Key Features of Planck AI Cloud

Overview

A Platform-as-a-Service, Planck AI Cloud offers its clients AI model deployment, inferencing, fine-tuning, and training services. To help any business implement custom models, we at Planck also offer advisory services for creating a custom MLOps pipeline.

We offer AI-based companies an alternative to public cloud, which protects your data, lowers your monthly costs and provides simple, predictable billing. Public cloud doesn't make sense for AI (+data) workloads.

Customize, test, and deploy all major open-source models from Google, Mistral, Meta, and more. Use our full-stack platform to build, test, and deploy enterprise-ready AI apps, customized with your own data, with any model, on our cloud. Compared to AWS or Azure, the costs associated are 60% lower on Planck, with pay-per-usage and no up-front fees.


Features

API Calls:

Build AI apps with foundational models like Llama-3 and other major open-source models.

  • Access a vast library of pre-trained AI models, covering a wide range of tasks such as natural language processing, image recognition, and more.

  • Easily integrate these models into your applications through simple API calls, without the need for deep machine learning expertise.

  • Example: A developer building a chatbot can use the Llama-3 API to provide the chatbot with advanced language understanding and generation capabilities.

  • Use case: A content creation platform can leverage the API to generate personalized product descriptions or blog post summaries based on user preferences.

AI Inference:

Deploy trained AI models to make predictions and inferences in real-time applications.

  • Once you've trained or fine-tuned a model, deploy it to our cloud platform for efficient inference.

  • Receive real-time predictions and insights from your models, enabling you to build responsive and intelligent applications.

  • Example: A fraud detection system can use a deployed AI model to analyze transaction data and identify suspicious activity in real time.

  • Use case: A customer support chatbot can leverage a deployed model to understand customer inquiries and provide accurate and timely responses.

AI Training:

Train custom AI models from scratch using large datasets and our powerful infrastructure.

  • Build highly tailored AI models that meet your specific needs and requirements.

  • Utilize our scalable cloud infrastructure to train models on massive datasets, accelerating the training process.

  • Example: A medical researcher can train a custom AI model to analyze medical images and diagnose diseases with high accuracy.

  • Use case: An e-commerce company can train a model to predict customer preferences and recommend relevant products.

AI Fine-Tuning:

Adapt pre-trained models to specific tasks and domains for improved performance.

  • Start with a pre-trained model as a foundation and fine-tune it on your own data to specialize it for your use case.

  • This process allows you to achieve better results with less training data and time.

  • Example: A language translation service can fine-tune a pre-trained language model on a large dataset of parallel texts to improve the accuracy of translations.

  • Use case: A social media platform can fine-tune a sentiment analysis model on its user-generated content to better understand user opinions and engagement.

AI Model Hosting:

Deploy and manage AI models on our scalable cloud platform for easy access and use.

  • Easily deploy your trained or fine-tuned models to our cloud platform for seamless integration into your applications.

  • Benefit from our scalable infrastructure to handle varying inference loads and ensure high availability.

  • Example: A mobile app developer can deploy an AI model to the cloud to enable real-time image recognition features on the app.

  • Use case: A financial institution can host a risk assessment model on the cloud to provide automated credit scoring for loan applications.


GPU Models Available - 2024

500 H100 Bare Metal Servers with each 8 GPUs

GPU:

• NVIDIA HGX H100 with H100 SXM5 GPUs *8, 80GB * 8, 640GB HBM3 Memory

CPU:

• 96 cores, 192 threads, Intel(R) Xeon(R) Platinum 8468 *2

Memory:

• 2048GB (64GB * 32)

Local Storage:

• 40TB NVMe SSD 2.5inch

Network:

• Mellanox Network Adapter MT2910 Family ConnectX-7, 400Gbps *8

GPU Interconnect:

• Supports NVLINK & RoCE with 23.2Tbs bandwidth

Private Network:

• 10Gbps

Public Network:

• Guaranteed bandwidth of 100 Mbps

More GPU & CPU models coming in Q1 2025*

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