lower. For smaller models (7B) this difference is less noticeable, but for 13B models the slower bandwidth becomes more apparent.

This card makes the most sense for someone building a new workstation who wants CUDA support, does not need to run 70B models, and wants to keep the total GPU cost reasonable. Pair it with 32 GB of system RAM and you can even offload larger models, albeit at reduced speed.

The RTX 5080 hits the price-performance sweet spot for local LLMs. At 16 GB GDDR7 with 960 GB/s bandwidth, it runs 7B models at or near their full potential and handles 13B models at 4-bit quantization comfortably. If your workflow centers on Llama 3.1 8B, Mistral 7B, or Phi-3 medium, this card delivers without the premium tax of the 5090.

GDDR7 memory is the key upgrade over the previous generation. The bandwidth is competitive with the RTX 4090 despite having less total VRAM, which means token generation speeds for models that fit in 16 GB are very fast. You are not sacrificing speed - you are sacrificing capacity.

Power draw is reasonable at 360 W with an 850 W PSU recommendation. That is within the comfort zone of most modern PSUs and cases, unlike the 5090 which needs a significant power infrastructure upgrade for many builders.

The limitation is 16 GB of VRAM. Models like Llama 3.1 70B at 4-bit quantization need roughly 38 GB, which does not fit. You can still run it with offloading to system RAM, but inference speed drops significantly. If your goal is running the largest models locally, step up to the 5090 or consider a used 24 GB card.