Gemma 3 4B Instruct vs Llama 3.2 3B Instruct
Side-by-side VRAM requirements, benchmark scores, and GPU compatibility for local AI inference.
Quick verdict
Gemma 3 4B Instruct is more hardware-efficient — it needs 2.8 GB at Q4_K_M vs 2.8 GB for Llama 3.2 3B Instruct, fitting on 66 GPUs natively.
VRAM at each quantization (8k context)
| Quant | Gemma 3 4B Instruct | Llama 3.2 3B Instruct | Diff |
|---|---|---|---|
| FP16 | 9.5 GB | 8.2 GB | +16% |
| Q8 | 5.0 GB | 4.6 GB | +9% |
| Q6_K | 3.9 GB | 3.7 GB | +5% |
| Q5_K_M | 3.4 GB | 3.3 GB | +2% |
| Q4_K_M | 2.8 GB | 2.8 GB | -1% |
| Q3_K_M | 2.4 GB | 2.5 GB | -5% |
| Q2_K | 1.9 GB | 2.1 GB | -10% |
Diff is Gemma 3 4B Instruct relative to Llama 3.2 3B Instruct. Green = lower VRAM (fits more GPUs).
Model specifications
| Spec | Gemma 3 4B Instruct | Llama 3.2 3B Instruct |
|---|---|---|
| Org | Meta | |
| Parameters | 4B | 3.2B |
| Architecture | Dense | Dense |
| Context | 128k tokens | 125k tokens |
| Modalities | text, vision | text |
| License | Gemma | Llama 3.2 Community |
| Commercial | Yes | Yes |
| Released | 2025-03-12 | 2024-09-25 |
| GPUs (native) | 66 / 67 | 66 / 67 |
GPUs that run only Gemma 3 4B Instruct(0)
Every GPU that runs Gemma 3 4B 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 Gemma 3 4B 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 Gemma 3 4B Instruct if:
- • You want maximum capability and have a 3 GB+ GPU
- • Long context matters — it supports 128k tokens vs 125k
- • You need vision/image understanding
Choose Llama 3.2 3B Instruct if:
- • You have limited VRAM — it's a smaller model needing 2.8 GB vs 2.8 GB
Frequently asked questions
- Which is better, Gemma 3 4B Instruct or Llama 3.2 3B Instruct?
- Gemma 3 4B Instruct has 4B parameters vs 3.2B for Llama 3.2 3B Instruct, so Gemma 3 4B Instruct is the larger model. Gemma 3 4B Instruct is more hardware-efficient, needing 2.8 GB at Q4_K_M vs 2.8 GB.
- How much VRAM does Gemma 3 4B Instruct need vs Llama 3.2 3B Instruct?
- At Q4_K_M quantization with 8k context, Gemma 3 4B Instruct needs approximately 2.8 GB of VRAM, while Llama 3.2 3B Instruct needs 2.8 GB. At FP16, Gemma 3 4B Instruct requires 9.5 GB vs 8.2 GB for Llama 3.2 3B Instruct.
- Can you run Gemma 3 4B 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 Gemma 3 4B Instruct without also fitting Llama 3.2 3B Instruct, and no GPU can run Llama 3.2 3B Instruct without also fitting Gemma 3 4B Instruct.
- What is the difference between Gemma 3 4B Instruct and Llama 3.2 3B Instruct?
- Gemma 3 4B Instruct has 4B parameters (dense) with a 128k context window. Llama 3.2 3B Instruct has 3.2B parameters (dense) with a 125k context window. Licensing differs: Gemma 3 4B Instruct is Gemma while Llama 3.2 3B Instruct is Llama 3.2 Community.
- Which model fits in 24 GB of VRAM, Gemma 3 4B Instruct or Llama 3.2 3B Instruct?
- Both fit in 24 GB of VRAM at Q4_K_M — Gemma 3 4B Instruct needs 2.8 GB and Llama 3.2 3B Instruct needs 2.8 GB.