Gemma 3 4B vs Llama 3.2 3B
Size, context window, license, approximate VRAM and the minimum local hardware each model needs — computed from our catalog and compatibility engine, not benchmarks.
| Gemma 3 4B | Llama 3.2 3B | |
|---|---|---|
| Parameters | 4B | 3B |
| Context window | 128K tokens | 128K tokens |
| License | Gemma Terms of Use | Llama Community License |
| ~VRAM @ 4-bit (Q4_K_M) | ~3 GB | ~2.5 GB |
| ~VRAM @ 8-bit (Q8_0) | ~4.5 GB | ~4 GB |
| Minimum device | NVIDIA GeForce RTX 3060 12GB | NVIDIA GeForce RTX 3060 12GB |
| Recommended device | Supermicro 8x H100 SuperServer | Supermicro 8x H100 SuperServer |
| Deployment | Local / on-prem | Local / on-prem |
| Capabilities | Vision, Multilingual, Long context | Tools, Multilingual, Long context |
Highlighted cells mark the lighter / longer / more permissive side per row, for local deployment. Informational rows have no winner.
Bottom line
Llama 3.2 3B (~3B) is lighter than Gemma 3 4B (~4B), so it runs on more modest hardware, while Gemma 3 4B trades a larger footprint for more capacity. At 4-bit, Llama 3.2 3B needs about 2.5GB versus ~3GB, a meaningful gap when choosing a GPU. Both target a 128K context window. Both can start on a NVIDIA GeForce RTX 3060 12GB-class machine. Figures are approximate working-set estimates, not benchmarks — verify the exact release before committing hardware.
Pick Gemma 3 4B if you have the memory to spare and want the larger model.
Pick Llama 3.2 3B if you want the lighter footprint and cheaper hardware.
8GB GPUs, a Mac mini, or a small mini PC at 4-bit. A current small generalist with a long context and image input.
Comfortable on any 8GB GPU, a Mac mini, or a small mini PC. A good entry assistant for a single office.
Run the winner on hardware you control
Pick the model that fits your footprint, then turn the right machine into a private AI Business OS — no per-seat data leaving your premises.