Update README.md

README.md

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 # Model Overview
 
 ## Description:
 The NVIDIA Qwen3.8-Flash-Next-FP4-Local model is the quantized version of Alibaba's Qwen3.8-Flash-Next model, which is an auto-regressive language model that uses an optimized transformer architecture. Qwen3.8-Flash-Next is a causal language model with a vision encoder, hybrid attention (Gated DeltaNet and Qwen Sparse Attention), Mixture-of-Experts, gated residual streams, and n-gram embeddings. For more information, please check [here](https://huggingface.co/Qwen/Qwen3.8-Flash-Next). The NVIDIA Qwen3.8-Flash-Next-FP4-Local model is quantized with [Model Optimizer](https://github.com/NVIDIA/Model-Optimizer) using [quantization-aware distillation (QAD)](https://developer.nvidia.com/blog/how-quantization-aware-training-enables-low-precision-accuracy-recovery/).
 
 The checkpoint occupies approximately **96 GiB on disk** and is best suited for local AI hardware. It has been validated with vLLM on the following hardware: <br>
-* **NVIDIA RTX PRO 6000 Blackwell** — single GPU, with PLE (n-gram embedding table) offload. <br>
-* **NVIDIA DGX Spark** — with or without PLE offload. <br>
+* **NVIDIA RTX PRO 6000 Blackwell** — single GPU, with PLE (n-gram embedding table) offload. 850K tokens of FP8 KV Cache <br>
+* **NVIDIA DGX Spark** — Can run with or without PLE offload. 1M tokens of FP8 KV Cache with PLE offload <br>
 
 This model is ready for commercial or non-commercial use.  <br>