TensorRT-LLM Benchmarking

Important

This benchmarking suite is a work in progress. Expect breaking API changes.

TensorRT-LLM provides the trtllm-bench CLI, a packaged benchmarking utility that aims to make it easier for users to reproduce our officially published performance overiew. trtllm-bench provides the follows:

• A streamlined way to build tuned engines for benchmarking for a variety of models and platforms.

• An entirely Python workflow for benchmarking.

• Ability to benchmark various flows and features within TensorRT-LLM.

trtllm-bench executes all benchmarks using [in-flight batching] – for more information see the this section that describes the concept in further detail.

Throughput Benchmarking

Limitations and Caveats

Validated Networks for Benchmarking

While trtllm-bench should be able to run any network that TensorRT-LLM supports, the following are the list that have been validated extensively and is the same listing as seen on the Performance Overview page.

• meta-llama/Llama-2-7b-hf

• meta-llama/Llama-2-70b-hf

• tiiuae/falcon-180B

• EleutherAI/gpt-j-6b

• meta-llama/Meta-Llama-3-8B

• meta-llama/Llama-3.1-8B

• meta-llama/Meta-Llama-3-70B

• meta-llama/Llama-3.1-70B

• meta-llama/Llama-3.1-405B

• mistralai/Mixtral-8x7B-v0.1

• mistralai/Mistral-7B-v0.1

• meta-llama/Llama-3.1-8B-Instruct

• meta-llama/Llama-3.1-70B-Instruct

• meta-llama/Llama-3.1-405B-Instruct

• mistralai/Mixtral-8x7B-v0.1-Instruct

Tip

trtllm-bench can automatically download the model from Hugging Face Model Hub. Export your token in the HF_TOKEN environment variable.

Supported Quantization Modes

trtllm-bench supports the following quantization modes:

• None (no quantization applied)

• FP8

• NVFP4

For more information about quantization, refer to Numerical Precision and the support matrix of the supported quantization methods for each network.

Tip

Although TensorRT-LLM supports more quantization modes than listed above, trtllm-bench currently only configures for a smaller subset.

Quickstart

This quick start focuses on running a short max throughput benchmark on meta-llama/Llama-3.1-8B on a synthetic dataset with a uniform distribution of prompts with ISL:OSL of 128:128. To run the benchmark from start to finish, run the following commands:

python benchmarks/cpp/prepare_dataset.py --stdout --tokenizer meta-llama/Llama-3.1-8B token-norm-dist --input-mean 128 --output-mean 128 --input-stdev 0 --output-stdev 0 --num-requests 3000 > /tmp/synthetic_128_128.txt
trtllm-bench --model meta-llama/Llama-3.1-8B build --dataset /tmp/synthetic_128_128.txt --quantization FP8
trtllm-bench --model meta-llama/Llama-3.1-8B throughput --dataset /tmp/synthetic_128_128.txt --engine_dir /tmp/meta-llama/Llama-3.1-8B/tp_1_pp_1

After the benchmark completes, trtllm-bench prints a summary with summary metrics.

===========================================================
= ENGINE DETAILS
===========================================================
Model:                  meta-llama/Llama-3.1-8B
Engine Directory:       /tmp/meta-llama/Llama-3.1-8B/tp_1_pp_1
TensorRT-LLM Version:   0.17.0
Dtype:                  bfloat16
KV Cache Dtype:         FP8
Quantization:           FP8
Max Input Length:       256
Max Sequence Length:    256

===========================================================
= WORLD + RUNTIME INFORMATION
===========================================================
TP Size:                1
PP Size:                1
Max Runtime Batch Size: 4096
Max Runtime Tokens:     8192
Scheduling Policy:      Guaranteed No Evict
KV Memory Percentage:   90.00%
Issue Rate (req/sec):   5.0689E+14

