mixedbread-ai/mxbai-embed-large-v1
mixedbread-ai/mxbai-embed-large-v1
Parameters
333.2M
Context
512
Downloads
4.3M
Likes
820
Architecture
bert
24 layers · head 64 · hidden 1024
GPU Requirements
VRAM / RAM / disk estimates by quantization and usage scenario
| Quantization | Minimum | Recommended | Production |
|---|---|---|---|
| FP1616bit | 2.1 GB RAM 32.0 · Disk 40.0 | 2.3 GB RAM 32.0 · Disk 66.0 | 2.7 GB RAM 32.0 · Disk 105.0 |
| FP88bit | 1.7 GB RAM 32.0 · Disk 39.5 | 1.8 GB RAM 32.0 · Disk 65.5 | 2.2 GB RAM 32.0 · Disk 104.5 |
| INT88bit | 1.7 GB RAM 32.0 · Disk 39.5 | 1.8 GB RAM 32.0 · Disk 65.5 | 2.2 GB RAM 32.0 · Disk 104.5 |
| AWQ4bit | 1.4 GB RAM 32.0 · Disk 39.3 | 1.6 GB RAM 32.0 · Disk 65.3 | 1.9 GB RAM 32.0 · Disk 104.3 |
| GPTQ4bit | 1.4 GB RAM 32.0 · Disk 39.3 | 1.6 GB RAM 32.0 · Disk 65.3 | 1.9 GB RAM 32.0 · Disk 104.3 |
| GGUF4bit | 1.5 GB RAM 32.0 · Disk 39.3 | 1.6 GB RAM 32.0 · Disk 65.3 | 1.9 GB RAM 32.0 · Disk 104.3 |
GPU Recommendations
1× RTX 5060 Ti
8 GB VRAM
≈ 5600 vec/s
Effective production throughput ≈ 4480 vectors/s
score 0.5464
1× RTX 5060 Ti
8 GB VRAM
≈ 5600 vec/s
Effective production throughput ≈ 4480 vectors/s
score 0.5464
8× RTX 5060 Ti
64 GB VRAM
≈ 44800 vec/s
Effective production throughput ≈ 35840 vectors/s
score 0.3636
2× RTX 5060 Ti
16 GB VRAM
≈ 11200 vec/s
Effective production throughput ≈ 8960 vectors/s
score 0.451
💬 Community — real deployment experience
0 Articles · 0 Benchmarks
GPUs that fit (single card)
Tesla V100
16 GB VRAM
$20.3
/mo · vast
RTX 5060 Ti
8 GB VRAM
$39.7
/mo · vast
RTX 4070S Ti
16 GB VRAM
$49.5
/mo · vast
RTX 4080S
16 GB VRAM
$50.0
/mo · vast
RTX 5070
12 GB VRAM
$58.7
/mo · vast