vLLM Benchmark suite improvement (#22119)

Signed-off-by: Tsai, Louie <louie.tsai@intel.com>
Signed-off-by: Louie Tsai <louie.tsai@intel.com>
Co-authored-by: Li, Jiang <bigpyj64@gmail.com>

.buildkite/nightly-benchmarks/README.md

@@ -7,7 +7,7 @@ This directory contains two sets of benchmark for vllm.
 - Performance benchmark: benchmark vllm's performance under various workload, for **developers** to gain clarity on whether their PR improves/degrades vllm's performance
 - Nightly benchmark: compare vllm's performance against alternatives (tgi, trt-llm and lmdeploy), for **the public** to know when to choose vllm.
 
-See [vLLM performance dashboard](https://perf.vllm.ai) for the latest performance benchmark results and [vLLM GitHub README](https://github.com/vllm-project/vllm/blob/main/README.md) for latest nightly benchmark results.
+See [vLLM performance dashboard](https://hud.pytorch.org/benchmark/llms?repoName=vllm-project%2Fvllm) for the latest performance benchmark results and [vLLM GitHub README](https://github.com/vllm-project/vllm/blob/main/README.md) for latest nightly benchmark results.
 
 ## Performance benchmark quick overview
 
@@ -138,28 +138,20 @@ The raw benchmarking results (in the format of json files) are in the `Artifacts
 
 The `compare-json-results.py` helps to compare benchmark results JSON files converted using `convert-results-json-to-markdown.py`.
 When run, benchmark script generates results under `benchmark/results` folder, along with the `benchmark_results.md` and `benchmark_results.json`.
-`compare-json-results.py` compares two `benchmark_results.json` files and provides performance ratio e.g. for Output Tput, Median TTFT and Median TPOT.
+`compare-json-results.py` compares two `benchmark_results.json` files and provides performance ratio e.g. for Output Tput, Median TTFT and Median TPOT.  
+If only one benchmark_results.json is passed, `compare-json-results.py` compares different TP and PP configurations in the benchmark_results.json instead.
 
-Here is an example using the script to compare result_a and result_b without detail test name.
+Here is an example using the script to compare result_a and result_b with Model, Dataset name, input/output lenght, max concurrency and qps.
-`python3 compare-json-results.py -f results_a/benchmark_results.json -f results_b/benchmark_results.json --ignore_test_name`
-
-|    | results_a/benchmark_results.json | results_b/benchmark_results.json | perf_ratio        |
-|----|----------------------------------------|----------------------------------------|----------|
-| 0  | 142.633982                             | 156.526018                             | 1.097396 |
-| 1  | 241.620334                             | 294.018783                             | 1.216863 |
-| 2  | 218.298905                             | 262.664916                             | 1.203235 |
-| 3  | 242.743860                             | 299.816190                             | 1.235113 |
-
-Here is an example using the script to compare result_a and result_b with detail test name.
 `python3 compare-json-results.py -f results_a/benchmark_results.json -f results_b/benchmark_results.json`
 
