Llama-3.2-3B-Instruct vs Phi-3-mini-4k-instruct
Llama-3.2-3B-Instruct (3.2B parameters) and Phi-3-mini-4k-instruct (3.8B 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
| Fact | Llama-3.2-3B-Instruct | Phi-3-mini-4k-instruct |
|---|---|---|
| Parameters | 3.2B | 3.8B |
| Architecture | Grouped-query attention | – |
| Context length | 131,072 tokens | 4K tokens (4,096) |
| License | Llama 3.2 Community License Agreement | MIT |
| Published precision | BF16 | BF16 |
| VRAM needed, As published | 7.2 GB | 8.5 GB |
| VRAM needed, FP8 | 3.6 GB | 4.3 GB |
| VRAM needed, INT4 | 1.8 GB | 2.1 GB |
| Cheapest live fit, As published | RTX 4070 Super · $0.121/hr | RTX 4070 Super · $0.121/hr |
| Cheapest live fit, FP8 | RTX 4070 Super · $0.121/hr | RTX 4070 Super · $0.121/hr |
| Cheapest live fit, INT4 | RTX 4070 Super · $0.121/hr | RTX 4070 Super · $0.121/hr |
| KV cache per token (16-bit) | 112 KB | Not published for this architecture |
| KV cache at 32k tokens | 3.50 GB | Not published for this architecture |
| KV cache at 128k tokens | 14.0 GB | Not published for this architecture |
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-3.2-3B-Instruct needs less VRAM at its published precision (7.2 GB against 8.5 GB), so it fits on a smaller GPU.
- Llama-3.2-3B-Instruct lists the longer context window (131,072 tokens against 4,096).
- Licenses differ: Phi-3-mini-4k-instruct is under MIT, which our catalog notes as permissive; Llama-3.2-3B-Instruct is under Llama 3.2 Community License Agreement, so read its terms 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.
Keep reading
- Llama-3.2-3B-Instruct: full VRAM table and live GPU fit
- Phi-3-mini-4k-instruct: full VRAM table and live GPU fit
- The Llama model series
- The Phi model series
- All models that fit in 8 GB
- All models that fit in 12 GB
Other comparisons
- Llama-3.2-3B-Instruct vs Qwen2.5-3B-Instruct
- Llama-3.2-3B-Instruct vs Qwen3-4B
- Llama-3.2-3B-Instruct vs Qwen3-4B-Instruct-2507
- Llama-3.2-3B-Instruct vs Qwen3-4B-Base
- Phi-3-mini-4k-instruct vs Qwen2.5-3B-Instruct
- Phi-3-mini-4k-instruct vs Qwen3-4B
- Phi-3-mini-4k-instruct vs Qwen3-4B-Instruct-2507
- Phi-3-mini-4k-instruct vs Qwen3-4B-Base
