Qwen2.5-Coder 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.
| Qwen2.5-Coder 7B | StarCoder2 7B | |
|---|---|---|
| Parameters | 7B | 7B |
| Context window | 128K tokens | 16K tokens |
| License | Apache-2.0 | BigCode OpenRAIL-M |
| ~VRAM @ 4-bit (Q4_K_M) | ~5.5 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, Tools, Long context | Code |
Highlighted cells mark the lighter / longer / more permissive side per row, for local deployment. Informational rows have no winner.
Bottom line
Qwen2.5-Coder 7B and StarCoder2 7B are the same size (~7B parameters), so their memory footprints are comparable. At 4-bit, StarCoder2 7B needs about 5GB versus ~5.5GB, a meaningful gap when choosing a GPU. Qwen2.5-Coder 7B advertises the longer context window (128K vs 16K), which helps with long documents. Qwen2.5-Coder 7B'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 Qwen2.5-Coder 7B if you need the longer 128K context window, or you want the more permissive Apache-2.0 license, or your focus is coding.
Pick StarCoder2 7B if your focus is coding.
8GB+ GPUs at 4-bit. Ideal for responsive in-editor completion on modest hardware.
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.