-|   | results_a/benchmark_results.json_name | results_a/benchmark_results.json | results_b/benchmark_results.json_name | results_b/benchmark_results.json | perf_ratio        |
+|   | Model | Dataset Name | Input Len | Output Len | # of max concurrency | qps  | results_a/benchmark_results.json | results_b/benchmark_results.json | perf_ratio        |
-|---|---------------------------------------------|----------------------------------------|---------------------------------------------|----------------------------------------|----------|
+|----|---------------------------------------|--------|-----|-----|------|-----|-----------|----------|----------|
-| 0 | serving_llama8B_tp1_sharegpt_qps_1          | 142.633982                             | serving_llama8B_tp1_sharegpt_qps_1          | 156.526018                             | 1.097396 |
+| 0  | meta-llama/Meta-Llama-3.1-8B-Instruct | random | 128 | 128 | 1000 | 1 | 142.633982                             | 156.526018                             | 1.097396 |
-| 1 | serving_llama8B_tp1_sharegpt_qps_16         | 241.620334                             | serving_llama8B_tp1_sharegpt_qps_16         | 294.018783                             | 1.216863 |
+| 1  | meta-llama/Meta-Llama-3.1-8B-Instruct | random | 128 | 128 | 1000 | inf| 241.620334                             | 294.018783                             | 1.216863 |
-| 2 | serving_llama8B_tp1_sharegpt_qps_4          | 218.298905                             | serving_llama8B_tp1_sharegpt_qps_4          | 262.664916                             | 1.203235 |
+
-| 3 | serving_llama8B_tp1_sharegpt_qps_inf        | 242.743860                             | serving_llama8B_tp1_sharegpt_qps_inf        | 299.816190                             | 1.235113 |
+A comparison diagram will be generated below the table.
-| 4 | serving_llama8B_tp2_random_1024_128_qps_1   | 96.613390                              | serving_llama8B_tp4_random_1024_128_qps_1   | 108.404853                             | 1.122048 |
+Here is an example to compare between 96c/results_gnr_96c_091_tp2pp3 and 128c/results_gnr_128c_091_tp2pp3
+<img width="1886" height="828" alt="image" src="https://github.com/user-attachments/assets/c02a43ef-25d0-4fd6-90e5-2169a28682dd" />
 
 ## Nightly test details
 

.buildkite/nightly-benchmarks/scripts/compare-json-results.py

@@ -1,24 +1,38 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import argparse
+import json
+import os
 
 import pandas as pd
 
 
 def compare_data_columns(
-    files, name_column, data_column, drop_column, ignore_test_name=False
+    files, name_column, data_column, info_cols, drop_column, debug=False
 ):
     print("\ncompare_data_column: " + data_column)
     frames = []
+    raw_data_cols = []
     compare_frames = []
     for file in files:
         data_df = pd.read_json(file)
         serving_df = data_df.dropna(subset=[drop_column], ignore_index=True)
-        if ignore_test_name is False:
+        # Show all info columns in the first couple columns
+        if not frames:
+            for col in info_cols:
+                if col not in serving_df.columns:
+                    print(f"Skipping missing column: {col}")
+                    continue
+                frames.append(serving_df[col])
+        # only show test name under debug mode
+        if debug is True:
             serving_df = serving_df.rename(columns={name_column: file + "_name"})
             frames.append(serving_df[file + "_name"])
+
+        file = "/".join(file.split("/")[:-1])
         serving_df = serving_df.rename(columns={data_column: file})
         frames.append(serving_df[file])
+        raw_data_cols.append(file)
         compare_frames.append(serving_df[file])
         if len(compare_frames) >= 2:
             # Compare numbers among two files
@@ -27,7 +41,68 @@ def compare_data_columns(
             compare_frames.pop(1)
 
     concat_df = pd.concat(frames, axis=1)
-    return concat_df
+    print(raw_data_cols)
+    return concat_df, raw_data_cols
+
+
+def split_json_by_tp_pp(
+    input_file: str = "benchmark_results.json", output_root: str = "."
+) -> list[str]:
+    """
+    Split a benchmark JSON into separate folders by (TP Size, PP Size).
+
+    Creates: <output_root>/tp{TP}_pp{PP}/benchmark_results.json
+    Returns: list of file paths written.
+    """
+    # Load JSON data into DataFrame
+    with open(input_file, encoding="utf-8") as f:
+        data = json.load(f)
+
+    # If the JSON is a dict with a list under common keys, use that list
+    if isinstance(data, dict):
+        for key in ("results", "serving_results", "benchmarks", "data"):
+            if isinstance(data.get(key), list):
+                data = data[key]
+                break
+
+    df = pd.DataFrame(data)
+
+    # Handle alias column names
+    rename_map = {
+        "tp_size": "TP Size",
+        "tensor_parallel_size": "TP Size",
+        "pp_size": "PP Size",
+        "pipeline_parallel_size": "PP Size",
+    }
+    df.rename(
+        columns={k: v for k, v in rename_map.items() if k in df.columns}, inplace=True
+    )
+
+    # Ensure TP/PP columns exist (default to 1 if missing)
+    if "TP Size" not in df.columns:
+        df["TP Size"] = 1
+    if "PP Size" not in df.columns:
+        df["PP Size"] = 1
+
+    # make sure TP/PP are numeric ints with no NaN
+    df["TP Size"] = (
+        pd.to_numeric(df.get("TP Size", 1), errors="coerce").fillna(1).astype(int)
+    )
+    df["PP Size"] = (
+        pd.to_numeric(df.get("PP Size", 1), errors="coerce").fillna(1).astype(int)
+    )
+
+    # Split into separate folders
+    saved_paths: list[str] = []
+    for (tp, pp), group_df in df.groupby(["TP Size", "PP Size"], dropna=False):
+        folder_name = os.path.join(output_root, f"tp{int(tp)}_pp{int(pp)}")
+        os.makedirs(folder_name, exist_ok=True)
+        filepath = os.path.join(folder_name, "benchmark_results.json")
+        group_df.to_json(filepath, orient="records", indent=2, force_ascii=False)
+        print(f"Saved: {filepath}")
+        saved_paths.append(filepath)
+
+    return saved_paths
 
