chore(card): add hardware compatibility section

README.md

@@ -4,17 +4,14 @@ license_name: minimax-model-license
 license_link: https://huggingface.co/MiniMaxAI/MiniMax-M2.7/blob/main/LICENSE
 base_model: MiniMaxAI/MiniMax-M2.7
 tags:
-- rotorquant
-- kv-cache-quantization
-- minimax
-- m2.7
-- moe
-- quantized
+  - rotorquant
+  - kv-cache-quantization
+  - minimax
+  - m2.7
+  - moe
+  - quantized
 library_name: transformers
 pipeline_tag: text-generation
-language:
-- en
-inference: false
 ---
 
 # MiniMax-M2.7-RotorQuant
@@ -23,6 +20,12 @@ inference: false
 
 This is a **documentation repository** that explains how to combine MiniMax-M2.7's weights with RotorQuant inference-time KV cache compression. No weights are stored here — use the base model directly and apply RotorQuant via the Python package or llama.cpp fork.
 
+## Hardware compatibility
+
+| Device | VRAM / RAM | Recommendation |
+| --- | --- | --- |
+| Any host that runs the base model | baseline + runtime savings | RotorQuant/TurboQuant is a KV-cache runtime modifier; pair with any weight variant |
+
 ## What is this?
 
 KV cache compression reduces the memory used by the attention cache during inference. Unlike weight quantization (which is baked into the GGUF/MLX file), KV cache compression is applied at runtime — so the same base weights can be used with or without compression.