Gemma 2 9B vs Llama 3.1 8B
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 9B | Llama 3.1 8B | |
|---|---|---|
| Parameters | 9B | 8B |
| Context window | 8K tokens | 128K tokens |
| License | Gemma Terms of Use | Llama Community License |
| ~VRAM @ 4-bit (Q4_K_M) | ~7 GB | ~6 GB |
| ~VRAM @ 8-bit (Q8_0) | ~10 GB | ~9 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 | Multilingual | 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 3.1 8B (~8B) is lighter than Gemma 2 9B (~9B), so it runs on more modest hardware, while Gemma 2 9B trades a larger footprint for more capacity. At 4-bit, Llama 3.1 8B needs about 6GB versus ~7GB, a meaningful gap when choosing a GPU. Llama 3.1 8B advertises the longer context window (128K vs 8K), which helps with long documents. 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 2 9B if you have the memory to spare and want the larger model.
Pick Llama 3.1 8B if you want the lighter footprint and cheaper hardware, or you need the longer 128K context window.
8-12GB GPUs at 4-bit. Strong quality for its size, with a shorter native context window.
Runs comfortably at 4-bit on any 8GB+ GPU, a Mac mini, or a small mini PC. The classic entry point for local AI.
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.