Gemma 2 27B vs Gemma 3 27B
Size, context window, license, approximate VRAM and the minimum local hardware each model needs — computed from our catalog and compatibility engine, not benchmarks.
| Gemma 2 27B | Gemma 3 27B | |
|---|---|---|
| Parameters | 27B | 27B |
| Context window | 8K tokens | 128K tokens |
| License | Gemma Terms of Use | Gemma Terms of Use |
| ~VRAM @ 4-bit (Q4_K_M) | ~17 GB | ~17 GB |
| ~VRAM @ 8-bit (Q8_0) | ~29 GB | ~29 GB |
| Minimum device | NVIDIA GeForce RTX 3090 | NVIDIA GeForce RTX 3090 |
| Recommended device | Supermicro 8x H100 SuperServer | Supermicro 8x H100 SuperServer |
| Deployment | Local / on-prem | Hybrid |
| Capabilities | Multilingual | Vision, Multilingual, Long context |
Highlighted cells mark the lighter / longer / more permissive side per row, for local deployment. Informational rows have no winner.
Bottom line
Gemma 2 27B and Gemma 3 27B are the same size (~27B parameters), so their memory footprints are comparable. Gemma 3 27B advertises the longer context window (128K vs 8K), which helps with long documents. Both can start on a NVIDIA GeForce RTX 3090-class machine. Figures are approximate working-set estimates, not benchmarks — verify the exact release before committing hardware.
Pick Gemma 2 27B if quality team assistant.
Pick Gemma 3 27B if you need the longer 128K context window.
Needs 24GB+ for comfortable 4-bit inference (RTX 3090/4090 class). Short context limits very long documents.
A 24GB card (RTX 3090/4090) or 32GB+ Mac at 4-bit. The flagship Gemma 3 with long context and vision.
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.