 
 if __name__ == "__main__":
@@ -36,31 +111,105 @@ if __name__ == "__main__":
         "-f", "--file", action="append", type=str, help="input file name"
     )
     parser.add_argument(
-        "--ignore_test_name", action="store_true", help="ignore_test_name or not"
+        "--debug", action="store_true", help="show all information for debugging"
+    )
+    parser.add_argument(
+        "--plot",
+        action=argparse.BooleanOptionalAction,
+        default=True,
+        help="plot perf diagrams or not --no-plot --plot",
+    )
+    parser.add_argument(
+        "-x",
+        "--xaxis",
+        type=str,
+        default="# of max concurrency.",
+        help="column name to use as X Axis in comparision graph",
     )
     args = parser.parse_args()
-    files = args.file
-    print("comparing : " + ", ".join(files))
 
     drop_column = "P99"
     name_column = "Test name"
+    info_cols = [
+        "Model",
+        "Dataset Name",
+        "Input Len",
+        "Output Len",
+        "TP Size",
+        "PP Size",
+        "# of max concurrency.",
+        "qps",
+    ]
     data_cols_to_compare = ["Output Tput (tok/s)", "Median TTFT (ms)", "Median"]
     html_msgs_for_data_cols = [
         "Compare Output Tokens /n",
         "Median TTFT /n",
         "Median TPOT /n",
     ]
-    ignore_test_name = args.ignore_test_name
+
+    if len(args.file) == 1:
+        files = split_json_by_tp_pp(args.file[0], output_root="splits")
+        info_cols = [c for c in info_cols if c not in ("TP Size", "PP Size")]
+    else:
+        files = args.file
+    print("comparing : " + ", ".join(files))
+    debug = args.debug
+    plot = args.plot
+    # For Plot feature, assign y axis from one of info_cols
+    y_axis_index = info_cols.index(args.xaxis) if args.xaxis in info_cols else 6
     with open("perf_comparison.html", "w") as text_file:
         for i in range(len(data_cols_to_compare)):
-            output_df = compare_data_columns(
+            output_df, raw_data_cols = compare_data_columns(
                 files,
                 name_column,
                 data_cols_to_compare[i],
+                info_cols,
                 drop_column,
-                ignore_test_name=ignore_test_name,
+                debug=debug,
             )
-            print(output_df)
+
-            html = output_df.to_html()
+            # For Plot feature, insert y axis from one of info_cols
-            text_file.write(html_msgs_for_data_cols[i])
+            raw_data_cols.insert(0, info_cols[y_axis_index])
-            text_file.write(html)
+
+            filtered_info_cols = info_cols[:-2]
+            existing_group_cols = [
+                c for c in filtered_info_cols if c in output_df.columns
+            ]
+            if not existing_group_cols:
+                raise ValueError(
+                    f"No valid group-by columns  "
+                    f"Expected subset: {filtered_info_cols}, "
+                    f"but DataFrame has: {list(output_df.columns)}"
+                )
+
+            output_df_sorted = output_df.sort_values(by=existing_group_cols)
+            output_groups = output_df_sorted.groupby(existing_group_cols, dropna=False)
+            for name, group in output_groups:
+                html = group.to_html()
+                text_file.write(html_msgs_for_data_cols[i])
+                text_file.write(html)
+
+                if plot is True:
+                    import pandas as pd
+                    import plotly.express as px
+
+                    df = group[raw_data_cols]
+                    df_sorted = df.sort_values(by=info_cols[y_axis_index])
+                    # Melt DataFrame for plotting
+                    df_melted = df_sorted.melt(
+                        id_vars=info_cols[y_axis_index],
+                        var_name="Configuration",
+                        value_name=data_cols_to_compare[i],
+                    )
+                    title = data_cols_to_compare[i] + " vs " + info_cols[y_axis_index]
+                    # Create Plotly line chart
+                    fig = px.line(
+                        df_melted,
+                        x=info_cols[y_axis_index],
+                        y=data_cols_to_compare[i],
+                        color="Configuration",
+                        title=title,
+                        markers=True,
+                    )
+                    # Export to HTML
+                    text_file.write(fig.to_html(full_html=True, include_plotlyjs="cdn"))