===========================================================
= PERFORMANCE OVERVIEW
===========================================================
Number of requests:             3000
Average Input Length (tokens):  128.0000
Average Output Length (tokens): 128.0000
Token Throughput (tokens/sec):  28390.4265
Request Throughput (req/sec):   221.8002
Total Latency (ms):             13525.6862

===========================================================

Workflow

The workflow for trtllm-bench is composed of the following steps:

1. Prepare a dataset to drive the inflight batching benchmark.

2. Build a benchmark engine using trtllm-bench build subcommand (not required for PyTorch flow).

3. Run the max throughput benchmark using the trtllm-bench throughput subcommand or low latency benchmark using the trtllm-bench latency subcommand.

Preparing a Dataset

The throughput benchmark utilizes a fixed JSON schema to specify requests. The schema is defined as follows:

Key	Required	Type	Description
task_id	Y	String	Unique identifier for the request.
prompt	N*	String	Input text for a generation request.
input_ids	Y*	List[Integer]	List of logits that make up the request prompt.
output_tokens	Y	Integer	Number of generated tokens for this request.

Tip

* Specifying prompt or input_ids is required. However, you can not have both prompts and logits (input_ids) defined at the same time. If you specify input_ids, the prompt entry is ignored for request generation.

Refer to the following examples of valid entries for the benchmark:

• Entries with a human-readable prompt and no logits.

  {"task_id": 1, "prompt": "Generate an infinite response to the following: This is the song that never ends, it goes on and on my friend.", "output_tokens": 1000}
  {"task_id": 2, "prompt": "Generate an infinite response to the following: Na, na, na, na", "output_tokens": 1000}
  

• Entries which contain logits.

  {"task_id":0,"input_ids":[863,22056,25603,11943,8932,13195,3132,25032,21747,22213],"output_tokens":128}
  {"task_id":1,"input_ids":[14480,13598,15585,6591,1252,8259,30990,26778,7063,30065,21764,11023,1418],"output_tokens":128}
  

Tip

Specify each entry on one line. To simplify passing the data, a complete JSON entry is on each line so that the benchmarker can simply read a line and assume a complete entry. When creating a dataset, be sure that a complete JSON entry is on every line.

In order to prepare a synthetic dataset, you can use the provided script in the benchmarks/cpp directory. For example, to generate a synthetic dataset of 1000 requests with a uniform ISL/OSL of 128/128 for meta-llama/Llama-3.1-8B, run:

benchmarks/cpp/prepare_dataset.py --stdout --tokenizer meta-llama/Llama-3.1-8B token-norm-dist --input-mean 128 --output-mean 128 --input-stdev 0 --output-stdev 0 --num-requests 1000 > /tmp/synthetic_128_128.txt

Building a Benchmark Engine

Default Build Behavior

The trtllm-bench CLI tool provides the build subcommand to build the TRT-LLM engines for max throughput benchmark. To build an engine for benchmarking, you can specify the dataset generated with prepare_dataset.py through --dataset option. By default, trtllm-bench’s tuning heuristic uses the high-level statistics of the dataset (average ISL/OSL, max sequence length) to optimize engine build settings. The following command builds an FP8 quantized engine optimized using the dataset’s ISL/OSL.

trtllm-bench --model meta-llama/Llama-3.1-8B build --quantization FP8 --dataset /tmp/synthetic_128_128.txt

Other Build Modes

The build subcommand also provides other ways to build the engine where users have larger control over the tuning values.

