We use essential cookies to make our site work. With your consent, we may also use non-essential cookies to improve user experience and analyze website traffic…

GLM-5.1 - state-of-the-art agentic engineering, now available on DeepInfra!

Inference LoRA adapter model
Published on 2024.12.06 by Askar Aitzhan
Inference LoRA adapter model

Understanding LoRA inference

Concepts

  • Base model: The original model that is used as a starting point.
  • LoRA adapter model: A small model that is used to adapt the base model for a specific task.
  • LoRA Rank: The rank of the matrix that is used to adapt the model.

What you need to inference with LoRA adapter model

  1. Supported base model
  2. LoRA adapter model hosted on HuggingFace
  3. HuggingFace token if the LoRA adapter model is private
  4. DeepInfra account

How to inference with LoRA adapter in DeepInfra

  1. Go to the dashboard
  2. Click on the 'New Deployment' button
  3. Click on the 'LoRA Model' tab
  4. Fill the form:
    • LoRA model name: model name used to reference the deployment
    • Hugging Face Model Name: Hugging Face model name
    • Hugging Face Token: (optional) Hugging Face token if the LoRA adapter model is private
  5. Click on the 'Upload' button

Note: The list of supported base models is listed on the same page. If you need a base model that is not listed, please contact us at feedback@deepinfra.com

Rate limits on LoRA adapter model

Rate limit will apply on combined traffic of all LoRA adapter models with the same base model. For example, if you have 2 LoRA adapter models with the same base model, and have rate limit of 200. Those 2 LoRA adapter models combined will have rate limit of 200.

Pricing on LoRA adapter model

Pricing is 50% higher than base model.

How is LoRA adapter model speed compared to base model speed?

LoRA adapter model speed is lower than base model, because there is additional compute and memory overhead to apply the LoRA adapter. From our benchmarks, the LoRA adapter model speed is about 50-60% slower than base model.

How to make LoRA adapter model faster?

You could merge the LoRA adapter with the base model to reduce the overhead. And use custom deployment, the speed will be close to the base model.

Related articles
Qwen3.5 122B A10B API Benchmarks: Latency, Throughput & CostQwen3.5 122B A10B API Benchmarks: Latency, Throughput & Cost<p>About Qwen3.5 122B A10B Qwen3.5 122B A10B is Alibaba Cloud&#8217;s mid-tier multimodal foundation model, released in February 2026. It is a multimodal vision-language Mixture-of-Experts model supporting text, image, and video inputs, designed for native multimodal agent applications. It features 122 billion total parameters with 10 billion activated per token through a hybrid architecture that integrates [&hellip;]</p>
A Milestone on Our Journey Building Deep Infra and Scaling Open Source AI InfrastructureA Milestone on Our Journey Building Deep Infra and Scaling Open Source AI InfrastructureToday we're excited to share that Deep Infra has raised $18 million in Series A funding, led by Felicis and our earliest believer and advisor Georges Harik.
Deploy Custom LLMs on DeepInfraDeploy Custom LLMs on DeepInfraDid you just finetune your favorite model and are wondering where to run it? Well, we have you covered. Simple API and predictable pricing. Put your model on huggingface Use a private repo, if you wish, we don't mind. Create a hf access token just for the repo for better security. Create c...