CodeLlama 34B 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.
| CodeLlama 34B | Qwen2.5-Coder 32B | |
|---|---|---|
| Parameters | 34B | 32B |
| Context window | 16K tokens | 128K tokens |
| License | Llama Community License | Apache-2.0 |
| ~VRAM @ 4-bit (Q4_K_M) | ~21 GB | ~20 GB |
| ~VRAM @ 8-bit (Q8_0) | ~37 GB | ~34 GB |
| Minimum device | NVIDIA GeForce RTX 3090 | NVIDIA GeForce RTX 3090 |
| Recommended device | Supermicro 8x H100 SuperServer | Supermicro 8x H100 SuperServer |
| Deployment | Hybrid | Hybrid |
| 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
Qwen2.5-Coder 32B (~32B) is lighter than CodeLlama 34B (~34B), so it runs on more modest hardware, while CodeLlama 34B trades a larger footprint for more capacity. At 4-bit, Qwen2.5-Coder 32B needs about 20GB versus ~21GB, 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. Both can start on a NVIDIA GeForce RTX 3090-class machine. Figures are approximate working-set estimates, not benchmarks — verify the exact release before committing hardware.
Pick CodeLlama 34B if you have the memory to spare and want the larger model, or your focus is coding.
Pick Qwen2.5-Coder 32B if you want the lighter footprint and cheaper hardware, or you need the longer 128K context window, or you want the more permissive Apache-2.0 license.
A 24GB+ card (RTX 3090/4090) or 32GB+ Mac at 4-bit. The largest CodeLlama for a single box.
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.