Models · Alibaba NLP

Deploy Alibaba NLP models on dedicated GPUs

4 Alibaba NLP models ready to deploy on GPU capacity reserved for you, from 149M to 31B parameters, covering chat & reasoning, embeddings and rerankers. Private OpenAI-compatible endpoint, European GPUs, billed per GPU-hour.

ModelParams
Alibaba NLP305M
Alibaba NLP31B
Alibaba NLP150M
Alibaba NLP149M

Specs from Hugging Face, synced 2026-10-27. Machine-readable version: https://sference.com/models/Alibaba-NLP.md

FAQ

Which Alibaba NLP models can I deploy on sference?

4 today, most requested first: gte-multilingual-base, Tongyi-DeepResearch-30B-A3B, gte-reranker-modernbert-base and gte-modernbert-base. Any other Alibaba NLP checkpoint or a fine-tune of one can be requested with the form on this page.

How many GPUs do Alibaba NLP models need?

It depends on the model: the lineup runs from 149M to 31B parameters. Each model page lists the checkpoint size and the minimum footprint on 80 GB and 141 GB GPUs. You don't pick hardware on sference: you choose a performance tier and we size the deployment.

Can I use Alibaba NLP models commercially?

4 of the 4 listed models are under licenses that allow commercial use (Apache 2.0 across the lineup). Each model page summarizes its license and links the full text.

How is a dedicated deployment priced?

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

Request a Alibaba NLP model

Tell us which Alibaba NLP model and roughly how much traffic you expect. Fine-tunes of these models work the same way.

  1. 01
    We size it

    A performance tier, the capacity behind it, and a quote for your volume.

  2. 02
    We benchmark it

    Bring one workload with a sample of real traffic; we come back with latency and throughput numbers.

  3. 03
    It goes live

    We provision the deployment. You call it with the same API and model id as before.

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