Open-weight LLM comparison
Gemma 2 27B vs Llama 3.1 405B
Side-by-side
Straight from each model's release page and Hugging Face card. Memory shown is for the weights alone: add roughly 15% for real serving. Bold column marks the more parameters and the smaller memory footprint.
| Field | Gemma 2 27B | Llama 3.1 405B |
|---|---|---|
| Total parameters | 27 B | 405 B |
| Architecture | dense | dense |
| Vendor | Meta | |
| License | Gemma Terms of Use | Llama 3.1 Community License |
| Released | 2024-06-27 | 2024-07-23 |
| Weights @ FP16 | 54 GB | 810 GB |
| Weights @ INT8 | 27 GB | 405 GB |
| Weights @ INT4 | 14 GB | 203 GB |
Which is smarter (published benchmarks)
Vendor-reported quality scores on the standard leaderboards. Bold column marks the higher score on the same test.
| Benchmark | Gemma 2 27B | Llama 3.1 405B |
|---|---|---|
| MMLU (5-shot) | 75.2 | 88.6 |
| HumanEval | 51.8 | 89.0 |
| MATH (0-shot) | 42.3 | 73.8 |
| GPQA | 25.3 | 51.1 |
Sources: Gemma 2 27B model card · Llama 3.1 405B model card. Benchmark methodology and prompt template can shift these numbers by several points, so treat these as relative rankings, not absolute scores.
Common questions
Gemma 2 27B vs Llama 3.1 405B: which is bigger?
Llama 3.1 405B has more parameters (Gemma 2 27B: 27 B; Llama 3.1 405B: 405 B). More parameters usually means higher ceiling on capability and higher memory requirement, though MoE architectures decouple total parameters from per-token compute.
Gemma 2 27B vs Llama 3.1 405B: which is newer?
Llama 3.1 405B released 2024-07-23; Gemma 2 27B released 2024-06-27.
Gemma 2 27B vs Llama 3.1 405B: which needs less memory to serve?
Gemma 2 27B needs less HBM. Weights-only footprint at FP16: Gemma 2 27B 54 GB; Llama 3.1 405B 810 GB. Half those numbers at INT8, quarter at INT4. Real serving adds 10-30% for KV cache.
Gemma 2 27B vs Llama 3.1 405B: which license is more permissive?
Gemma 2 27B: Gemma Terms of Use. Llama 3.1 405B: Llama 3.1 Community License. Apache 2.0 and MIT allow unrestricted commercial use; Llama Community License allows commercial use but restricts training larger models on outputs; CC-BY-NC and vendor-specific licenses (Qwen 72B, Gemma) have narrower terms. Check the model card for the exact clauses.
See also: every LLM comparison · Gemma 2 27B full page · Llama 3.1 405B full page.