Qwen2.5-Coder 32B 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.
| Qwen2.5-Coder 32B | StarCoder2 15B | |
|---|---|---|
| Parameters | 32B | 15B |
| Context window | 128K tokens | 16K tokens |
| License | Apache-2.0 | BigCode OpenRAIL-M |
| ~VRAM @ 4-bit (Q4_K_M) | ~20 GB | ~10 GB |
| ~VRAM @ 8-bit (Q8_0) | ~34 GB | ~17 GB |
| Minimum device | NVIDIA GeForce RTX 3090 | NVIDIA GeForce RTX 3060 12GB |
| Recommended device | Supermicro 8x H100 SuperServer | Supermicro 8x H100 SuperServer |
| Deployment | Hybrid | 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
StarCoder2 15B (~15B) is lighter than Qwen2.5-Coder 32B (~32B), so it runs on more modest hardware, while Qwen2.5-Coder 32B trades a larger footprint for more capacity. At 4-bit, StarCoder2 15B needs about 10GB versus ~20GB, a meaningful gap when choosing a GPU. Qwen2.5-Coder 32B advertises the longer context window (128K vs 16K), which helps with long documents. Qwen2.5-Coder 32B's Apache-2.0 license is the more permissive of the two for commercial use. Minimum viable hardware differs: Qwen2.5-Coder 32B starts on a NVIDIA GeForce RTX 3090, StarCoder2 15B on a NVIDIA GeForce RTX 3060 12GB. Figures are approximate working-set estimates, not benchmarks — verify the exact release before committing hardware.
Pick Qwen2.5-Coder 32B 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.
Pick StarCoder2 15B if you want the lighter footprint and cheaper hardware, or your focus is coding.
A 24GB card (RTX 3090/4090) or 32GB+ Mac at 4-bit. The strongest open coder you can run on one consumer card.
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.