Llama-3.2-3B-Instruct vs Qwen3-4B-Base

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

• Llama-3.2-3B-Instruct needs less VRAM at its published precision (7.2 GB against 9.0 GB), so it fits on a smaller GPU.
• Llama-3.2-3B-Instruct lists the longer context window (131,072 tokens against 32,768).
• Llama-3.2-3B-Instruct caches less per sequence at 32k tokens (3.5 GB against 4.5 GB), leaving more memory for batching.
• Licenses differ: Qwen3-4B-Base is under Apache 2.0, which our catalog notes as permissive; Llama-3.2-3B-Instruct is under Llama 3.2 Community License Agreement, so read its terms before commercial use.

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

• Llama-3.2-3B-Instruct: full VRAM table and live GPU fit
• Qwen3-4B-Base: full VRAM table and live GPU fit
• The Llama model series
• The Qwen model series
• All models that fit in 8 GB
• All models that fit in 12 GB

Other comparisons

• Llama-3.2-3B-Instruct vs Qwen2.5-3B-Instruct
• Llama-3.2-3B-Instruct vs Qwen3-4B
• Llama-3.2-3B-Instruct vs Qwen3-4B-Instruct-2507
• Llama-3.2-3B-Instruct vs SmolLM3-3B-Base
• Qwen3-4B-Base vs SmolLM3-3B-Base
• Qwen3-4B-Base vs Phi-3-mini-4k-instruct
• Qwen3-4B-Base vs SmolLM3-3B