CodeLlama 7B vs StarCoder2 3B
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 3B | |
|---|---|---|
| Parameters | 7B | 3B |
| Context window | 16K tokens | 16K tokens |
| License | Llama Community License | BigCode OpenRAIL-M |
| ~VRAM @ 4-bit (Q4_K_M) | ~5 GB | ~2.2 GB |
| ~VRAM @ 8-bit (Q8_0) | ~8 GB | ~3.4 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
StarCoder2 3B (~3B) is lighter than CodeLlama 7B (~7B), so it runs on more modest hardware, while CodeLlama 7B trades a larger footprint for more capacity. At 4-bit, StarCoder2 3B needs about 2.2GB versus ~5GB, 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 7B if you have the memory to spare and want the larger model, or your focus is coding.
Pick StarCoder2 3B if you want the lighter footprint and cheaper hardware, or your focus is coding.
8GB+ GPUs at 4-bit. A well-established small coder for responsive in-editor completion.
Runs on a CPU or any small GPU. A tiny code model for fast, 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.