CodeLlama 7B vs StarCoder2 7B
Size, context window, license, approximate VRAM and the minimum local hardware each model needs — computed from our catalog and compatibility engine, not benchmarks.
| CodeLlama 7B | StarCoder2 7B | |
|---|---|---|
| Parameters | 7B | 7B |
| Context window | 16K tokens | 16K tokens |
| License | Llama Community License | BigCode OpenRAIL-M |
| ~VRAM @ 4-bit (Q4_K_M) | ~5 GB | ~5 GB |
| ~VRAM @ 8-bit (Q8_0) | ~8 GB | ~8 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 7B and StarCoder2 7B are the same size (~7B parameters), so their memory footprints are comparable. 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 7B if your focus is coding.
Pick StarCoder2 7B if your focus is coding.
8GB+ GPUs at 4-bit. A well-established small coder for responsive in-editor completion.
8GB+ GPUs at 4-bit. A small code model for responsive private completion.
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.