.buildkite/nightly-benchmarks/scripts/convert-results-json-to-markdown.py

@@ -1,17 +1,19 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import argparse
 import json
 import os
+import re
+import shlex
 from importlib import util
 from pathlib import Path
+from typing import Any
 
 import pandas as pd
 import psutil
 from tabulate import tabulate
 
-results_folder = Path("results/")
-
 # latency results and the keys that will be printed into markdown
 latency_results = []
 latency_column_mapping = {
@@ -42,14 +44,22 @@ throughput_results_column_mapping = {
 serving_results = []
 serving_column_mapping = {
     "test_name": "Test name",
+    "model_id": "Model",
+    "dataset_name": "Dataset Name",
+    "input_len": "Input Len",
+    "output_len": "Output Len",
+    "tp_size": "TP Size",
+    "pp_size": "PP Size",
+    "dtype": "dtype",
     "gpu_type": "GPU",
     "completed": "# of req.",
+    "qps": "qps",
     "max_concurrency": "# of max concurrency.",
     "request_throughput": "Tput (req/s)",
     "total_token_throughput": "Total Token Tput (tok/s)",
     "output_throughput": "Output Tput (tok/s)",
-    "total_input_tokens": "Total input tokens",
+    # "total_input_tokens": "Total input tokens",
-    "total_output_tokens": "Total output tokens",
+    # "total_output_tokens": "Total output tokens",
     "mean_ttft_ms": "Mean TTFT (ms)",
     "median_ttft_ms": "Median TTFT (ms)",
     "p99_ttft_ms": "P99 TTFT (ms)",
@@ -94,7 +104,104 @@ def get_size_with_unit(bytes, suffix="B"):
         bytes /= factor
 
