gemma-3-1b-it vs Qwen2.5-1.5B-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

• gemma-3-1b-it needs less VRAM at its published precision (2.2 GB against 3.5 GB), so it fits on a smaller GPU.
• gemma-3-1b-it caches less per sequence at 32k tokens (0.1 GB against 0.9 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

• gemma-3-1b-it: full VRAM table and live GPU fit
• Qwen2.5-1.5B-Instruct: full VRAM table and live GPU fit
• The Gemma model series
• The Qwen model series
• All models that fit in 8 GB

Other comparisons

• gemma-3-1b-it vs Qwen3-0.6B
• gemma-3-1b-it vs Llama-3.2-1B-Instruct
• gemma-3-1b-it vs Qwen2.5-0.5B-Instruct
• gemma-3-1b-it vs Qwen3-1.7B
• Qwen2.5-1.5B-Instruct vs Llama-3.2-1B-Instruct
• Qwen2.5-1.5B-Instruct vs SmolLM2-135M
• Qwen2.5-1.5B-Instruct vs phi-2