DeepSeek-Coder V2 (class) vs Qwen2.5-Coder 32B
Size, context window, license, approximate VRAM and the minimum local hardware each model needs — computed from our catalog and compatibility engine, not benchmarks.
| DeepSeek-Coder V2 (class) | Qwen2.5-Coder 32B | |
|---|---|---|
| Parameters | 16B | 32B |
| Context window | 128K tokens | 128K tokens |
| License | DeepSeek License | Apache-2.0 |
| ~VRAM @ 4-bit (Q4_K_M) | ~11 GB | ~20 GB |
| ~VRAM @ 8-bit (Q8_0) | ~18 GB | ~34 GB |
| Minimum device | Intel Arc A770 16GB | NVIDIA GeForce RTX 3090 |
| Recommended device | Supermicro 8x H100 SuperServer | Supermicro 8x H100 SuperServer |
| Deployment | Local / on-prem | Hybrid |
| Capabilities | Code, Long context | 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
DeepSeek-Coder V2 (class) (~16B) 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, DeepSeek-Coder V2 (class) needs about 11GB versus ~20GB, a meaningful gap when choosing a GPU. Both target a 128K context window. Qwen2.5-Coder 32B's Apache-2.0 license is the more permissive of the two for commercial use. Minimum viable hardware differs: DeepSeek-Coder V2 (class) starts on a Intel Arc A770 16GB, Qwen2.5-Coder 32B on a NVIDIA GeForce RTX 3090. Figures are approximate working-set estimates, not benchmarks — verify the exact release before committing hardware.
Pick DeepSeek-Coder V2 (class) if you want the lighter footprint and cheaper hardware, or your focus is coding.
Pick Qwen2.5-Coder 32B if you have the memory to spare and want the larger model, or you want the more permissive Apache-2.0 license, or your focus is coding.
The compact coder variants fit 16GB+ at 4-bit; larger MoE variants need 48GB+ or cloud.
A 24GB card (RTX 3090/4090) or 32GB+ Mac at 4-bit. The strongest open coder you can run on one consumer 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.