 
+def _coerce(val: str) -> Any:
+    """Best-effort type coercion from string to Python types."""
+    low = val.lower()
+    if low == "null":
+        return None
+    if low == "true":
+        return True
+    if low == "false":
+        return False
+    # integers
+    if re.fullmatch(r"[+-]?\d+", val):
+        try:
+            return int(val)
+        except ValueError:
+            pass
+    # floats (keep 'inf'/'-inf'/'nan' as strings)
+    if re.fullmatch(r"[+-]?\d*\.\d+", val):
+        try:
+            return float(val)
+        except ValueError:
+            pass
+    return val
+
+
+def parse_client_command(cmd: str) -> dict[str, Any]:
+    """Parse the client_command shell string into {executable, script, args}."""
+    toks = shlex.split(cmd)
+    if len(toks) < 2:
+        raise ValueError("client_command must include an executable and a script")
+    executable, script = toks[0], toks[1]
+    args: dict[str, Any] = {}
+
+    i = 2
+    while i < len(toks):
+        t = toks[i]
+        if t.startswith("--"):
+            # --key=value or --key (value) or boolean flag
+            if "=" in t:
+                key, val = t.split("=", 1)
+                if key == "--metadata":
+                    md = {}
+                    if val:
+                        if "=" in val:
+                            k, v = val.split("=", 1)
+                            md[k] = _coerce(v)
+                        else:
+                            md[val] = True
+                    args[key] = md
+                else:
+                    args[key] = _coerce(val)
+                i += 1
+                continue
+
+            key = t
+
+            # Special: consume metadata k=v pairs until next --flag
+            if key == "--metadata":
+                i += 1
+                md = {}
+                while i < len(toks) and not toks[i].startswith("--"):
+                    pair = toks[i]
+                    if "=" in pair:
+                        k, v = pair.split("=", 1)
+                        md[k] = _coerce(v)
+                    else:
+                        md[pair] = True
+                    i += 1
+                args[key] = md
+                continue
+
+            # Standard: check if next token is a value (not a flag)
+            if i + 1 < len(toks) and not toks[i + 1].startswith("--"):
+                args[key] = _coerce(toks[i + 1])
+                i += 2
+            else:
+                # lone flag -> True
+                args[key] = True
+                i += 1
+        else:
+            # unexpected positional; skip
+            i += 1
+
+    return {"executable": executable, "script": script, "args": args}
+
+
 if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-r",
+        "--result",
+        type=str,
+        default="results",
+        help="Folder name for benchmark output results.",
+    )
+    args = parser.parse_args()
+    results_folder = Path(args.result)
+    if not results_folder.exists():
+        raise FileNotFoundError(f"results folder does not exist: {results_folder}")
     # collect results
     for test_file in results_folder.glob("*.json"):
         with open(test_file) as f:
@@ -102,7 +209,6 @@ if __name__ == "__main__":
 
         if "serving" in str(test_file):
             # this result is generated via `vllm bench serve` command
-
             # attach the benchmarking command to raw_result
             try:
                 with open(test_file.with_suffix(".commands")) as f:
@@ -110,12 +216,44 @@ if __name__ == "__main__":
             except OSError as e:
                 print(e)
                 continue
+            # Parse Server Command Arg
+            out: dict[str, Any] = {
+                "server_command": parse_client_command(command["server_command"])
+            }
+            parse_args = [
+                "--tensor-parallel-size",
+                "--pipeline-parallel-size",
+                "--dtype",
+            ]
+            col_mapping = ["tp_size", "pp_size", "dtype"]
+            for index, arg in enumerate(parse_args):
+                if arg in out["server_command"]["args"]:
+                    raw_result.update(
+                        {col_mapping[index]: out["server_command"]["args"][arg]}
+                    )
 
+            # Parse Client Command Arg
+            out: dict[str, Any] = {
+                "client_command": parse_client_command(command["client_command"])
+            }
+            parse_args = [
+                "--dataset-name",
+                "--random-input-len",
+                "--random-output-len",
+                "--request-rate",
+            ]
+            col_mapping = ["dataset_name", "input_len", "output_len", "qps"]
+
+            for index, arg in enumerate(parse_args):
+                if arg in out["client_command"]["args"]:
+                    raw_result.update(
+                        {col_mapping[index]: out["client_command"]["args"][arg]}
+                    )
+            # Add Server, Client command
             raw_result.update(command)
 
             # update the test name of this result
             raw_result.update({"test_name": test_file.stem})
-
             # add the result to raw_result
             serving_results.append(raw_result)
             continue
@@ -205,7 +343,10 @@ if __name__ == "__main__":
             columns=latency_column_mapping
         )
     if not serving_results.empty:
-        serving_results = serving_results[list(serving_column_mapping.keys())].rename(
+        valid_columns = [
+            col for col in serving_column_mapping if col in serving_results.columns
+        ]
+        serving_results = serving_results[valid_columns].rename(
             columns=serving_column_mapping
         )
     if not throughput_results.empty:
@@ -245,7 +386,9 @@ if __name__ == "__main__":
     )
 
     # document the result
-    with open(results_folder / "benchmark_results.md", "w") as f:
+    md_file = "benchmark_results.md"
+    json_file = "benchmark_results.json"
+    with open(results_folder / md_file, "w") as f:
         results = read_markdown(
             "../.buildkite/nightly-benchmarks/"
             + "performance-benchmarks-descriptions.md"
@@ -260,7 +403,7 @@ if __name__ == "__main__":
         f.write(results)
 
