Cross-encoder

cross-encoder/ms-marco-MiniLM-L6-v2

cross-encoder/ms-marco-MiniLM-L6-v2

Parameters

22.3M

Context

512

Downloads

59.3M

Likes

446

Architecture

bert

6 layers · head 32 · hidden 384

View on HuggingFace

GPU Requirements

VRAM / RAM / disk estimates by quantization and usage scenario

QuantizationMinimumRecommendedProduction
FP1616bit
2.3 GB
RAM 32.0 · Disk 39.1
3.0 GB
RAM 32.0 · Disk 65.1
6.7 GB
RAM 32.0 · Disk 104.1
FP88bit
2.3 GB
RAM 32.0 · Disk 39.0
2.9 GB
RAM 32.0 · Disk 65.0
6.7 GB
RAM 32.0 · Disk 104.0
INT88bit
2.3 GB
RAM 32.0 · Disk 39.0
2.9 GB
RAM 32.0 · Disk 65.0
6.7 GB
RAM 32.0 · Disk 104.0
AWQ4bit
2.3 GB
RAM 32.0 · Disk 39.0
2.9 GB
RAM 32.0 · Disk 65.0
6.7 GB
RAM 32.0 · Disk 104.0
GPTQ4bit
2.3 GB
RAM 32.0 · Disk 39.0
2.9 GB
RAM 32.0 · Disk 65.0
6.7 GB
RAM 32.0 · Disk 104.0
GGUF4bit
2.3 GB
RAM 32.0 · Disk 39.0
2.9 GB
RAM 32.0 · Disk 65.0
6.7 GB
RAM 32.0 · Disk 104.0

GPU Recommendations

Best ValueBEST

1× RTX 3080 Ti

12 GB VRAM

$78.8/mo

≈ 91200 tok/s

Effective production throughput ≈ 63840 tok/s

score 0.5595

Deploy on vast ↗
Cheapest

1× Tesla V100

16 GB VRAM

$35.7/mo

≈ 0 tok/s (N/A — benchmark unavailable)

Effective production throughput ≈ 0 tok/s

score 0.5378

Deploy on vast ↗
Performance

8× H200 SXM

1128 GB VRAM

$20,440/mo

≈ 1536000 tok/s

Effective production throughput ≈ 1075200 tok/s

score 0.2197

Min Complexity

1× RTX 4060 Ti

8 GB VRAM

$59.2/mo

≈ 28800 tok/s

Effective production throughput ≈ 20160 tok/s

score 0.5878

Deploy on vast ↗

💬 Community — real deployment experience

0 Articles · 0 Benchmarks

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GPUs that fit (single card)

Tesla V100

16 GB VRAM

$35.7

/mo · vast

RTX 4060 Ti

8 GB VRAM

$59.2

/mo · vast

RTX 3080

10 GB VRAM

$63.8

/mo · vast

L4

24 GB VRAM

$76.2

/mo · vast

RTX 5060 Ti

8 GB VRAM

$78.3

/mo · vast

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