• Build engine with self-defined tuning values: You specify the tuning values to build the engine with by setting --max_batch_size and --max_num_tokens directly. max_batch_size and max_num_tokens control the maximum number of requests and tokens that can be scheduled in each iteration. If no value is specified, the default max_batch_size and max_num_tokens values of 2048 and 8192 are used. The following command builds an FP8 quantized engine by specifying the engine tuning values.

trtllm-bench --model meta-llama/Llama-3.1-8B build --quantization FP8 --max_seq_len 4096 --max_batch_size 1024 --max_num_tokens 2048

• [Experimental] Build engine with target ISL/OSL for optimization: In this experimental mode, you can provide hints to trtllm-bench’s tuning heuristic to optimize the engine on specific ISL and OSL targets. Generally, the target ISL and OSL aligns with the average ISL and OSL of the dataset, but you can experiment with different values to optimize the engine using this mode. The following command builds an FP8 quantized engine and optmizes for ISL:OSL targets of 128:128.

trtllm-bench --model meta-llama/Llama-3.1-8B build --quantization FP8 --max_seq_len 4096 --target_isl 128 --target_osl 128

Parallelism Mapping Support

The trtllm-bench build subcommand supports combinations of tensor-parallel (TP) and pipeline-parallel (PP) mappings as long as the world size (tp_size x pp_size) <= 8. The parallelism mapping in build subcommad is controlled by --tp_size and --pp_size options. The following command builds an engine with TP2-PP2 mapping.

trtllm-bench --model meta-llama/Llama-3.1-8B build --quantization FP8 --dataset /tmp/synthetic_128_128.txt --tp_size 2 --pp_size 2

Example of Build Subcommand Output:

The output of the build subcommand looks similar to the snippet below (for meta-llama/Llama-3.1-8B):

user@387b12598a9e:/scratch/code/trt-llm/tekit_2025$ trtllm-bench --model meta-llama/Llama-3.1-8B build --dataset /tmp/synthetic_128_128.txt --quantization FP8
[TensorRT-LLM] TensorRT-LLM version: 0.17.0
[01/18/2025-00:55:14] [TRT-LLM] [I] Found dataset.
[01/18/2025-00:55:14] [TRT-LLM] [I]
===========================================================
= DATASET DETAILS
===========================================================
Max Input Sequence Length:      128
Max Output Sequence Length:     128
Max Sequence Length:    256
Target (Average) Input Sequence Length: 128
Target (Average) Output Sequence Length:        128
Number of Sequences:    3000
===========================================================


[01/18/2025-00:55:14] [TRT-LLM] [I] Max batch size and max num tokens are not provided, use tuning heuristics or pre-defined setting from trtllm-bench.
[01/18/2025-00:55:14] [TRT-LLM] [I] Estimated total available memory for KV cache: 132.37 GB
[01/18/2025-00:55:14] [TRT-LLM] [I] Estimated total KV cache memory: 125.75 GB
[01/18/2025-00:55:14] [TRT-LLM] [I] Estimated max number of requests in KV cache memory: 8048.16
[01/18/2025-00:55:14] [TRT-LLM] [I] Estimated max batch size (after fine-tune): 4096
[01/18/2025-00:55:14] [TRT-LLM] [I] Estimated max num tokens (after fine-tune): 8192
[01/18/2025-00:55:14] [TRT-LLM] [I] Set dtype to bfloat16.
[01/18/2025-00:55:14] [TRT-LLM] [I] Set multiple_profiles to True.
[01/18/2025-00:55:14] [TRT-LLM] [I] Set use_paged_context_fmha to True.
[01/18/2025-00:55:14] [TRT-LLM] [I] Set use_fp8_context_fmha to True.
[01/18/2025-00:55:14] [TRT-LLM] [I]
===========================================================
= ENGINE BUILD INFO
===========================================================
Model Name:             meta-llama/Llama-3.1-8B
Model Path:             None
Workspace Directory:    /tmp
Engine Directory:       /tmp/meta-llama/Llama-3.1-8B/tp_1_pp_1

===========================================================
= ENGINE CONFIGURATION DETAILS
===========================================================
Max Sequence Length:            256
Max Batch Size:                 4096
Max Num Tokens:                 8192
Quantization:                   FP8
KV Cache Dtype:                 FP8
===========================================================

