BAAI · Embeddings and rerank · 568M parameters · Apache-2.0
bge-reranker-v2-m3
Needs 1 GB at FP8 / INT8. The smallest real configuration today is 1× Quadro P2000 at Vast.ai, $0.03 per hour, about $22 a month at 730 hours.
Parameters568Mdense
Context8k tokensmaximum published
Checkpoint2 GBFP32, as published
LicenseApache-2.0check before commercial use
ReleasedMar 15, 2024documented date
Smallest configuration · FP8 / INT8
1× Quadro P20005 GB VRAM · 14% used
- Per hour
- $0.03
- Per month
- $22
- Cheapest at
Vast.ai
What is documented about bge-reranker-v2-m3
news checked 2026-10-2714.6M downloads/month, the most-used open reranker, but missing from the 2026-10-04 catalogue. Date = HF repo creation.
- https://huggingface.co/BAAI/bge-reranker-v2-m3
- https://huggingface.co/api/models/BAAI/bge-reranker-v2-m3?blobs=true
How much VRAM it needs
Weights plus runtime overhead, then the KV cache that grows with the context you serve.
| Precision | Weights | Weights + headroom | 4k context | 8k context |
|---|---|---|---|---|
| FP32published checkpoint | 2 GB | 3 GB | 4 GB | 4 GB |
| FP8 / INT8estimated | 1 GB | 1 GB | 2 GB | 2 GB |
| INT4 / MXFP4estimated | 0 GB | 1 GB | 1 GB | 2 GB |
| KV cacheone sequence, FP16 cache, included above | — | — | 1 GB | 1 GB |
Cross-checked on Hugging Face Oct 28, 2026: 0.57B parameters in the safetensors headers, 2.3 GB of weight files, attention layout from config.json (24 layers, 16 KV heads × 64). Method: weights as published at the native precision, verified on Hugging Face 2026-10-28; other precisions estimated at 1 (8-bit) and 0.6 (4-bit) bytes per parameter; × 1.2 runtime headroom; KV cache = 4096 bytes per token per layer × 24 full-attention layers (FP16 cache, one sequence).config.json ↗
What it costs to run, GPU by GPU
The fewest GPUs of one type whose total memory fits the model at short context, priced from the cheapest eligible on-demand offer for exactly that count (45-day window, newest reading per provider), with the next providers beside it. Monthly = 730 hours.
| GPU | Smallest config | VRAM used | $ / hour | $ / month | Cheapest at |
|---|---|---|---|---|---|
| Quadro P2000Best fit | 1× 5 GB5 GB total | 14% | $0.03 | $22 | |
| RTX 3060 | 1× 12 GB12 GB total | 6% | $0.04 | $26 | |
| RTX A2000 | 1× 12 GB12 GB total | 6% | $0.04 | $26 | |
| GTX 1660 Ti | 1× 6 GB6 GB total | 11% | $0.04 | $26 | |
| GTX 1050 | 1× 2 GB2 GB total | 34% | $0.04 | $27 | |
| Tesla P4 | 1× 8 GB8 GB total | 9% | $0.04 | $29 | |
| T4 | 1× 16 GB16 GB total | 4% | $0.04 | $29 | |
| GTX 1060 | 1× 6 GB6 GB total | 11% | $0.04 | $30 | |
| GTX 1660 | 1× 6 GB6 GB total | 11% | $0.04 | $30 | |
| Tesla P40 | 1× 24 GB24 GB total | 3% | $0.04 | $30 | |
| GTX Titan X | 1× 12 GB12 GB total | 6% | $0.04 | $31 | |
| Titan Xp | 1× 12 GB12 GB total | 6% | $0.04 | $31 | |
| GTX 1080 | 1× 8 GB8 GB total | 9% | $0.04 | $32 | |
| Tesla P100 | 1× 16 GB16 GB total | 4% | $0.04 | $33 | |
| RTX 2060 | 1× 6 GB6 GB total | 11% | $0.04 | $33 | |
| GTX 1650 | 1× 4 GB4 GB total | 17% | $0.05 | $35 | |
| GTX 1070 | 1× 8 GB8 GB total | 9% | $0.05 | $36 | |
| GTX 1660 Super | 1× 6 GB6 GB total | 11% | $0.05 | $38 | |
| Quadro P4000 | 1× 8 GB8 GB total | 9% | $0.06 | $40 | |
| RTX 2070 | 1× 8 GB8 GB total | 9% | $0.06 | $41 | |
| RTX 3060 Ti | 1× 8 GB8 GB total | 9% | $0.06 | $41 | |
| RTX 2060 Super | 1× 8 GB8 GB total | 9% | $0.06 | $41 | |
| GTX 1070 Ti | 1× 8 GB8 GB total | 9% | $0.06 | $44 | |
| RTX 2070 Super | 1× 8 GB8 GB total | 9% | $0.06 | $44 | |
| The 24 cheapest of 109 GPU types that fit. Every GPU, any precision and context, in the finder | |||||
