Llama 3.1 8B Instruct vs Gemma 2 9B Instruct
Side-by-side VRAM requirements, benchmark scores, and GPU compatibility for local AI inference.
Quick verdict
Llama 3.1 8B Instruct is more hardware-efficient — it needs 5.7 GB at Q4_K_M vs 8.3 GB for Gemma 2 9B Instruct, fitting on 66 GPUs natively.
VRAM at each quantization (8k context)
| Quant | Llama 3.1 8B Instruct | Gemma 2 9B Instruct | Diff |
|---|---|---|---|
| FP16 | 19.1 GB | 23.8 GB | -20% |
| Q8 | 10.2 GB | 13.5 GB | -25% |
| Q6_K | 7.9 GB | 10.9 GB | -27% |
| Q5_K_M | 6.8 GB | 9.6 GB | -29% |
| Q4_K_M | 5.7 GB | 8.3 GB | -32% |
| Q3_K_M | 4.8 GB | 7.3 GB | -34% |
| Q2_K | 3.9 GB | 6.2 GB | -38% |
Diff is Llama 3.1 8B Instruct relative to Gemma 2 9B Instruct. Green = lower VRAM (fits more GPUs).
Model specifications
| Spec | Llama 3.1 8B Instruct | Gemma 2 9B Instruct |
|---|---|---|
| Org | Meta | |
| Parameters | 8B | 9.2B |
| Architecture | Dense | Dense |
| Context | 125k tokens | 8k tokens |
| Modalities | text | text |
| License | Llama 3.1 Community | Gemma |
| Commercial | Yes | Yes |
| Released | 2024-07-23 | 2024-06-27 |
| GPUs (native) | 66 / 67 | 63 / 67 |
Benchmark scores
| Benchmark | Llama 3.1 8B Instruct | Gemma 2 9B Instruct |
|---|---|---|
| MMLU-Pro | 37.5 | 32.0 |
| GPQA | 30.4 | 31.5 |
| IFEval | 77.4 | 74.4 |
| MATH | 48.0 | 44.3 |
| HumanEval | 72.6 | 60.4 |
| Arena ELO | 1176.0 | 1190.0 |
Green = higher score (better). — = not yet available.
GPUs that run only Llama 3.1 8B Instruct(3)
- Apple M3 (8GB)8 GB
- Apple M2 (8GB)8 GB
- Apple M1 (8GB)8 GB
GPUs that run only Gemma 2 9B Instruct(0)
Every GPU that runs Gemma 2 9B Instruct also runs Llama 3.1 8B Instruct.
GPUs that run both natively(63)
- 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
- +51 more GPUs run both
Which should you use?
Choose Llama 3.1 8B Instruct if:
- • You have limited VRAM — it's a smaller model needing 5.7 GB vs 8.3 GB
- • Long context matters — it supports 125k tokens vs 8k
- • Benchmark quality matters — scores 37.5 vs 32.0 on MMLU-Pro
Choose Gemma 2 9B Instruct if:
- • You want maximum capability and have a 9 GB+ GPU
Frequently asked questions
- Which is better, Llama 3.1 8B Instruct or Gemma 2 9B Instruct?
- Llama 3.1 8B Instruct has 8B parameters vs 9.2B for Gemma 2 9B Instruct, so Gemma 2 9B Instruct is the larger model. Llama 3.1 8B Instruct is more hardware-efficient, needing 5.7 GB at Q4_K_M vs 8.3 GB. Llama 3.1 8B Instruct runs on more GPUs natively (66 vs 63). On MMLU-Pro, Llama 3.1 8B Instruct scores higher (37.5 vs 32.0).
- How much VRAM does Llama 3.1 8B Instruct need vs Gemma 2 9B Instruct?
- At Q4_K_M quantization with 8k context, Llama 3.1 8B Instruct needs approximately 5.7 GB of VRAM, while Gemma 2 9B Instruct needs 8.3 GB. At FP16, Llama 3.1 8B Instruct requires 19.1 GB vs 23.8 GB for Gemma 2 9B Instruct.
- Can you run Llama 3.1 8B Instruct on the same GPUs as Gemma 2 9B Instruct?
- Yes, 63 GPUs can run both natively in VRAM, including NVIDIA RTX 5090, NVIDIA RTX 4090, NVIDIA RTX 4080. However, 3 GPUs can run Llama 3.1 8B Instruct but not Gemma 2 9B Instruct, and no GPU can run Gemma 2 9B Instruct without also fitting Llama 3.1 8B Instruct.
- What is the difference between Llama 3.1 8B Instruct and Gemma 2 9B Instruct?
- Llama 3.1 8B Instruct has 8B parameters (dense) with a 125k context window. Gemma 2 9B Instruct has 9.2B parameters (dense) with a 8k context window. Licensing differs: Llama 3.1 8B Instruct is Llama 3.1 Community while Gemma 2 9B Instruct is Gemma.
- Which model fits in 24 GB of VRAM, Llama 3.1 8B Instruct or Gemma 2 9B Instruct?
- Both fit in 24 GB of VRAM at Q4_K_M — Llama 3.1 8B Instruct needs 5.7 GB and Gemma 2 9B Instruct needs 8.3 GB.