Update README.md

README.md

@@ -48,7 +48,7 @@ The end result is a model that is especially capable at reasoning and agentic ta
 US open-weight models with more than 10x the number of total parameters.
 At only 65B total parameters, the model can fit on consumer hardware such as the NVIDIA DGX Spark. 
 To encourage local model development, we provide an NVFP4 checkpoint and colab to help you get started. 
-The model is also fast at token generation when running on datacenter GPUs, reaching 250 tok/s on 4xB200 batch=8 prior to quantization or custom kernels. 
+The model is also fast at token generation when running on datacenter GPUs, reaching 250 tok/s on 2xB200 batch=8 prior to quantization or custom kernels. 
 
 **Pre-training.** Ripple-65B Preview is pretrained from scratch on our 12T text-only predominantly-English and code corpus at 8K sequence length. 
 Ripple-65B Base Preview is a **pretrain-only checkpoint** without any midtraining or synthetic QA data.