Compare
BGE-M3 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 | BGE-M3 | Llama 3.1 8B Instruct |
|---|---|---|
| Organization | BAAI | Meta |
| Parameters | 0.568B | 8B |
| Architecture | XLM-RoBERTa · dense | Llama · dense |
| Context | 8k | 128k |
| Layers | 24 | 32 |
| Hidden size | 1,024 | 4,096 |
| KV heads | 16 of 16 | 8 of 32 |
| Licence | MIT | Llama 3.1 Community License |
| Commercial use | Yes | Yes |
| Modalities | embedding | text |
| Released | 2024-01-30 | 2024-07-23 |
| Recommended quantization | F16 | Q8_0 |
| Memory needed | 2.3 GB | 9.5 GB |
| Estimated tok/s | 686 | 92 |
| Fit | Excellent fit | Excellent fit |
| Fine-tune here | No | 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
BGE-M3
Multilingual retrieval across 100+ languages, with dense, sparse and multi-vector output from one model.
Strengths
- Genuinely multilingual retrieval
- Hybrid dense + sparse scoring
- 8k context
Limitations
- Heavier than Nomic Embed
- Large vocabulary increases memory
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