Best GPU for Running Local LLMs
Author · AI Local Check
What matters in a GPU for LLMs
Memory capacity determines whether a model fits entirely on the GPU, enabling faster processing by avoiding data transfers between chipsets. When memory is insufficient, part of the model must offload to system memory, which introduces latency due to slower access speeds. Raw processing speed alone cannot compensate for this bottleneck, as computational efficiency depends on continuous data availability. Larger memory allows uninterrupted execution, while smaller memory forces iterative, fragmented computation.
Choosing by budget and need
Larger models demand more memory to process data without degradation. Choosing hardware requires aligning available resources with the scale of tasks to avoid performance bottlenecks. Higher memory capacity increases cost, creating a trade-off between operational flexibility and financial constraints. Prioritize systems where expected workload matches the balance between allocated budget and required computational headroom.
| GPU | VRAM | Largest model (Q4) |
|---|---|---|
| NVIDIA RTX 4060 | 8 GB | 8B |
| NVIDIA RTX 3060 12 GB | 12 GB | 14B |
| NVIDIA RTX 4070 | 12 GB | 14B |
| NVIDIA RTX 4060 Ti 16 GB | 16 GB | 14B |
| NVIDIA RTX 4070 Ti Super | 16 GB | 14B |
| NVIDIA RTX 4080 | 16 GB | 14B |
| NVIDIA RTX 3090 | 24 GB | 32B |
| NVIDIA RTX 4090 | 24 GB | 32B |
Browse every GPU and what it runs
New, used, and other brands
Memory capacity determines how much of a model can load at once, enabling smoother processing without compression. Larger memory retains full model context, preserving accuracy where smaller limits force truncation. Newer hardware often prioritizes speed over memory, leaving older designs with more space to handle complex tasks. Used hardware with ample memory thus outperforms recent models constrained by insufficient capacity.
Frequently asked questions
What is the most important GPU spec for running LLMs?
The amount of video memory (VRAM). It sets the ceiling on which models you can run; raw speed matters less than having enough memory.
Is more VRAM worth it?
Yes if you want to run larger models or longer contexts. Match the memory to the models you actually plan to use rather than overspending.
Can older or used GPUs run LLMs?
Often yes. An older card with ample memory can run larger models than a newer card with less memory, since memory is the main constraint.