Qwen3-4B-Instruct-2507 vs SmolLM3-3B-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

• SmolLM3-3B-Base needs less VRAM at its published precision (6.9 GB against 9.0 GB), so it fits on a smaller GPU.
• Qwen3-4B-Instruct-2507 lists the longer context window (262,144 tokens against 65,536).
• SmolLM3-3B-Base caches less per sequence at 32k tokens (2.3 GB against 4.5 GB), leaving more memory for batching.

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

• Qwen3-4B-Instruct-2507: full VRAM table and live GPU fit
• SmolLM3-3B-Base: full VRAM table and live GPU fit
• The Qwen model series
• The SmolLM model series
• All models that fit in 12 GB
• All models that fit in 8 GB

Other comparisons

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