Loading Model: [1/3]    Downloading HF model
Downloaded model to /data/models--meta-llama--Llama-3.1-8B/snapshots/d04e592bb4f6aa9cfee91e2e20afa771667e1d4b
Time: 0.321s
Loading Model: [2/3]    Loading HF model to memory
Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:59<00:00, 14.79s/it]
Generating train split: 100%|████████████████████████████████████████████████████████████████████████████████████| 287113/287113 [00:06<00:00, 41375.57 examples/s]
Generating validation split: 100%|█████████████████████████████████████████████████████████████████████████████████| 13368/13368 [00:00<00:00, 41020.63 examples/s]
Generating test split: 100%|███████████████████████████████████████████████████████████████████████████████████████| 11490/11490 [00:00<00:00, 41607.11 examples/s]
Inserted 675 quantizers
/usr/local/lib/python3.12/dist-packages/modelopt/torch/quantization/model_quant.py:71: DeprecationWarning: forward_loop should take model as argument, but got forward_loop without any arguments. This usage will be deprecated in future versions.
  warnings.warn(
Disable lm_head quantization for TRT-LLM export due to deployment limitations.
current rank: 0, tp rank: 0, pp rank: 0
Time: 122.568s
Loading Model: [3/3]    Building TRT-LLM engine
/usr/local/lib/python3.12/dist-packages/tensorrt/__init__.py:85: DeprecationWarning: Context managers for TensorRT types are deprecated. Memory will be freed automatically when the reference count reaches 0.
  warnings.warn(
Time: 53.820s
Loading model done.
Total latency: 176.709s

<snip verbose logging>

===========================================================
ENGINE SAVED: /tmp/meta-llama/Llama-3.1-8B/tp_1_pp_1
===========================================================

The engine in this case will be written to /tmp/meta-llama/Llama-3.1-8B/tp_1_pp_1 (the end of the log).

Max Throughput Benchmark

The trtllm-bench command line tool provides a max throughput benchmark that is accessible via the throughput subcommand. This benchmark tests a TensorRT-LLM engine or PyTorch backend under maximum load to provide an upper bound throughput number.

How the Benchmarker Works

The benchmarker reads a data file where a single line contains a complete JSON request entry as specified in Preparing a Dataset. The process that the benchmarker is as follows:

1. Iterate over all input requests. If logits is specified, construct the request using the specified list of logits. Otherwise, tokenize the prompt with as specified by --model $HF_MODEL_NAME.

2. Submit the dataset to the TensorRT-LLM Executor API as fast as possible (offline mode).

3. Wait for all requests to return, compute statistics, and then report results.

To run the benchmarker, run the following commands with the engine and dataset generated from previous steps:

trtllm-bench --model meta-llama/Llama-3.1-8B throughput --dataset /tmp/synthetic_128_128.txt --engine_dir /tmp/meta-llama/Llama-3.1-8B/tp_1_pp_1
[TensorRT-LLM] TensorRT-LLM version: 0.17.0
[01/18/2025-01:01:13] [TRT-LLM] [I] Preparing to run throughput benchmark...
[01/18/2025-01:01:13] [TRT-LLM] [I] Setting up throughput benchmark.

