phi-2 vs Qwen3-0.6B

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

• Qwen3-0.6B needs less VRAM at its published precision (1.7 GB against 6.2 GB), so it fits on a smaller GPU.
• Qwen3-0.6B lists the longer context window (40,960 tokens against 2,048).
• Qwen3-0.6B caches less per sequence at 32k tokens (3.5 GB against 10.0 GB), leaving more memory for batching.
• phi-2 has the cheaper live GPU fit at its published precision ($0.088/hr against $0.121/hr).

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

• phi-2: full VRAM table and live GPU fit
• Qwen3-0.6B: full VRAM table and live GPU fit
• The Phi model series
• The Qwen model series
• All models that fit in 8 GB

Other comparisons

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