mixedbread-ai/mxbai-embed-large-v1
mixedbread-ai/mxbai-embed-large-v1
Parameters
333.2M
Context
512
Downloads
4.1M
Likes
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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 0.5445
1× RTX 4060 Ti
8 GB VRAM
≈ 3600 vec/s
Effective production throughput ≈ 2880 vectors/s
score 0.5444
8× RTX 5060 Ti
64 GB VRAM
≈ 44800 vec/s
Effective production throughput ≈ 35840 vectors/s
score 0.3487
2× RTX 4060 Ti
16 GB VRAM
≈ 7200 vec/s
Effective production throughput ≈ 5760 vectors/s
score 0.4475
💬 Community — real deployment experience
0 Articles · 0 Benchmarks
GPUs that fit (single card)
RTX 3080
10 GB VRAM
$58.6
/mo · vast
Tesla V100
16 GB VRAM
$64.8
/mo · vast
RTX 4070S Ti
16 GB VRAM
$68.5
/mo · vast
RTX 4060 Ti
8 GB VRAM
$78.9
/mo · vast
RTX 3080 Ti
12 GB VRAM
$79.5
/mo · vast