Budget GPU Options for Local LLM Inference Under $700
A user in the LocalLLaMA community is seeking advice on choosing a budget GPU under $700 to run quantized ~30B parameter LLMs locally. They are weighing options such as a modified NVIDIA RTX 2080 Ti upgraded to 22GB VRAM against server-grade hardware like the AMD Instinct MI50 with 32GB VRAM. Running ~30B parameter models locally traditionally requires expensive workstation GPUs, pushing budget enthusiasts toward hardware modifications and gray-market enterprise accelerators. This highlights the growing demand for affordable high-VRAM solutions to lower the barrier to entry for open-source AI inference. Modified RTX 2080 Ti cards reach 22GB VRAM by swapping 1GB GDDR6 chips with 2GB chips and adjusting PCB strap resistors for BIOS support. In contrast, enterprise accelerators like the AMD MI50 provide 32GB VRAM at low costs but lack display outputs, require custom cooling setups, and depend on AMD's ROCm software stack.
## BACKGROUND
Local Large Language Model (LLM) inference is primarily constrained by Video RAM (VRAM) capacity, which must store both the model weights and key-value context caches. Quantization techniques like Q4_K_M compress model weights to 4-bit precision, reducing memory footprint significantly while retaining good output quality. Because modern high-VRAM consumer GPUs remain pricey, enthusiasts often modify older cards like the RTX 2080 Ti or repurpose enterprise accelerators to achieve high VRAM at a fraction of the cost.