diff --git a/examples/kv_cache_memory_estimator.py b/examples/kv_cache_memory_estimator.py
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+++ b/examples/kv_cache_memory_estimator.py
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+"""
+Motivation:
+A frequent question from vLLM users is how to estimate the memory required for
+the attention key/value (KV) cache when scaling up context length, batch size,
+or model size. While the underlying formulas are simple, there was no clear,
+standalone example in the repository that demonstrates how to compute an
+approximate KV memory footprint directly from a model’s configuration.
+
+What this example provides:
+This script extracts the relevant architectural attributes (number of layers and
+hidden size) from a Hugging Face model configuration and applies a simple KV
+sizing rule to estimate memory usage for a given seq_len, batch_size, and dtype.
+The goal is to give users a back-of-the-envelope understanding of how KV cache
+memory scales — without requiring them to run inference or inspect GPU memory.
+
+Why this is helpful:
+- Helps plan for long-context inference workloads
+- Allows users to reason about memory tradeoffs before running vLLM
+- Clarifies how KV memory scales with model architecture
+- Useful for educational purposes when learning about LLM inference internals
+
+This estimator intentionally abstracts away fragmentation, paged layout
+overhead, and other runtime details. It is meant as a planning aid, not a
+precise profiler.
+"""
+
+import argparse
+from dataclasses import dataclass
+
+try:
+    from transformers import AutoConfig
+except ImportError as e:
+    raise SystemExit(
+        "This example requires `transformers`. Install it with:\n"
+        "  pip install transformers\n"
+    ) from e
+
+
+DTYPE_BYTES = {
+    "fp16": 2,
+    "bf16": 2,
+    "fp32": 4,
+    "int8": 1,
+}
+
+
+@dataclass
+class KVEstimate:
+    model_name: str
+    num_layers: int
+    hidden_size: int
+    seq_len: int
+    batch_size: int
+    dtype: str
+
+    def total_elements(self) -> int:
+        # KV per token per layer = 2 * hidden_size
+        return self.batch_size * self.seq_len * self.num_layers * (2 * self.hidden_size)
+
+    def total_bytes(self) -> int:
+        return self.total_elements() * DTYPE_BYTES[self.dtype]
+
+    def total_gb(self) -> float:
+        return self.total_bytes() / (1024 ** 3)
+
+    def pretty(self) -> str:
+        return (
+            f"Model:         {self.model_name}\n"
+            f"Layers:        {self.num_layers}\n"
+            f"Hidden size:   {self.hidden_size}\n"
+            f"Batch size:    {self.batch_size}\n"
+            f"Seq length:    {self.seq_len}\n"
+            f"Dtype:         {self.dtype}\n"
+            f"-------------------------------\n"
+            f"Approx KV cache memory: {self.total_gb():.2f} GB\n"
+        )
+
+
+def load_model_config(model_name: str):
+    cfg = AutoConfig.from_pretrained(model_name)
+
+    num_layers = getattr(cfg, "num_hidden_layers", getattr(cfg, "n_layer", None))
+    hidden_size = getattr(cfg, "hidden_size", getattr(cfg, "n_embd", None))
+
+    if num_layers is None or hidden_size is None:
+        raise ValueError(
+            f"Could not extract num_layers/hidden_size from config for {model_name}."
+        )
+
+    return num_layers, hidden_size
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Estimate KV cache memory usage.")
+    parser.add_argument("--model", type=str, required=True)
+    parser.add_argument("--seq-len", type=int, required=True)
+    parser.add_argument("--batch-size", type=int, default=1)
+    parser.add_argument("--dtype", type=str, default="fp16", choices=DTYPE_BYTES.keys())
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    num_layers, hidden_size = load_model_config(args.model)
+
+    est = KVEstimate(
+        model_name=args.model,
+        num_layers=num_layers,
+        hidden_size=hidden_size,
+        seq_len=args.seq_len,
+        batch_size=args.batch_size,
+        dtype=args.dtype,
+    )
+
+    print(est.pretty())
+
+
+if __name__ == "__main__":
+    main()