BGE Large EN v1.5 vs Nomic Embed Text v1.5
Side-by-side comparison of hardware requirements, quantization options, and specifications to help you choose the right model for your device.
Specifications Comparison
| Spec | BGE Large EN v1.5 | Nomic Embed Text v1.5 |
|---|---|---|
| Parameters | 0.335B | 0.137B |
| Architecture | bert | nomic-bert |
| License | MIT | Apache 2.0 |
| Context Length | 1K tokens | 8K tokens |
| Category | Embedding | Embedding |
| Author | BAAI | Nomic AI |
| HF Downloads | 7.9M | 10.9M |
| VRAM Range | 0.83 - 1.12 GB | 0.3 - 0.76 GB |
| Quantizations | 2 options | 2 options |
| Best Quality Score | 100% | 100% |
Quantization Options
BGE Large EN v1.5
Q8_0
0.3 GB0.83 GB VRAM98% quality
FP16
0.6 GB1.12 GB VRAM100% quality
Nomic Embed Text v1.5
Q8_0
0.1 GB0.3 GB VRAM98% quality
FP16
0.3 GB0.76 GB VRAM100% quality
Which Should You Choose?
Nomic Embed Text v1.5 is more memory-efficient, starting at just 0.3GB VRAM compared to 0.83GB for BGE Large EN v1.5. BGE Large EN v1.5 is significantly larger at 0.335B parameters, which generally means better output quality but requires more hardware.