The Ground Reality the Real Problems AI Teams Face.
Talk to the founders of AI, ML engineers or CTOs in India and you will have the same story.
The ideas are solid.
The talent is strong.
However, the roadblock is the infrastructure.
The teams always have problems with:
Training of AI models that require days, but not hours.
CPU based servers fail during deep learning loads.
Expensive and risky price of purchasing GPUs.
Explosive user growth degrading the inference.
Bills on the cloud that are incomprehensible and difficult to forecast.
A large number of start-ups start with a simple cloud server with the belief that it will grow with them. However, that setup does not last long as the models get larger and the number of users rises. This comes to be realized after the fact and migrating infrastructure is already painful and costly.
Infrastructure ought to facilitate innovation, and not to retard it.
Possible Solutions Teams tend to make attempts.
- Buying Physical GPU Servers
Other teams opt to get GPUs in-house.
What they are going through in reality:
Intensive initial capital expenditure.
Prolonged purchase and installation process.
When there are low workloads, GPUs are idle.
Very poor scaling capabilities.
It is only applicable to big businesses that have a known amount of work to get done- not start ups in AI.
- Large Global Cloud Providers.
Key cloud offerings are high-intensity GUI instances and enterprise-level infrastructure.
They can, and even most teams believe:
It is challenging to foretell and comprehend pricing.
The creation and administration are too complicated.
They are paying of services they do not even need.
Indian users can have a problem with latency.
In the case of smaller teams, administration of such platforms can be a full-time job, which takes attention to the development of AI products.
- Migration to GPU-Based Cloud Solutions.
Other teams shift to GPU-first cloud providers to resolve the problem of availability.
Although the accessibility of GPUs improves, there are still problems:
Platforms are usually extremely technical.
Not very beginner-friendly
Not always geared towards India-based teams.
The actual issue is not merely the access to GPUs, but its simplicity, clear explanation, and usability.
What actually works: smarter cloud server Approach.
AI teams don’t need more tools.
They require the appropriate cloud server.
Training vs Inference – An Easy To Understand Introduction.
The training of AI models implies that the model is instructed on large datasets. It is graphics-intensive and time-consuming.
AI inference implies applying the result of such trained model to provide results in real time. It is time sensitive and requires stability.
A current cloud GPU platform should be capable of supporting each and every one of these, without necessarily compelling teams to restructure infrastructure with each change in workloads.
Server Virtualization is important because it saves money and enables faster and efficient performance.The reason why Server Virtualization is important is that it will save money and allow faster and efficient performance.
Under cloud computing with server virtualization, AI teams enjoy:
Isolated GPU environments
Faster setup and deployment
Easy scaling up or down
Liberty of hardware restriction.
This is highly essential when models are changing regularly and experimentation is also always present.
What The Best Cloud Server to Use with AI Is.
A typical cloud server that is optimal to use with AI workloads is that which provides:
On-demand GPU access
Storage and networking High-performance storage and networking.
Foreseeable, open pricing.
Easy implementation and administration.
Dependable assistance in training and inference.
Most importantly, it must not be difficult to use- another system to maintain.
The Solution to These Problems of Our Product.
The right platform does not have to adjust AI teams to the infrastructure, but the opposite.
The solution to a successfully designed Nvidia GPU enables teams to:
Train AI models faster
Scale-inferencing with high reliability.
Test without worrying about out of control expenses.
Workload scales automatically with increase in demand.
Waste less time with models–not servers.
This is what the contemporary AI infrastructure must be like: straightforward, versatile, and reliable.
Where inhosted.ai Fits In
These are real-world challenges that inhosted.ai is based upon.
It focuses on:
Cloud server infrastructure based on GPUs.
Streamlined AI and ML team management.
No surprises with transparency of pricing.
Indian workload-optimized performance.
This will fit instinctively to startups and businesses that are willing to get the best cloud server hosting experience without the complexity of an enterprise.
Why This is Particularly a Concern in India.
With regard to Indian AI companies, a cloud server India installation has definite benefits:
Reduced latency in the local users.
Infrastructure costs are better controlled.
Localized service and response.
Faster time to market of AI products.
With the increased use of AI, infrastructure decisions will determine a person who scales to higher levels with ease and those who do not.
Cloud Server with GPU (People Also Ask).
What is a cloud server having GPU?
A cloud server containing the graphics processors is a virtual server incorporating GPUs to speed up AI, machine learning, and data intensive tasks.
- Why does it require cloud GPU in AI models?
A cloud GPU calculates data simultaneously, and thus, AI training and inference are much faster than servers with a CPU.
- Is it possible to use only one cloud server to do training and inference?
Yes. Cloud computing allows the efficient use of a single environment to support both with appropriate configuration and server virtualization.
- Is cloud GPU superior to physical purchase of GPUs?
For most teams, yes. The cloud GPUs eliminate the initial hardware expenses and provide elastic and pay-as-you-scale options.
- What is the advantage of putting AI workloads in India on a cloud server?
A cloud server solution based in India has lower latency, easier billing and it is more compatible with local business and compliance requirements.
