Compare
Gemma 3 27B Instruct vs Llama 3.1 8B Instruct
Same hardware, same arithmetic, side by side. Every memory figure is calculated from published model geometry rather than quoted from a marketing page.
Estimated
Side by side
On GeForce RTX 4090
| Property | Gemma 3 27B Instruct | Llama 3.1 8B Instruct |
|---|---|---|
| Organization | Meta | |
| Parameters | 27.4B | 8B |
| Architecture | Gemma3 · dense | Llama · dense |
| Context | 128k | 128k |
| Layers | 62 | 32 |
| Hidden size | 5,376 | 4,096 |
| KV heads | 16 of 32 | 8 of 32 |
| Licence | Gemma Terms of Use | Llama 3.1 Community License |
| Commercial use | Yes | Yes |
| Modalities | text, vision | text |
| Released | 2025-03-12 | 2024-07-23 |
| Recommended quantization | Q4_K_M | Q8_0 |
| Memory needed | 21 GB | 9.5 GB |
| Estimated tok/s | 47 | 92 |
| Fit | Tight fit | Excellent fit |
| Fine-tune here | Yes | Yes |
| MMLU (reported) | — | 69.4% |
| HUMANEVAL (reported) | — | 72.6% |
Benchmark rows are figures the model's authors published, not ModelLM measurements, and the two models may not have been evaluated under identical conditions.
Trade-offs
Gemma 3 27B Instruct
The largest Gemma 3. Multimodal, long-context, and designed to run on a single high-memory accelerator.
Strengths
- Strong multimodal quality
- Excellent multilingual performance
- 128k context
Limitations
- 16 KV heads make its cache larger than peers
- Needs 24 GB+ at Q4_K_M
Llama 3.1 8B Instruct
The most widely supported open model there is. If a tool, adapter or tutorial exists, it was written for this one first.
Strengths
- Unmatched ecosystem support
- 128k context
- Very stable fine-tuning behaviour
Limitations
- Benchmarks now behind newer 7–9B models
- Community licence with an acceptable-use policy
Common comparisons