granite-4.1-30b vs Qwen2.5-Coder-32B-Instruct

Side by side

VRAM is the weight size at each precision times a flat 1.2 overhead; see the methodology. The FP8 and INT4 rows need a quantized checkpoint or an engine that quantizes on load. The fit is the lowest-priced single GPU type that holds the model at that precision, or the lowest-priced multi-GPU set (up to 8) when none does. KV cache is for one sequence at 16-bit, computed from each model's config where the attention layout is known.

Which to pick

• granite-4.1-30b needs less VRAM at its published precision (64.5 GB against 73.2 GB), so it fits on a smaller GPU.
• granite-4.1-30b lists the longer context window (131,072 tokens against 32,768).

These follow only from the facts in the table above. Whether either model does your task well is a separate question this page does not answer.

Keep reading

• granite-4.1-30b: full VRAM table and live GPU fit
• Qwen2.5-Coder-32B-Instruct: full VRAM table and live GPU fit
• The Granite model series
• The Qwen model series
• All models that fit in 80 GB

Other comparisons

• granite-4.1-30b vs Qwen3-32B
• granite-4.1-30b vs Qwen3-30B-A3B
• granite-4.1-30b vs Qwen2.5-32B-Instruct
• Qwen2.5-Coder-32B-Instruct vs GLM-4.7-Flash
• Qwen2.5-Coder-32B-Instruct vs NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
• Qwen2.5-Coder-32B-Instruct vs NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16