# Deploy multilingual-e5-large-instruct on dedicated GPUs

multilingual-e5-large-instruct by intfloat, deployable on sference as a dedicated deployment: reserved GPU capacity, private endpoint, European GPUs, billed per GPU-hour on monthly commitments. Request it at https://sference.com/models/intfloat/multilingual-e5-large-instruct.

## Specs

- Hugging Face: https://huggingface.co/intfloat/multilingual-e5-large-instruct
- Category: Embeddings
- Parameters: 560M
- Architecture: Dense
- Context: 1K tokens
- Published precision: FP16
- Checkpoint size: 1.1 GB
- Minimum footprint: 1 GPU (80 GB) or 1 GPU (141 GB) to load the weights; more for long contexts and concurrency
- License: MIT (Commercial use allowed)

## FAQ

### Can I run multilingual-e5-large-instruct on a dedicated deployment in Europe?

Yes. sference deploys multilingual-e5-large-instruct on GPU capacity reserved for you, behind a private endpoint, with every request processed on European GPUs and logged in your audit trail. Tell us your expected volume and latency targets with the form on this page and we come back with a sizing and a quote.

### How many GPUs does multilingual-e5-large-instruct need?

The published checkpoint is about 1.1 GB (FP16), so loading it takes at least 1 GPU with 80 GB of memory each, or 1 GPU with 141 GB each. Long contexts and high concurrency need more for the KV cache; quantization needs less. You don't pick hardware on sference: you choose a performance tier and we size the deployment.

### Is multilingual-e5-large-instruct licensed for commercial use?

multilingual-e5-large-instruct is published under the MIT license. Permissive: commercial use, modification, and private deployment allowed; keep the copyright notice.

### How is a dedicated deployment priced?

Per GPU-hour on a monthly commitment, sized to your throughput and latency targets.

Facts from Hugging Face, synced 2026-10-27. All deployable models: https://sference.com/dedicated.md