     # document benchmarking results in json
-    with open(results_folder / "benchmark_results.json", "w") as f:
+    with open(results_folder / json_file, "w") as f:
         results = (
             latency_results.to_dict(orient="records")
             + throughput_results.to_dict(orient="records")

.buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh

@@ -194,9 +194,11 @@ run_latency_tests() {
 
     # check if there is enough GPU to run the test
     tp=$(echo "$latency_params" | jq -r '.tensor_parallel_size')
-    if [ "$ON_CPU" == "1" ];then
+    if [ "$ON_CPU" == "1" ]; then
-      if [[ $numa_count -lt $tp ]]; then
+      pp=$(echo "$latency_params" | jq -r '.pipeline_parallel_size')
-        echo "Required tensor-parallel-size $tp but only $numa_count NUMA nodes found. Skip testcase $test_name."
+      world_size=$(($tp*$pp))
+      if [[ $numa_count -lt $world_size  && -z "${REMOTE_HOST}" ]]; then
+        echo "Required world-size $world_size but only $numa_count NUMA nodes found. Skip testcase $test_name."
         continue
       fi
     else
@@ -261,9 +263,11 @@ run_throughput_tests() {
 
     # check if there is enough GPU to run the test
     tp=$(echo "$throughput_params" | jq -r '.tensor_parallel_size')
-    if [ "$ON_CPU" == "1" ];then
+    if [ "$ON_CPU" == "1" ]; then
-      if [[ $numa_count -lt $tp ]]; then
+      pp=$(echo "$throughput_params" | jq -r '.pipeline_parallel_size')
-        echo "Required tensor-parallel-size $tp but only $numa_count NUMA nodes found. Skip testcase $test_name."
+      world_size=$(($tp*$pp))
+      if [[ $numa_count -lt $world_size  && -z "${REMOTE_HOST}" ]]; then
+        echo "Required world-size $world_size but only $numa_count NUMA nodes found. Skip testcase $test_name."
         continue
       fi
     else
@@ -329,12 +333,21 @@ run_serving_tests() {
     qps_list=$(echo "$params" | jq -r '.qps_list')
     qps_list=$(echo "$qps_list" | jq -r '.[] | @sh')
     echo "Running over qps list $qps_list"
+    max_concurrency_list=$(echo "$params" | jq -r '.max_concurrency_list')
+    if [[ -z "$max_concurrency_list" || "$max_concurrency_list" == "null" ]]; then
+        num_prompts=$(echo "$client_params" | jq -r '.num_prompts')
+        max_concurrency_list="[$num_prompts]"
+    fi
+    max_concurrency_list=$(echo "$max_concurrency_list" | jq -r '.[] | @sh')
+    echo "Running over max concurrency list $max_concurrency_list"
 
     # check if there is enough resources to run the test
     tp=$(echo "$server_params" | jq -r '.tensor_parallel_size')
-    if [ "$ON_CPU" == "1" ];then
+    if [ "$ON_CPU" == "1" ]; then
-      if [[ $numa_count -lt $tp ]]; then
+      pp=$(echo "$server_params" | jq -r '.pipeline_parallel_size')
-        echo "Required tensor-parallel-size $tp but only $numa_count NUMA nodes found. Skip testcase $test_name."
+      world_size=$(($tp*$pp))
+      if [[ $numa_count -lt $world_size  && -z "${REMOTE_HOST}" ]]; then
+        echo "Required world-size $world_size but only $numa_count NUMA nodes found. Skip testcase $test_name."
         continue
       fi
     else
@@ -390,35 +403,39 @@ run_serving_tests() {
         echo "now qps is $qps"
       fi
 
