Increase Throughput
Reduce Latency
Reduce Model Size
Reduce Memory Footprint

Key Features:
Automate Compilation & Post Training Quantization
Easily compile and quantize your models (FP16/INT8) and evaluate different production settings with a click of a button.
Build EfficientArchitectures
Build accurate & efficient architectures tailored for the application, hardware, and performance targets with Deci’s AutoNAC -a NAS based engine.
QuantizationAware Training
Boost performance without compromising on accuracy by quantizing your model to INT8 during the training process with one line of code.
AcceleratePipeline
Boost model serving performance with parallelism and concurrent execution. Compatible with multiple frameworks and hardware types.

Key Features:
Build Better Models Architectures
Achieve better accuracy with custom architecture built with Deci’s NAS engine - AutoNAC.
Maximize Accuracy with Advanced Training Techniques
Train models with the SuperGradients library and leverage custom training recipes with one line of code.

Key Features:
Find the Bottlenecks in your Model
Benchmark runtime performance of every layer in your neural network to find inference runtime bottlenecks in the architecture as it runs on your target inference Hardware.
Find the Best HW for the Job
Benchmark your models’ expected inference performance across multiple hardware types on Deci’s online hardware fleet. Get actionable insights for the ideal hardware and production settings.
Measure Performance Before You Train
Easily benchmark and compare the performance of different models on your target inference hardware against other hardware including GPUs, CPUs, and commercial edge devices.

Key Features:
Containerized Production Environment
Save time and hassle by getting a containerized inference engine that can be easily plugged into your production environment.
Seamless Portability with a Unified API
Easily switch between inference frameworks with zero code changes. Compatible with multiple frameworks and hardware types.