Gemma 2 27B vs Mistral Small 24B
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 | Mistral Small 24B | |
|---|---|---|
| Parameters | 27B | 24B |
| Context window | 8K tokens | 32K tokens |
| License | Gemma Terms of Use | Apache-2.0 |
| ~VRAM @ 4-bit (Q4_K_M) | ~17 GB | ~14 GB |
| ~VRAM @ 8-bit (Q8_0) | ~29 GB | ~25 GB |
| Minimum device | NVIDIA GeForce RTX 3090 | Intel Arc A770 16GB |
| Recommended device | Supermicro 8x H100 SuperServer | Supermicro 8x H100 SuperServer |
| Deployment | Local / on-prem | Local / on-prem |
| Capabilities | Multilingual | Tools, Code, Multilingual, Long context |
Highlighted cells mark the lighter / longer / more permissive side per row, for local deployment. Informational rows have no winner.
Bottom line
Mistral Small 24B (~24B) is lighter than Gemma 2 27B (~27B), so it runs on more modest hardware, while Gemma 2 27B trades a larger footprint for more capacity. At 4-bit, Mistral Small 24B needs about 14GB versus ~17GB, a meaningful gap when choosing a GPU. Mistral Small 24B advertises the longer context window (32K vs 8K), which helps with long documents. Mistral Small 24B's Apache-2.0 license is the more permissive of the two for commercial use. Minimum viable hardware differs: Gemma 2 27B starts on a NVIDIA GeForce RTX 3090, Mistral Small 24B on a Intel Arc A770 16GB. Figures are approximate working-set estimates, not benchmarks — verify the exact release before committing hardware.
Pick Gemma 2 27B if you have the memory to spare and want the larger model.
Pick Mistral Small 24B if you want the lighter footprint and cheaper hardware, or you need the longer 32K context window, or you want the more permissive Apache-2.0 license.
Needs 24GB+ for comfortable 4-bit inference (RTX 3090/4090 class). Short context limits very long documents.
A 24GB card at 4-bit. A capable, openly-licensed mid-size model between 14B and 32B.
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.