-      new_test_name=$test_name"_qps_"$qps
+      # iterate over different max_concurrency
+      for max_concurrency in $max_concurrency_list; do
+        new_test_name=$test_name"_qps_"$qps"_concurrency_"$max_concurrency
+        echo " new test name $new_test_name"
+        # pass the tensor parallel size to the client so that it can be displayed
+        # on the benchmark dashboard
+        client_command="vllm bench serve \
+          --save-result \
+          --result-dir $RESULTS_FOLDER \
+          --result-filename ${new_test_name}.json \
+          --request-rate $qps \
+          --max-concurrency $max_concurrency \
+          --metadata "tensor_parallel_size=$tp" \
+          $client_args $client_remote_args "
 
-      # pass the tensor parallel size to the client so that it can be displayed
+        echo "Running test case $test_name with qps $qps"
-      # on the benchmark dashboard
+        echo "Client command: $client_command"
-      client_command="vllm bench serve \
-        --save-result \
-        --result-dir $RESULTS_FOLDER \
-        --result-filename ${new_test_name}.json \
-        --request-rate $qps \
-        --metadata "tensor_parallel_size=$tp" \
-        $client_args $client_remote_args "
 
-      echo "Running test case $test_name with qps $qps"
+        bash -c "$client_command"
-      echo "Client command: $client_command"
 
-      bash -c "$client_command"
+        # record the benchmarking commands
-
+        jq_output=$(jq -n \
-      # record the benchmarking commands
+          --arg server "$server_command" \
-      jq_output=$(jq -n \
+          --arg client "$client_command" \
-        --arg server "$server_command" \
+          --arg gpu "$gpu_type" \
-        --arg client "$client_command" \
+          '{
-        --arg gpu "$gpu_type" \
+            server_command: $server,
-        '{
+            client_command: $client,
-          server_command: $server,
+            gpu_type: $gpu
-          client_command: $client,
+          }')
-          gpu_type: $gpu
+        echo "$jq_output" >"$RESULTS_FOLDER/${new_test_name}.commands"
-        }')
-      echo "$jq_output" >"$RESULTS_FOLDER/${new_test_name}.commands"
 
+      done
     done
 
     # clean up

.buildkite/nightly-benchmarks/tests/latency-tests-cpu.json

@@ -6,7 +6,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 1,
             "load_format": "dummy",
             "num_iters_warmup": 5,
@@ -20,7 +20,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 4,
             "load_format": "dummy",
             "num_iters_warmup": 5,

.buildkite/nightly-benchmarks/tests/serving-tests-cpu-snc2.json

@@ -1,7 +1,8 @@
 [
     {
         "test_name": "serving_llama8B_tp1_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -10,7 +11,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 1,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -23,17 +24,17 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
             "num_prompts": 200
         }
     },
     {
         "test_name": "serving_llama8B_tp2_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -42,7 +43,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 2,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -55,17 +56,17 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
             "num_prompts": 200
         }
     },
     {
         "test_name": "serving_llama8B_tp4_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -74,7 +75,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 4,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -87,17 +88,17 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
             "num_prompts": 200
         }
     },
     {
         "test_name": "serving_llama8B_tp1_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -106,7 +107,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 1,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -120,19 +121,19 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "random",
 	    "random-input-len": 128,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-	    "max_concurrency": 1000,
             "num_prompts": 1000
         }
     },
     {
         "test_name": "serving_llama8B_tp2_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -141,7 +142,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 2,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -155,19 +156,19 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "random",
 	    "random-input-len": 128,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-	    "max_concurrency": 1000,
             "num_prompts": 1000
         }
     },
     {
         "test_name": "serving_llama8B_tp4_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -176,7 +177,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 4,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -190,13 +191,11 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "random",
 	    "random-input-len": 128,
 	    "random-output-len": 128,
-	    "ignore-eos": "",
-	    "max_concurrency": 1000,
             "num_prompts": 1000
         }
     }

