granite-4.1-30b vs Qwen3-32B

granite-4.1-30b (28.9B parameters) and Qwen3-32B (32.8B parameters) side by side: the memory each needs at every precision, what it costs to run on a live GPU, and the context window, KV cache and license where they are published. Numbers are computed from the models' published specs; this page does not rank quality.

Side by side

Factgranite-4.1-30bQwen3-32B
Parameters28.9B32.8B
ArchitectureGrouped-query attentionGrouped-query attention
Context length131,072 tokens40K tokens (40,960)
License–Apache 2.0
Published precisionBF16BF16
VRAM needed, As published64.5 GB73.2 GB
VRAM needed, FP832.3 GB36.6 GB
VRAM needed, INT416.1 GB18.3 GB
Cheapest live fit, As publishedA100 · $1.21/hrA100 · $1.21/hr
Cheapest live fit, FP8B300 · $0.550/hrL40 · $0.742/hr
Cheapest live fit, INT4RTX A5000 · $0.176/hrRTX A5000 · $0.176/hr
KV cache per token (16-bit)256 KB256 KB
KV cache at 32k tokens8.00 GB8.00 GB
KV cache at 128k tokens32.0 GB32.0 GB

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 40,960).

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.

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