<snip verbose logging>

[01/18/2025-01:01:26] [TRT-LLM] [I] Setting up for warmup...
[01/18/2025-01:01:26] [TRT-LLM] [I] Running warmup.
[01/18/2025-01:01:26] [TRT-LLM] [I] Starting benchmarking async task.
[01/18/2025-01:01:26] [TRT-LLM] [I] Starting benchmark...
[01/18/2025-01:01:26] [TRT-LLM] [I] Request submission complete. [count=2, time=0.0000s, rate=121847.20 req/s]
[01/18/2025-01:01:28] [TRT-LLM] [I] Benchmark complete.
[01/18/2025-01:01:28] [TRT-LLM] [I] Stopping LLM backend.
[01/18/2025-01:01:28] [TRT-LLM] [I] Cancelling all 0 tasks to complete.
[01/18/2025-01:01:28] [TRT-LLM] [I] All tasks cancelled.
[01/18/2025-01:01:28] [TRT-LLM] [I] LLM Backend stopped.
[01/18/2025-01:01:28] [TRT-LLM] [I] Warmup done.
[01/18/2025-01:01:28] [TRT-LLM] [I] Starting benchmarking async task.
[01/18/2025-01:01:28] [TRT-LLM] [I] Starting benchmark...
[01/18/2025-01:01:28] [TRT-LLM] [I] Request submission complete. [count=3000, time=0.0012s, rate=2590780.97 req/s]
[01/18/2025-01:01:42] [TRT-LLM] [I] Benchmark complete.
[01/18/2025-01:01:42] [TRT-LLM] [I] Stopping LLM backend.
[01/18/2025-01:01:42] [TRT-LLM] [I] Cancelling all 0 tasks to complete.
[01/18/2025-01:01:42] [TRT-LLM] [I] All tasks cancelled.
[01/18/2025-01:01:42] [TRT-LLM] [I] LLM Backend stopped.
[01/18/2025-01:01:42] [TRT-LLM] [I]

===========================================================
= ENGINE DETAILS
===========================================================
Model:                  meta-llama/Llama-3.1-8B
Engine Directory:       /tmp/meta-llama/Llama-3.1-8B/tp_1_pp_1
TensorRT-LLM Version:   0.17.0
Dtype:                  bfloat16
KV Cache Dtype:         FP8
Quantization:           FP8
Max Input Length:       256
Max Sequence Length:    256

===========================================================
= WORLD + RUNTIME INFORMATION
===========================================================
TP Size:                1
PP Size:                1
Max Runtime Batch Size: 4096
Max Runtime Tokens:     8192
Scheduling Policy:      Guaranteed No Evict
KV Memory Percentage:   90.00%
Issue Rate (req/sec):   5.0689E+14

===========================================================
= PERFORMANCE OVERVIEW
===========================================================
Number of requests:             3000
Average Input Length (tokens):  128.0000
Average Output Length (tokens): 128.0000
Token Throughput (tokens/sec):  28390.4265
Request Throughput (req/sec):   221.8002
Total Latency (ms):             13525.6862

===========================================================

[01/18/2025-01:01:42] [TRT-LLM] [I] Thread proxy_dispatch_result_thread stopped.
[TensorRT-LLM][INFO] Refreshed the MPI local session

Running with the PyTorch Workflow

To benchmark the PyTorch backend (tensorrt_llm._torch), use the following command with dataset generated from previous steps. With the PyTorch flow, you will not need to run trtllm-bench build; the throughput benchmark initializes the backend by tuning against the dataset provided via --dataset (or the other build mode settings described above). Note that CUDA graph is enabled by default. You can add additional pytorch config with --extra_llm_api_options followed by the path to a YAML file. For more details, please refer to the help text by running the command with --help.

Tip

The command below specifies the --model_path option. The model path is optional and used only when you want to run a locally stored checkpoint. When using --model_path, the --model is still required for reporting reasons and in order to look up parameters for build heuristics.

trtllm-bench --model meta-llama/Llama-3.1-8B --model_path /Ckpt/Path/To/Llama-3.1-8B throughput --dataset /tmp/synthetic_128_128.txt --backend pytorch

# Example output
<snip verbose logging>
===========================================================
= PyTorch backend
===========================================================
Model:                  meta-llama/Llama-3.1-8B
Model Path:             /Ckpt/Path/To/Llama-3.1-8B
TensorRT-LLM Version:   0.17.0
Dtype:                  bfloat16
KV Cache Dtype:         None
Quantization:           FP8

