CodeLlama 13B vs StarCoder2 15B
Size, context window, license, approximate VRAM and the minimum local hardware each model needs — computed from our catalog and compatibility engine, not benchmarks.
| CodeLlama 13B | StarCoder2 15B | |
|---|---|---|
| Parameters | 13B | 15B |
| Context window | 16K tokens | 16K tokens |
| License | Llama Community License | BigCode OpenRAIL-M |
| ~VRAM @ 4-bit (Q4_K_M) | ~8 GB | ~10 GB |
| ~VRAM @ 8-bit (Q8_0) | ~14 GB | ~17 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 | Code | Code |
Highlighted cells mark the lighter / longer / more permissive side per row, for local deployment. Informational rows have no winner.
Bottom line
CodeLlama 13B (~13B) is lighter than StarCoder2 15B (~15B), so it runs on more modest hardware, while StarCoder2 15B trades a larger footprint for more capacity. At 4-bit, CodeLlama 13B needs about 8GB versus ~10GB, a meaningful gap when choosing a GPU. Both target a 16K 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 CodeLlama 13B if you want the lighter footprint and cheaper hardware, or your focus is coding.
Pick StarCoder2 15B if you have the memory to spare and want the larger model, or your focus is coding.
16GB+ GPUs at 4-bit. The mid-size CodeLlama for stronger completion and light refactoring.
16GB+ GPUs at 4-bit. The largest StarCoder2 for stronger completion on one card.
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.