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 ## 🔰 Introduction
 
 Sharing high-end GPUs or even prosumer & consumer GPUs between multiple users is the most cost-effective 
-way to accelerate AI development. Unfortunately until now the 
-only solution existed applied for MIG/Slicing high-end GPUs (A100+) and required Kubernetes, <br>
+way to accelerate AI development. Unfortunately, until now the 
+only existing solution applied for MIG/Slicing high-end GPUs (A100+) and required Kubernetes, <br>
+
 🔥 🎉 Welcome To Container Based Fractional GPU For Any Nvidia Card! 🎉 🔥 <br>
+
 We present pre-packaged containers supporting CUDA 11.x & CUDA 12.x with pre-built hard memory limitation!
-This means multiple containers can be launched on the same GPU ensuring one user cannot allocate the entire host GPU memory!
-(no more greedy processes grabbing the entire GPU memory! finally we have a driver level hard limiting memory option)
+This means multiple containers can be launched on the same GPU, ensuring one user cannot allocate the entire host GPU memory!
+(No more greedy processes grabbing the entire GPU memory! Finally we have a driver level hard limiting memory option).
+
+## 🚀 Offerings 
+
+ClearML offers several options to optimize GPU resource utilization by partitioning GPUs:
+* **Container-based Memory Limits** (**this repository**): Use pre-packaged containers with built-in memory limits to 
+run multiple containers on the same GPU (available as part of the ClearML open source offering).
+* **Kubernetes-based Static MIG Slicing**: Set up Kubernetes support for NVIDIA MIG (Multi-Instance GPU) to define GPU 
+fractions for specific workloads (available as part of the ClearML open source offering).
+* **Dynamic GPU Slicing**: On-demand GPU slicing per task for both MIG and non-MIG devices, supporting both bare metal 
+and Kubernetes deployments (available under the ClearML Enterprise plan).
+
+With these options, ClearML enables running AI workloads with optimized hardware utilization and workload performance. 
+This repository covers container-based fractional GPUs. For more information on ClearML's fractional GPU offerings, see 
+the [ClearML documentation](https://clear.ml/docs/latest/docs/clearml_agent/clearml_agent_fractional_gpus). 
+
 
 ## ⚡ Installation