Compare
SmolLM2 1.7B 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 | SmolLM2 1.7B Instruct | Llama 3.1 8B Instruct |
|---|---|---|
| Organization | Hugging Face | Meta |
| Parameters | 1.7B | 8B |
| Architecture | Llama · dense | Llama · dense |
| Context | 8k | 128k |
| Layers | 24 | 32 |
| Hidden size | 2,048 | 4,096 |
| KV heads | 32 of 32 | 8 of 32 |
| Licence | Apache 2.0 | Llama 3.1 Community License |
| Commercial use | Yes | Yes |
| Modalities | text | text |
| Released | 2024-11-01 | 2024-07-23 |
| Recommended quantization | F16 | Q8_0 |
| Memory needed | 5.2 GB | 9.5 GB |
| Estimated tok/s | 229 | 92 |
| Fit | Excellent 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
SmolLM2 1.7B Instruct
Fully open training data and recipe. A good base when you want to understand exactly what the model saw.
Strengths
- Completely open training pipeline
- Apache 2.0
- Very cheap to fine-tune
Limitations
- 8k context
- Limited capability without fine-tuning
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