Qwen3-Max-Thinking state-of-the-art reasoning model at your fingertips!
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.
GLM-4.6 API: Get fast first tokens at the best $/M from Deepinfra's API - Deep Infra<p>GLM-4.6 is a high-capacity, “reasoning”-tuned model that shows up in coding copilots, long-context RAG, and multi-tool agent loops. With this class of workload, provider infrastructure determines perceived speed (first-token time), tail stability, and your unit economics. Using ArtificialAnalysis (AA) provider charts for GLM-4.6 (Reasoning), DeepInfra (FP8) pairs a sub-second Time-to-First-Token (TTFT) (0.51 s) with the […]</p>
GLM-4.6 vs DeepSeek-V3.2: Performance, Benchmarks & DeepInfra Results<p>The open-source LLM ecosystem has evolved rapidly, and two models stand out as leaders in capability, efficiency, and practical usability: GLM-4.6, Zhipu AI’s high-capacity reasoning model with a 200k-token context window, and DeepSeek-V3.2, a sparsely activated Mixture-of-Experts architecture engineered for exceptional performance per dollar. Both models are powerful. Both are versatile. Both are widely adopted […]</p>
NVIDIA Nemotron API Pricing Guide 2026<p>While everyone knows Llama 3 and Qwen, a quieter revolution has been happening in NVIDIA’s labs. They have been taking standard Llama models and “supercharging” them using advanced alignment techniques and pruning methods. The result is Nemotron—a family of models that frequently tops the “Helpfulness” leaderboards (like Arena Hard), often beating GPT-4o while being significantly […]</p>
© 2026 Deep Infra. All rights reserved.