GPUs with 32 GB VRAM for Running LLMs
Browse 9 GPUs with 32 GB VRAM compatible with running LLM models locally. Compare VRAM, memory bandwidth, and AI performance. Top picks and budget guide
9 GPUsClear filters
| GPU | Price |
|---|---|
| RTX 509032 GBGDDR7 · 1,792 GB/s · 575 WNVIDIA · Blackwell · 2025 | about $5,376 |
| Radeon AI PRO R9700Pro32 GBGDDR6 · 640 GB/s · 300 WAMD · RDNA 4 · 2025 | about $1,299 |
| Arc Pro B70Pro32 GBGDDR6 · 608 GB/s · 230 WIntel · Xe2 · 2026 | about $949 |
| Arc Pro B65Pro32 GBGDDR6 · 608 GB/s · 200 WIntel · Xe2 · 2026 | about $910 |
| RTX PRO 4500 BlackwellPro32 GBGDDR7 · 896 GB/s · 200 WNVIDIA · Blackwell · 2025 | about $2,600 |
| RTX PRO 4500 Blackwell Server EditionPro32 GBGDDR7 · 800 GB/s · 165 WNVIDIA · Blackwell · 2026 | – |
| V100 SXM2 32GBPro32 GBHBM2 · 900 GB/s · 300 WNVIDIA · Volta · 2018 | – |
| RTX 5000 Ada GenerationPro32 GBGDDR6 · 576 GB/s · 250 WNVIDIA · Ada Lovelace · 2023 | about $4,000 |
| Radeon PRO W7800Pro32 GBGDDR6 · 576 GB/s · 260 WAMD · RDNA 3 · 2023 | about $2,499 |
Which GPU Do You Need for AI?
The amount of VRAM is the most important specification for running LLMs locally. Most 7B parameter models require 4–8 GB of VRAM at common quantization levels, while 70B models need 24–48 GB. Memory bandwidth determines how fast the model generates tokens — faster bandwidth means faster responses.