Llama-3.2-3B-Instruct vs Qwen2.5-3B-Instruct

Llama-3.2-3B-Instruct (3.2B parameters) and Qwen2.5-3B-Instruct (3.1B parameters) side by side: the memory each needs at every precision, what it costs to run on a live GPU, and the context window, KV cache and license where they are published. Numbers are computed from the models' published specs; this page does not rank quality.

Side by side

FactLlama-3.2-3B-InstructQwen2.5-3B-Instruct
Parameters3.2B3.1B
ArchitectureGrouped-query attentionGrouped-query attention
Context length131,072 tokens32K tokens (32,768)
LicenseLlama 3.2 Community License AgreementCustom license
Published precisionBF16BF16
VRAM needed, As published7.2 GB6.9 GB
VRAM needed, FP83.6 GB3.4 GB
VRAM needed, INT41.8 GB1.7 GB
Cheapest live fit, As publishedRTX 4070 Super · $0.121/hrRTX 4070 Super · $0.121/hr
Cheapest live fit, FP8RTX 4070 Super · $0.121/hrRTX 4070 Super · $0.121/hr
Cheapest live fit, INT4RTX 4070 Super · $0.121/hrRTX 4070 Super · $0.121/hr
KV cache per token (16-bit)112 KB36 KB
KV cache at 32k tokens3.50 GB1.13 GB
KV cache at 128k tokens14.0 GB4.50 GB

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

  • Qwen2.5-3B-Instruct needs less VRAM at its published precision (6.9 GB against 7.2 GB), so it fits on a smaller GPU.
  • Llama-3.2-3B-Instruct lists the longer context window (131,072 tokens against 32,768).
  • Qwen2.5-3B-Instruct caches less per sequence at 32k tokens (1.1 GB against 3.5 GB), leaving more memory for batching.
  • Licenses differ: Llama-3.2-3B-Instruct is under Llama 3.2 Community License Agreement and Qwen2.5-3B-Instruct under Custom license. Read both 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.

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