Llama-3.3-70B-Instruct vs Qwen-72B

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-3.3-70B-Instruct needs less VRAM at its published precision (158 GB against 162 GB), so it fits on a smaller GPU.
• Llama-3.3-70B-Instruct lists the longer context window (131,072 tokens against 32,768).
• Llama-3.3-70B-Instruct caches less per sequence at 32k tokens (10.0 GB against 80.0 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

• Llama-3.3-70B-Instruct: full VRAM table and live GPU fit
• Qwen-72B: full VRAM table and live GPU fit
• The Llama model series
• The Qwen model series
• All models that fit in 192 GB

Other comparisons

• Llama-3.3-70B-Instruct vs Qwen3-Coder-Next
• Llama-3.3-70B-Instruct vs Qwen2.5-72B-Instruct
• Llama-3.3-70B-Instruct vs Phi-3.5-MoE-instruct
• Llama-3.3-70B-Instruct vs Qwen2.5-72B
• Qwen-72B vs Llama-3.1-70B-Instruct
• Qwen-72B vs Phi-3.5-MoE-instruct
• Qwen-72B vs Meta-Llama-3-70B