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README.md

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   <span>&nbsp;&nbsp;Build AI for your world</span>
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-Red Hat AI is built on open-source innovation, driven through close collaboration with IBM and Red Hat AI research, engineering, and business units.
+Red Hat AI is an open-source initiative backed by deep collaboration between IBM and Red Hat’s research, engineering, and business units. We’re committed to making AI more accessible, efficient, and community-driven from research to production.
 
-We strongly believe the future of AI is open and community-driven.
-As such, we are hosting our latest optimized models on Hugging Face, fully open for the world to use.
-We hope that the AI community will find our efforts useful and that our models help fuel their research and efficient AI deployments.
+We believe the future of AI is open. That’s why we’re sharing our latest models and research on Hugging Face, which are freely available to help researchers, developers, and organizations deploy high-performance AI at scale.
 
-With Red Hat AI you can, 
-- Leverage quantized variants of the leading open source models such as Llama, Mistral, Granite, DeepSeek, Qwen, Gemma, Phi, and many more. 
-- Tune smaller, purpose-built models with your own data.
-- Quantize your models with [LLM Compressor](https://github.com/vllm-project/llm-compressor) or use our pre-optimized models on HuggingFace.
-- Optimize inference with [vLLM](https://github.com/vllm-project/vllm) across any hardware and deployment scenarios.
+🔧 **With Red Hat AI, you can:**
+- **Use or build optimized foundation models**, including Llama, Mistral, Qwen, Gemma, DeepSeek, and others, tailored for performance and accuracy in real-world deployments.
+- **Customize and fine-tune models for your workflows**, from experimentation to production, with tools and frameworks built to support reproducible research and enterprise AI pipelines.
+- **Maximize inference efficiency across hardware** using production-grade compression and optimization techniques like quantization (FP8, INT8, INT4), structured/unstructured sparsity, distillation, and more, ready for cost-efficient deployments with vLLM.
 
-We provide accurate model checkpoints compressed with SOTA methods ready to run in vLLM such as W4A16, W8A16, W8A8 (int8 and fp8), and many more! 
-If you would like help quantizing a model or have a request for us to add a checkpoint, please open an issue in https://github.com/vllm-project/llm-compressor.
+🔗 **Explore our open-source tools**:
+- [**vLLM**](https://github.com/vllm-project/vllm) – Serve large language models efficiently across GPUs and environments.
+- [**LLM Compressor**](https://github.com/vllm-project/llm-compressor) – Compress and optimize your own models with SOTA quantization and sparsity techniques.
+  - TODO: add speculators shortly once the first release of that goes out and we start pushing models up.
+- [**InstructLab**](https://github.com/instructlab) – Fine-tune open models with your data using scalable, community-backed workflows.
+- [**GuideLLM**](https://github.com/neuralmagic/guidellm) – Benchmark, evaluate, and guide your deployments with structured performance and latency insights.
 
-Learn more at https://www.redhat.com/en/products/ai
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+Or learn more about our full product suite at https://www.redhat.com/en/products/ai