Cut Your Compute Cost with Faster LLM Inference

Optimize and run your models with Infery, Deci’s easy-to-use LLM inference SDK. 

Unparalleled Inference Performance

Achieve low latency, high throughput to improve user experience.

Cut Your Inference
Compute Spend

Maximize hardware utilization or migrate your workloads to more affordable cloud instances.

Simplify Deployment
with a Unified API

Streamline deployment. Run inference in 3 lines of code.

See Infery In Action

Infery for LLM Inference

3-10x faster LLM inference

Up to 95% lower compute cost

Easy to use

Compatible with SOTA models

Boost LLM Inference Performance Without Compromising on Accuracy

Optimized Kernels

Speed up the prefill and decoding stages of generation with custom kernels optimized for grouped query attention. Adaptable to various decoder architectures.

Continuous Batching

Ensures GPU is always decoding at a maximal batch size and that every generated token is used. Sequences dynamically group and swapped upon completion for efficient response generation.

Optimized Beam Search

Gain faster sequence-to-sequence prediction with efficient search mechanism. Supports all common generation parameters and is highly tuned for the target inference hardware.

Selective Quantization

Apply either FP16 or INT8 quantization only to the layers that are quantization friendlly to enjoy the speed-up of quantization while maintaining FP32 quality.

"Our advanced text to videos solution is powered by proprietary and complex generative AI algorithms. Deci allows us to reduce our cloud computing cost and improve our user experience with faster time to video by accelerating our models’ inference performance and maximizing GPU utilization on the cloud.”

Lior Hakim, Co-Founder & CTO
Hour One

Cut Your Compute Cost with Faster LLM Inference
Optimize and run your models with Infery, Deci’s easy-to-use LLM inference SDK.
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					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")

model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")