Phi-3.5 Mini Instruct vs Llama 3.2 3B Instruct
Side-by-side VRAM requirements, benchmark scores, and GPU compatibility for local AI inference.
Quick verdict
Llama 3.2 3B Instruct is more hardware-efficient — it needs 2.8 GB at Q4_K_M vs 5.7 GB for Phi-3.5 Mini Instruct, fitting on 66 GPUs natively.
VRAM at each quantization (8k context)
| Quant | Phi-3.5 Mini Instruct | Llama 3.2 3B Instruct | Diff |
|---|---|---|---|
| FP16 | 12.1 GB | 8.2 GB | +47% |
| Q8 | 7.9 GB | 4.6 GB | +70% |
| Q6_K | 6.8 GB | 3.7 GB | +82% |
| Q5_K_M | 6.3 GB | 3.3 GB | +90% |
| Q4_K_M | 5.7 GB | 2.8 GB | +102% |
| Q3_K_M | 5.3 GB | 2.5 GB | +114% |
| Q2_K | 4.9 GB | 2.1 GB | +130% |
Diff is Phi-3.5 Mini Instruct relative to Llama 3.2 3B Instruct. Green = lower VRAM (fits more GPUs).
Model specifications
| Spec | Phi-3.5 Mini Instruct | Llama 3.2 3B Instruct |
|---|---|---|
| Org | Microsoft | Meta |
| Parameters | 3.8B | 3.2B |
| Architecture | Dense | Dense |
| Context | 125k tokens | 125k tokens |
| Modalities | text | text |
| License | MIT | Llama 3.2 Community |
| Commercial | Yes | Yes |
| Released | 2024-08-21 | 2024-09-25 |
| GPUs (native) | 66 / 67 | 66 / 67 |
Benchmark scores
| Benchmark | Phi-3.5 Mini Instruct | Llama 3.2 3B Instruct |
|---|---|---|
| MMLU-Pro | 35.6 | 24.0 |
| GPQA | 28.0 | — |
| IFEval | 65.7 | 77.4 |
| MATH | 48.5 | 48.0 |
| HumanEval | 62.8 | 56.7 |
Green = higher score (better). — = not yet available.
GPUs that run only Phi-3.5 Mini Instruct(0)
Every GPU that runs Phi-3.5 Mini Instruct also runs Llama 3.2 3B Instruct.
GPUs that run only Llama 3.2 3B Instruct(0)
Every GPU that runs Llama 3.2 3B Instruct also runs Phi-3.5 Mini Instruct.
GPUs that run both natively(66)
- NVIDIA RTX 509032 GB
- NVIDIA RTX 409024 GB
- NVIDIA RTX 408016 GB
- NVIDIA RTX 4070 Ti12 GB
- NVIDIA RTX 407012 GB
- NVIDIA RTX 4060 Ti 16GB16 GB
- NVIDIA RTX 40608 GB
- NVIDIA RTX 309024 GB
- NVIDIA RTX 3090 Ti24 GB
- NVIDIA RTX 3080 10GB10 GB
- NVIDIA RTX 3060 12GB12 GB
- NVIDIA H100 80GB80 GB
- +54 more GPUs run both
Which should you use?
Choose Phi-3.5 Mini Instruct if:
- • You want maximum capability and have a 6 GB+ GPU
- • Benchmark quality matters — scores 35.6 vs 24.0 on MMLU-Pro
Choose Llama 3.2 3B Instruct if:
- • You have limited VRAM — it's a smaller model needing 2.8 GB vs 5.7 GB
Frequently asked questions
- Which is better, Phi-3.5 Mini Instruct or Llama 3.2 3B Instruct?
- Phi-3.5 Mini Instruct has 3.8B parameters vs 3.2B for Llama 3.2 3B Instruct, so Phi-3.5 Mini Instruct is the larger model. Llama 3.2 3B Instruct is more hardware-efficient, needing 2.8 GB at Q4_K_M vs 5.7 GB. On MMLU-Pro, Phi-3.5 Mini Instruct scores higher (35.6 vs 24.0).
- How much VRAM does Phi-3.5 Mini Instruct need vs Llama 3.2 3B Instruct?
- At Q4_K_M quantization with 8k context, Phi-3.5 Mini Instruct needs approximately 5.7 GB of VRAM, while Llama 3.2 3B Instruct needs 2.8 GB. At FP16, Phi-3.5 Mini Instruct requires 12.1 GB vs 8.2 GB for Llama 3.2 3B Instruct.
- Can you run Phi-3.5 Mini Instruct on the same GPUs as Llama 3.2 3B Instruct?
- Yes, 66 GPUs can run both natively in VRAM, including NVIDIA RTX 5090, NVIDIA RTX 4090, NVIDIA RTX 4080. However, no GPU can run Phi-3.5 Mini Instruct without also fitting Llama 3.2 3B Instruct, and no GPU can run Llama 3.2 3B Instruct without also fitting Phi-3.5 Mini Instruct.
- What is the difference between Phi-3.5 Mini Instruct and Llama 3.2 3B Instruct?
- Phi-3.5 Mini Instruct has 3.8B parameters (dense) with a 125k context window. Llama 3.2 3B Instruct has 3.2B parameters (dense) with a 125k context window. Licensing differs: Phi-3.5 Mini Instruct is MIT while Llama 3.2 3B Instruct is Llama 3.2 Community.
- Which model fits in 24 GB of VRAM, Phi-3.5 Mini Instruct or Llama 3.2 3B Instruct?
- Both fit in 24 GB of VRAM at Q4_K_M — Phi-3.5 Mini Instruct needs 5.7 GB and Llama 3.2 3B Instruct needs 2.8 GB.