---
license: mit
base_model:
- intfloat/multilingual-e5-large-instruct
tags: [llmcompressor, compressed-tensors, quantized, fp8, vllm]
pipeline_tag: feature-extraction
library_name: vllm
---

# multilingual-e5-large-instruct-fp8

`FP8_DYNAMIC` quantization of [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct), produced with
[llm-compressor](https://github.com/vllm-project/llm-compressor) 0.13.0 and measured end to end on
NVIDIA H100 80 GB with vLLM 0.26.0.

Only the Linear layers of the encoder are quantized; the vocabulary, position and token-type
embeddings, the pooler and any classification head stay in bf16.

## Measured

| variant | size | SciFact nDCG@10 | STS-B de ρ | STS17 en-de ρ | texts/s (bs=256) |
|---|---|---|---|---|---|
| bf16 source | 1068 MiB | 0.7204 | 0.8347 | 0.8525 | 1488 |
| FP8 | 802 MiB | 0.7164 | 0.8349 | 0.8526 | 1511 |
| NVFP4 | 675 MiB | 0.7036 | 0.8371 | 0.8440 | – |

Retrieval and STS are unchanged against bf16. Size is **−25%**. Throughput is unchanged — the gain is memory only. If you want the smaller checkpoint, [the NVFP4 sibling](https://huggingface.co/mbehr90/multilingual-e5-large-instruct-nvfp4) is 675 MiB for 1.7 pp nDCG@10.

SciFact: 300 queries over 5183 documents, nDCG@10. STS-B de: mteb/stsb_multi_mt (1379 pairs).
STS17 en-de: mteb/sts17-crosslingual-sts (250 pairs). Reranking: 1 positive + 19 sampled negatives
per query. Throughput over 4096 multilingual texts through the vLLM OpenAI endpoint.


## Serving

```bash
vllm serve mbehr90/multilingual-e5-large-instruct-fp8 --runner pooling
```