Update README.md

README.md

@@ -40,14 +40,14 @@ There are multiple modules we actually provide to boost the performances of your
 
 ✅ [OpenAlphaTensor](https://github.com/nebuly-ai/nebullvm/tree/main/apps/accelerate/open_alpha_tensor): Increase the computational performances of an AI model with custom-generated matrix multiplication algorithms.
 
+✅ [Nos](https://github.com/nebuly-ai/nos): Automatically maximize the utilization of GPU resources in a Kubernetes cluster through real-time dynamic partitioning and elastic quotas - Effortless optimization at its finest!
+
 ✅ [Forward-Forward](https://github.com/nebuly-ai/nebullvm/tree/main/apps/accelerate/forward_forward): The Forward Forward algorithm is a method for training deep neural networks that replaces the backpropagation forward and backward passes with two forward passes.
 
 ## Next modules and roadmap
 We are actively working on incorporating the following modules, as requested by members of our community, in upcoming releases:
 
-- [ ]  [Nos](https://github.com/nebuly-ai/nos): Automatically maximize the utilization of GPU resources in a Kubernetes cluster through real-time dynamic partitioning and elastic quotas - Effortless optimization at its finest!
 - [ ]  [Promptify](https://github.com/nebuly-ai/nebullvm/blob/main/apps/extract/promptify): Effortlessly personalize large APIs generative models from OpenAI, Cohere, HF to your specific context and requirements.
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 - [ ]  [CloudSurfer](https://github.com/nebuly-ai/nebullvm/blob/main/apps/accelerate/cloud_surfer): Automatically discover the optimal cloud configuration and hardware on AWS, GCP and Azure to run your AI models.
 - [ ]  [OptiMate](https://github.com/nebuly-ai/nebullvm/blob/main/apps/accelerate/optimate): Interactive tool guiding savvy users in achieving the best inference performance out of a given model / hardware setup.
 - [ ]  [TrainingSim](https://github.com/nebuly-ai/nebullvm/blob/main/apps/simulate/training_sim): Easily simulate the training of large AI models on a distributed infrastructure to predict training behaviours without actual implementation.