.buildkite/nightly-benchmarks/tests/serving-tests-cpu-snc3.json

@@ -1,7 +1,8 @@
 [
     {
         "test_name": "serving_llama8B_pp1_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -10,7 +11,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "pipeline_parallel_size": 1,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -23,17 +24,17 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
             "num_prompts": 200
         }
     },
     {
         "test_name": "serving_llama8B_pp3_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -42,7 +43,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "pipeline_parallel_size": 3,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -55,17 +56,17 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
             "num_prompts": 200
         }
     },
     {
-        "test_name": "serving_llama8B_tp2pp6_sharegpt",
+        "test_name": "serving_llama8B_tp2pp3_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -74,7 +75,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 2,
             "pipeline_parallel_size": 3,
 	    "dtype": "bfloat16",
@@ -88,17 +89,17 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
             "num_prompts": 200
         }
     },
     {
         "test_name": "serving_llama8B_pp1_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -107,7 +108,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "pipeline_parallel_size": 1,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -121,28 +122,28 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "random",
 	    "random-input-len": 128,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-	    "max_concurrency": 1000,
             "num_prompts": 1000
         }
     },
     {
         "test_name": "serving_llama8B_pp3_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
 	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL:": 1,
+	    "VLLM_CPU_SGL_KERNEL": 1,
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "pipeline_parallel_size": 3,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -156,19 +157,19 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "random",
 	    "random-input-len": 128,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-	    "max_concurrency": 1000,
             "num_prompts": 1000
         }
     },
     {
         "test_name": "serving_llama8B_tp2pp3_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
+        "qps_list": ["inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -177,7 +178,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 2,
             "pipeline_parallel_size": 3,
 	    "dtype": "bfloat16",
@@ -192,13 +193,12 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "random",
 	    "random-input-len": 128,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-	    "max_concurrency": 1000,
             "num_prompts": 1000
         }
     }

.buildkite/nightly-benchmarks/tests/serving-tests-cpu.json

@@ -2,6 +2,7 @@
     {
         "test_name": "serving_llama8B_tp1_sharegpt",
         "qps_list": [1, 4, 16, "inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -10,7 +11,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 1,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -23,17 +24,17 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
             "num_prompts": 200
         }
     },
     {
         "test_name": "serving_llama8B_tp2_sharegpt",
         "qps_list": [1, 4, 16, "inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -42,7 +43,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 2,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -55,17 +56,17 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
             "num_prompts": 200
         }
     },
     {
         "test_name": "serving_llama8B_tp4_sharegpt",
         "qps_list": [1, 4, 16, "inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -74,7 +75,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 4,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -87,17 +88,17 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
             "num_prompts": 200
         }
     },
     {
         "test_name": "serving_llama8B_tp4_random_1024_128",
         "qps_list": [1, 4, 16, "inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -106,7 +107,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 4,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -120,19 +121,19 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "random",
 	    "random-input-len": 1024,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-	    "max_concurrency": 100,
             "num_prompts": 100
         }
     },
     {
         "test_name": "serving_llama8B_pp6_random_1024_128",
         "qps_list": [1, 4, 16, "inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -141,7 +142,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "pipeline_parallel_size": 6,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
@@ -155,13 +156,12 @@
             "load_format": "dummy"
         },
         "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "random",
 	    "random-input-len": 1024,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-	    "max_concurrency": 100,
             "num_prompts": 100
         }
     }

.buildkite/nightly-benchmarks/tests/throughput-tests-cpu.json

@@ -6,7 +6,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 1,
             "load_format": "dummy",
             "dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
@@ -21,7 +21,7 @@
 	    "VLLM_CPU_KVCACHE_SPACE": 40
         },
         "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 4,
             "load_format": "dummy",
             "dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",

docs/contributing/benchmarks.md

@@ -11,7 +11,7 @@ vLLM contains two sets of benchmarks:
 
 The performance benchmarks are used for development to confirm whether new changes improve performance under various workloads. They are triggered on every commit with both the `perf-benchmarks` and `ready` labels, and when a PR is merged into vLLM.
 
-The latest performance results are hosted on the public [vLLM Performance Dashboard](https://perf.vllm.ai).
+The latest performance results are hosted on the public [vLLM Performance Dashboard](https://hud.pytorch.org/benchmark/llms?repoName=vllm-project%2Fvllm).
 
 More information on the performance benchmarks and their parameters can be found [here](gh-file:.buildkite/nightly-benchmarks/performance-benchmarks-descriptions.md).