Qwen3 4B
qwen3:4b
Model file
2.5 GB
Planning VRAM
4 GB
Recorded context
256K
GPU × Ollama compatibility
Can NVIDIA GeForce RTX 4070 Ti SUPER 16GB run Ollama?
Check model-by-model hardware fit, VRAM pressure, context support, and the verified Windows NVIDIA acceleration path. No benchmark speed is invented.
VRAM
16 GB
Architecture
Ada Lovelace
Acceleration
CUDA
Windows 11
Supported
Interactive check
Try a model on this GPU
Change the model or context assumption and see how the hardware-fit signal changes. This does not predict speed or model quality.
Hardware fit
Good hardware fitGPU VRAM
16 GB
Planning VRAM
6.7 GB
Context
8K supported
What this means
The GPU has enough VRAM for the model weights plus the conservative planning headroom used by this tool.
Model file size and planning VRAM are not official VRAM minimums. Exact memory use changes with context, runtime behavior, and offloading.
All recorded models
Ollama model fit on 16 GB VRAM
These results compare the current model catalog against the GPU's recorded VRAM. A model file is not the same thing as an official VRAM requirement.
Good hardware fit
Qwen3 8B
qwen3:8b
Model file
5.2 GB
Planning VRAM
6.7 GB
Recorded context
40K
Qwen3 14B
qwen3:14b
Model file
9.3 GB
Planning VRAM
11.6 GB
Recorded context
40K
Gemma 3 4B
gemma3:4b
Model file
3.3 GB
Planning VRAM
4.8 GB
Recorded context
128K
Gemma 3 12B
gemma3:12b
Model file
8.1 GB
Planning VRAM
10.1 GB
Recorded context
128K
Gemma 3 27B
gemma3:27b
Model file
17 GB
Planning VRAM
21.3 GB
Recorded context
128K
Your exact PC may behave differently
GPU VRAM is only one part of the answer.
Add your real GPU, RAM, operating system, context needs, and workflow to get the full Advisor and Upgrade Advisor result.
Check my exact PCEvidence