Llama 3.1 8BvsGemma 2 9B
Complete comparison between Llama 3.1 8B (8B parameters, Meta) and Gemma 2 9B (9B, Google). VRAM required for each quantization, tokens per second measured on 4 reference GPUs, verdict by use case, license, and installation commands. All figures are calculated from the catalog data—no copy-pasting between pages.
In brief
| Feature | Llama 3.1 8B | Gemma 2 9B |
|---|---|---|
| Settings | 8B | 9B |
| Family | Llama | Gemma |
| Author | Meta | |
| Origin | US | US |
| License | Llama 3 Community | Gemma |
| Context | 131,072 tokens | 8,192 tokens |
| Output | July 2024 | June 2024 |
Memory footprint
Approximate VRAM required for inference with an average context window. The winner (in green) is the model that uses less — smaller models have the edge.
| Quantization | Llama 3.1 8B | Gemma 2 9B |
|---|---|---|
| Q4_K_M (lightweight) | 6 GB | 6 GB |
| Q5_K_M (balanced) | 7 GB | 7.5 GB |
| Q8 (near-lossless) | 10 GB | 11 GB |
| FP16 (max quality) | 18 GB | 20 GB |
| CPU-only RAM | 10 GB | 12 GB |
Estimated speed (tokens/second)
Estimates based on the best quantization that can fit on each GPU. Actual figures depend on the prompt, context, and engine (llama.cpp, vLLM, MLX). Methodology.
| Reference GPU | Llama 3.1 8B | Gemma 2 9B |
|---|---|---|
| RTX 4090 (24 GB) | 80 tok/s · FP16 | 75 tok/s · FP16 |
| RTX 4080 (16 GB) | 30 tok/s · Q8 | 28 tok/s · Q8 |
| RTX 3060 12GB (12 GB) | 10 tok/s · Q8 | 9 tok/s · Q8 |
| Apple M4 Pro (48 GB) (36 GB) | 30 tok/s · FP16 | 28 tok/s · FP16 |
Verdict by use case
For each common use case, we indicate which of the two is better suited based on its tags, size, and specialization.
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Strengths and weaknesses
Llama 3.1 8B
The open-weight benchmark. 128k context, strong across the board.
- 128k context
- Excellent quality-to-size ratio
- Very good at coding and following instructions
- Meta license <700M MAU
- Outperformed by Qwen 3 8B on most benchmarks
- No vision
Installation
Gemma 2 9B
Llama 3 quality with a slightly larger footprint.
- Outperforms Llama 3 8B on several benchmarks
- License Gemma
- Good quality-to-size ratio
- 8192 context only
- No vision
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Llama 3.1 8B or Gemma 2 9B?
On an RTX 4090, Llama 3.1 8B runs at FP16 (~80 tok/s), Gemma 2 9B in FP16 (~75 tok/s). In pure throughput, Llama 3.1 8B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
Both require the same VRAM in Q4 (6 GB). The choice then comes down to the license or perceived quality.
Can these models be used in commercial production?
Llama 3.1 8B is under Llama 3 Community—check the restrictions, especially the monthly user thresholds. Gemma 2 9B is under Gemma — also check the terms. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Llama 3.1 8B (8B). More capable: Gemma 2 9B (9B). If you're unsure, run it configurator with your GPU and use case — it will decide based on both.
Alternatives to consider
If neither works for you, here are some neighboring models you could explore.