Nemotron 3 Nano Omni — the first multimodal model in the Nemotron 3 family, now on DeepInfra!
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
- Supported base model
- LoRA adapter model hosted on HuggingFace
- HuggingFace token if the LoRA adapter model is private
- DeepInfra account
How to inference with LoRA adapter in DeepInfra
- Go to the dashboard
- Click on the 'New Deployment' button
- Click on the 'LoRA Model' tab
- 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
- 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.
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