Update README.md

Author: dkundel-openai

README.md

@@ -15,7 +15,7 @@ Welcome to the gpt-oss series, [OpenAI's open-weight models](https://openai.com/
 
 We're releasing two flavors of these open models:
 
-- `gpt-oss-120b` — for production, general purpose, high reasoning use cases that fit into a single H100 GPU (117B parameters with 5.1B active parameters)
+- `gpt-oss-120b` — for production, general purpose, high reasoning use cases that fit into a single 80GB GPU (like NVIDIA H100 or AMD MI300X) (117B parameters with 5.1B active parameters)
 - `gpt-oss-20b` — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters)
 
 Both models were trained using our [harmony response format][harmony] and should only be used with this format; otherwise, they will not work correctly.
@@ -27,7 +27,7 @@ Both models were trained using our [harmony response format][harmony] and should
 - **Full chain-of-thought:** Provides complete access to the model's reasoning process, facilitating easier debugging and greater trust in outputs. This information is not intended to be shown to end users.
 - **Fine-tunable:** Fully customize models to your specific use case through parameter fine-tuning.
 - **Agentic capabilities:** Use the models' native capabilities for function calling, [web browsing](#browser), [Python code execution](#python), and Structured Outputs.
-- **Native MXFP4 quantization:** The models are trained with native MXFP4 precision for the MoE layer, allowing `gpt-oss-120b` to run on a single H100 GPU and `gpt-oss-20b` to run within 16GB of memory.
+- **Native MXFP4 quantization:** The models are trained with native MXFP4 precision for the MoE layer, allowing `gpt-oss-120b` to run on a single 80GB GPU (like NVIDIA H100 or AMD MI300X) and `gpt-oss-20b` to run within 16GB of memory.
 
 ### Inference examples