GPU’s for AI Startups

Artificial Intelligence (AI) has been evolving at a higher pace. It is poised to become one of the most impactful technologies humanity has ever created, boosting us and giving the ability to solve the problems once thought to be unsolvable! 

Intelligent AI systems are being built having innovative algorithms & models that predict and prescribe the outcomes accurately, which are powered by essential a) underlying HPC compute platform, b) computes ability to interface, and self-tune at device level, c) associate & bind necessary built-in multi matrix operations, and d) effectively tweaking the runtime environments on the fly, for the desired outcome. GPU’s and its closely coupled platform plays a key role in building the smartest AI systems and successful AI Startups!

Typical journey of an AI model to evolve from Base model to Personalized model for an AI Startup and GPU Usage –

Base Models

Industry Specific Model Customer Specific Model

Personalized Model

Objective of the models

Typically a generalized model for many industries Target Industry & similar dataset for the model to be built upon Specific is terrific! Target Customer & the dataset from Customer to acquent the model on customer specific data Loopback after inferencing for retraining the models to capture personalized preferences


Image Recognition; Sentiment Analysis; Speech to text etc. Speech to text model trained for Indian accent and its ability to decode Indian Automobile Sector terms Speech to text model trained on customer specific automobile components, policies & offers Retail Industry would expect the personalized preference bing generated while making purchasing decision and would be interested in capturing it for awesome customer experience


Provides Jumpstart to AI Development Target the specific Industry model where the problem lies Customer specific trained model with 95% accuracy Enhanced accuracy and hitting business objectives


Diverse publicly available dataset Maximum proportion of the Industry Specific Data in the base data to be trained upon e.g. Indian Automobile Sector in this case More than 65% of dataset from the customer specific data Data source could be from Social media, web history, click preferences, transactional data etc.

Expected Accuracy

70% to 80%

80% to 85% 85% to 95%

95% to 100%

Assumption on accuracy

Human accuracy levels are assumed to be between 90% to 95%

% of execution for final model


60% 80%


Usage of GPU

Occasional Continuous for Training Continuous for Training Continuous for Training & Inference
Where should the GPU be? GPU on Cloud GPU on Cloud GPU on Cloud

In-house or GPU on Cloud


While GPUs are equally important, its also essential to have the GPU platform associated with the underlying key components fulfilling all the dependencies in order to expedite AI Startup Journey towards success. Specifically if the AI Startups are empowered with the GPU’s and the platform with below features –

Key GPU & associated platform components for a successful AI startup

  • Instant access to the industry datasets from various repositories such as UCI, Kaggle & Research Institutes. 
  • Access to set of well-known ML algorithms to jumpstart the AI model development
  • Easy access to auto detect the relevant model applicable to the dataset of your interest
  • Platform to link the trained model to the end-customer through globally accepted protocols such as REST & RPC
  • Customized & AI based DevOps to manage the AI model lifecycle
  • And finally collaboration options to share the cost & simplified pricing through the AI model lifecycle

Concluding Remarks

Selecting appropriate mix of hardware platform, software mix, frameworks, access to the datasets, models, pipe-line, along with simplified pricing for base models, industry models, customer model, and end-user models is the key to being a successful AI startup.