CodeLlama 13B vs Qwen2.5-Coder 14B
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 | Qwen2.5-Coder 14B | |
|---|---|---|
| Parameters | 13B | 14B |
| Context window | 16K tokens | 128K tokens |
| License | Llama Community License | Apache-2.0 |
| ~VRAM @ 4-bit (Q4_K_M) | ~8 GB | ~10 GB |
| ~VRAM @ 8-bit (Q8_0) | ~14 GB | ~16 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, Tools, Long context |
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 Qwen2.5-Coder 14B (~14B), so it runs on more modest hardware, while Qwen2.5-Coder 14B trades a larger footprint for more capacity. At 4-bit, CodeLlama 13B needs about 8GB versus ~10GB, a meaningful gap when choosing a GPU. Qwen2.5-Coder 14B advertises the longer context window (128K vs 16K), which helps with long documents. Qwen2.5-Coder 14B's Apache-2.0 license is the more permissive of the two for commercial use. 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 Qwen2.5-Coder 14B if you have the memory to spare and want the larger model, or you need the longer 128K context window, or you want the more permissive Apache-2.0 license.
16GB+ GPUs at 4-bit. The mid-size CodeLlama for stronger completion and light refactoring.
16GB+ GPUs at 4-bit. A strong balance of coding quality and footprint for a developer workstation.
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.