add benchmark description

app.py

@@ -16,11 +16,11 @@ OLD_COLUMNS = ["model", "backend.name", "backend.torch_dtype", "backend.quantiza
                "generate.latency(s)", "generate.throughput(tokens/s)"]
 
 NEW_COLUMNS = ["Model", "Backend 🏭", "Load Datatype", "Quantization 🗜️",
-               "Latency_100 (s) ⬇️", "Throughput_100 (tokens/s) ⬆️"]
+               "Latency (s) ⬇️", "Throughput (tokens/s) ⬆️"]
 
 COLUMNS_DATATYPES = ["markdown", "str", "str", "str", "number", "number"]
 
-SORTING_COLUMN = ["Throughput_100 (tokens/s) ⬆️"]
+SORTING_COLUMN = ["Throughput (tokens/s) ⬆️"]
 
 
 llm_perf_dataset_repo = load_dataset_repo(LLM_PERF_DATASET_REPO, OPTIMUM_TOKEN)
@@ -58,9 +58,12 @@ with demo:
     gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
 
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
-        with gr.TabItem("Vanilla Benchmark", elem_id="vanilla-benchmark", id=0):
-
+        with gr.TabItem("A100 Benchmark", elem_id="vanilla-benchmark", id=0):
             vanilla_benchmark_df = get_vanilla_benchmark_df()
+
+            A100_text = "Machine: 4x A100 80GB<br>Batches: 1<br>Number of tokens: 100<br>"
+            gr.HTML(A100_text)
+
             leaderboard_table_lite = gr.components.Dataframe(
                 value=vanilla_benchmark_df,
                 datatype=COLUMNS_DATATYPES,