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…

DeepInfra raises $107M Series B to scale the inference cloud — read the announcement

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
DeepInfra Launches Access to NVIDIA Nemotron Models for Vision, Retrieval, and AI SafetyDeepInfra Launches Access to NVIDIA Nemotron Models for Vision, Retrieval, and AI SafetyDeepInfra is serving the new, open NVIDIA Nemotron vision language and OCR AI models from day zero of their release. As a leading inference provider committed to performance and cost-efficiency, we're making these cutting-edge models available at the industry's best prices, empowering developers to build specialized AI agents without compromising on budget or performance.
DeepSeek V4 Pro Is Now Available on DeepInfraDeepSeek V4 Pro Is Now Available on DeepInfra<p>DeepSeek released V4 Pro on April 24, 2026 — a 1.6 trillion-parameter Mixture of Experts model with 49 billion active parameters, a 1-million-token context window, and weights available on Hugging Face under an MIT license. On LiveCodeBench, the V4-Pro-Max reasoning variant scores 93.5 Pass@1, leading every model in the comparison set, including Gemini-3.1-Pro High at [&hellip;]</p>
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...