Best GPUs for Running LLMs Locally
Compare 56 GPUs for local AI inference. Find the best GPU for your budget based on VRAM, memory bandwidth, and model compatibility.
Browse by VRAM Tier
Find the best models for your VRAM budget
Entry-level for LLMs (RTX 4060, RX 7600, Apple M-series base) — 7B models at Q4, small models at Q8
Mid-range (RTX 3060, RTX 4070, RTX 5070) — 7-13B models at Q4-Q6
Upper mid-range (RTX 4080, RTX 5070 Ti, Arc A770, Apple M4 16GB) — 13B models, some 30B at Q4
Enthusiast (RTX 3090, RTX 4090, RX 7900 XTX) — 30B+ models at Q4-Q6, 70B at aggressive quant
Popular GPUs
View all 56 →AMD Instinct MI210
AMD · CDNA 2
AMD Instinct MI250X
AMD · CDNA 2
AMD Instinct MI300X
AMD · CDNA 3
AMD Radeon PRO W7800
AMD · RDNA 3
AMD Radeon PRO W7900
AMD · RDNA 3
AMD Radeon RX 6700 XT
AMD · RDNA 2
AMD Radeon RX 6800
AMD · RDNA 2
AMD Radeon RX 6800 XT
AMD · RDNA 2
AMD Radeon RX 6900 XT
AMD · RDNA 2
AMD Radeon RX 7600
AMD · RDNA 3
AMD Radeon RX 7700 XT
AMD · RDNA 3
AMD Radeon RX 7800 XT
AMD · RDNA 3
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.