Gemma 2 2B Instruct vs Llama 3.2 1B Instruct
Side-by-side VRAM requirements, benchmark scores, and GPU compatibility for local AI inference.
Quick verdict
Llama 3.2 1B Instruct is more hardware-efficient — it needs 1.0 GB at Q4_K_M vs 2.4 GB for Gemma 2 2B Instruct, fitting on 66 GPUs natively.
VRAM at each quantization (8k context)
| Quant | Gemma 2 2B Instruct | Llama 3.2 1B Instruct | Diff |
|---|---|---|---|
| FP16 | 6.8 GB | 3.1 GB | +121% |
| Q8 | 3.9 GB | 1.7 GB | +130% |
| Q6_K | 3.2 GB | 1.3 GB | +136% |
| Q5_K_M | 2.8 GB | 1.2 GB | +139% |
| Q4_K_M | 2.4 GB | 1.0 GB | +145% |
| Q3_K_M | 2.1 GB | 0.9 GB | +150% |
| Q2_K | 1.9 GB | 0.7 GB | +158% |
Diff is Gemma 2 2B Instruct relative to Llama 3.2 1B Instruct. Green = lower VRAM (fits more GPUs).
Model specifications
| Spec | Gemma 2 2B Instruct | Llama 3.2 1B Instruct |
|---|---|---|
| Org | Meta | |
| Parameters | 2.6B | 1.24B |
| Architecture | Dense | Dense |
| Context | 8k tokens | 125k tokens |
| Modalities | text | text |
| License | Gemma | Llama 3.2 Community |
| Commercial | Yes | Yes |
| Released | 2024-07-31 | 2024-09-25 |
| GPUs (native) | 66 / 67 | 66 / 67 |
Benchmark scores
| Benchmark | Gemma 2 2B Instruct | Llama 3.2 1B Instruct |
|---|---|---|
| MMLU-Pro | 17.8 | 12.5 |
| IFEval | 55.8 | 59.5 |
| MATH | 25.0 | 30.6 |
| HumanEval | 40.2 | 38.4 |
Green = higher score (better). — = not yet available.
GPUs that run only Gemma 2 2B Instruct(0)
Every GPU that runs Gemma 2 2B Instruct also runs Llama 3.2 1B Instruct.
GPUs that run only Llama 3.2 1B Instruct(0)
Every GPU that runs Llama 3.2 1B Instruct also runs Gemma 2 2B 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 2 2B Instruct if:
- • You want maximum capability and have a 3 GB+ GPU
- • Benchmark quality matters — scores 17.8 vs 12.5 on MMLU-Pro
Choose Llama 3.2 1B Instruct if:
- • You have limited VRAM — it's a smaller model needing 1.0 GB vs 2.4 GB
- • Long context matters — it supports 125k tokens vs 8k
Frequently asked questions
- Which is better, Gemma 2 2B Instruct or Llama 3.2 1B Instruct?
- Gemma 2 2B Instruct has 2.6B parameters vs 1.24B for Llama 3.2 1B Instruct, so Gemma 2 2B Instruct is the larger model. Llama 3.2 1B Instruct is more hardware-efficient, needing 1.0 GB at Q4_K_M vs 2.4 GB. On MMLU-Pro, Gemma 2 2B Instruct scores higher (17.8 vs 12.5).
- How much VRAM does Gemma 2 2B Instruct need vs Llama 3.2 1B Instruct?
- At Q4_K_M quantization with 8k context, Gemma 2 2B Instruct needs approximately 2.4 GB of VRAM, while Llama 3.2 1B Instruct needs 1.0 GB. At FP16, Gemma 2 2B Instruct requires 6.8 GB vs 3.1 GB for Llama 3.2 1B Instruct.
- Can you run Gemma 2 2B Instruct on the same GPUs as Llama 3.2 1B 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 2 2B Instruct without also fitting Llama 3.2 1B Instruct, and no GPU can run Llama 3.2 1B Instruct without also fitting Gemma 2 2B Instruct.
- What is the difference between Gemma 2 2B Instruct and Llama 3.2 1B Instruct?
- Gemma 2 2B Instruct has 2.6B parameters (dense) with a 8k context window. Llama 3.2 1B Instruct has 1.24B parameters (dense) with a 125k context window. Licensing differs: Gemma 2 2B Instruct is Gemma while Llama 3.2 1B Instruct is Llama 3.2 Community.
- Which model fits in 24 GB of VRAM, Gemma 2 2B Instruct or Llama 3.2 1B Instruct?
- Both fit in 24 GB of VRAM at Q4_K_M — Gemma 2 2B Instruct needs 2.4 GB and Llama 3.2 1B Instruct needs 1.0 GB.