GPU for local LLMs

NVIDIA GPUs for Local LLMs

Compare NVIDIA RTX GPUs by VRAM, see which local models fit, and check Ollama or LM Studio compatibility using deterministic AIPCFit data.

Use this hub when you are choosing a GPU for local LLMs. Use the PC checker when you want results for your exact GPU, RAM, operating system, and local AI workload.

Browse RTX GPUsBest local LLMsCalculate VRAM

Choose by VRAM

Start with the memory tier, then confirm model fit.

Higher VRAM generally allows larger model variants or more headroom, but local LLM fit still depends on quantization, runtime overhead, context length, and offloading behavior. These links are planning tiers, not official requirements.

8GB VRAM guide

Planning tier for smaller quantized local LLM variants and careful headroom.

12GB VRAM guide

Planning tier for common quantized local LLM work with more room for runtime overhead.

16GB VRAM guide

Planning tier for common quantized local LLM work with more room for runtime overhead.

24GB VRAM guide

Planning tier for larger variants or more comfortable local AI memory headroom.

32GB VRAM guide

Planning tier for larger variants or more comfortable local AI memory headroom.

48GB VRAM guide

Planning tier for larger variants or more comfortable local AI memory headroom.

Which GPUs can run local LLMs?

The catalog groups NVIDIA GPUs by recorded VRAM.

AIPCFit does not rank one GPU as universally best. Instead, it records NVIDIA RTX hardware data and checks model fit against the GPU memory available for local AI planning.

6GB VRAM

Entry-level VRAM tier

2 catalog GPUs

  • RTX 2060 6GB
  • RTX 3050 6GB

8GB VRAM

Entry-level VRAM tier

15 catalog GPUs

  • RTX 2060 SUPER 8GB
  • RTX 2070 8GB
  • RTX 2070 SUPER 8GB
  • RTX 2080 8GB

10GB VRAM

Mid-range memory tier

1 catalog GPU

  • RTX 3080 10GB

11GB VRAM

Mid-range memory tier

1 catalog GPU

  • RTX 2080 Ti 11GB

12GB VRAM

Mid-range memory tier

8 catalog GPUs

Model-fit discovery

See deterministic examples before opening a full compatibility page.

These examples use dedicated VRAM only and do not infer system RAM. Open a GPU page or the compatibility hub for model-specific pages.

Representative GPU

RTX 4060 Ti 8GB

8GB VRAM
  • Llama 3.2 3B · Q4_K_M · good fit
  • Phi-3.5 Mini · Q4_K_M · good fit
  • Phi-4 Mini · Q4_K_M · good fit

Representative GPU

RTX 3060 12GB

12GB VRAM
  • Llama 3.2 3B · Q4_K_M · good fit
  • Phi-3.5 Mini · Q4_K_M · good fit
  • Phi-4 Mini · Q4_K_M · good fit

Representative GPU

RTX 4060 Ti 16GB

16GB VRAM
  • Llama 3.2 3B · Q4_K_M · good fit
  • Phi-3.5 Mini · Q4_K_M · good fit
  • Phi-4 Mini · Q4_K_M · good fit

Representative GPU

RTX 4090 24GB

24GB VRAM
  • Llama 3.2 3B · Q4_K_M · good fit
  • Phi-3.5 Mini · Q4_K_M · good fit
  • Phi-4 Mini · Q4_K_M · good fit

Representative GPU

RTX 5090 32GB

32GB VRAM
  • Llama 3.2 3B · Q4_K_M · good fit
  • Phi-3.5 Mini · Q4_K_M · good fit
  • Phi-4 Mini · Q4_K_M · good fit

NVIDIA RTX directory

Pick a GPU to check local AI compatibility.

How much VRAM do local LLMs need?

Model size, quantization, and runtime settings all matter.

A larger model or less compressed quantization needs more memory. Runtime overhead, context length, KV cache, other GPU work, system RAM, and CPU offloading can change what is practical. File size alone is not the same as full runtime VRAM.

Use the VRAM calculatorCheck my exact PCMethodology