===========================================================
= WORLD + RUNTIME INFORMATION
===========================================================
TP Size:                1
PP Size:                1
Max Runtime Batch Size: 2048
Max Runtime Tokens:     4096
Scheduling Policy:      Guaranteed No Evict
KV Memory Percentage:   90.00%
Issue Rate (req/sec):   7.6753E+14

===========================================================
= PERFORMANCE OVERVIEW
===========================================================
Number of requests:             3000
Average Input Length (tokens):  128.0000
Average Output Length (tokens): 128.0000
Token Throughput (tokens/sec):  20685.5510
Request Throughput (req/sec):   161.6059
Total Latency (ms):             18563.6825

Quantization in the PyTorch Flow

In order to run a quantized run with trtllm-bench utilizing the PyTorch flow, you will need to use a pre-quantized To run a quantized benchmark with trtllm-bench utilizing the PyTorch flow, you will need to use a pre-quantized checkpoint. For the Llama-3.1 models, TensorRT-LLM provides the following checkpoints via HuggingFace:

• nvidia/Llama-3.1-8B-Instruct-FP8

• nvidia/Llama-3.1-70B-Instruct-FP8

• nvidia/Llama-3.1-405B-Instruct-FP8

trtllm-bench utilizes the hf_quant_config.json file present in the pre-quantized checkpoints above. The configuration file is present in checkpoints quantized with TensorRT Model Optimizer and describes the compute and KV cache quantization that checkpoint was compiled with. For example, from the checkpoints above:

{
    "producer": {
        "name": "modelopt",
        "version": "0.23.0rc1"
    },
    "quantization": {
        "quant_algo": "FP8",
        "kv_cache_quant_algo": null
    }

The checkpoints above are quantized to run with a compute precision of FP8 and default to no KV cache quantization (full FP16 cache). When running trtllm-bench throughput. The benchmark will select a KV cache quantization that is best suited for the compute precision in the checkpoint automatically if kv_cache_quant_algo is specified as null, otherwise it will be forced to match the specified non-null KV cache quantization. The following are the mappings that trtllm-bench will follow when a checkpoint does not specify a KV cache quantization algorithm:

Checkpoint Compute Quant	Checkpoint KV Cache Quant	trtllm-bench	Note
null	null	null	In this case, a quantization config doesn’t exist.
FP8	FP8	FP8	Matches the checkpoint
FP8	null	FP8	Set to FP8 via benchmark
NVFP4	null	FP8	Set to FP8 via benchmark

If you would like to force the KV cache quantizaton, you can specify the following in the YAML file to force the precision when the checkpoint precision is null:

pytorch_backend_config:
  kv_cache_dtype: "fp8"

Tip

The two valid values for kv_cache_dtype are auto and fp8.

Low Latency Benchmark

The low latency benchmark follows a similar workflow to the throughput benchmark but requires building the engine separately from trtllm-bench. Low latency benchmarks has the following modes:

• A single-request low-latency engine

• A Medusa-enabled speculative-decoding engine

Low Latency TensorRT-LLM Engine for Llama-3 70B

To build a low-latency engine for the latency benchmark, run the following quantize and build commands. The $checkpoint_dir is the path to the meta-llama/Meta-Llama-3-70B Hugging Face checkpoint in your cache or downloaded to a specific location with the huggingface-cli. To prepare a dataset, follow the same process as specified in Preparing a Dataset.

Benchmarking a non-Medusa Low Latency Engine

To quantize the checkpoint:

cd tensorrt_llm/examples/llama
python ../quantization/quantize.py \
    --model_dir $checkpoint_dir \
    --dtype bfloat16 \
    --qformat fp8 \
    --kv_cache_dtype fp8 \
    --output_dir /tmp/meta-llama/Meta-Llama-3-70B/checkpoint \
    --calib_size 512 \
    --tp_size $tp_size

then build,

trtllm-build \
    --checkpoint_dir /tmp/meta-llama/Meta-Llama-3-70B/checkpoint \
    --use_fused_mlp enable \
    --gpt_attention_plugin bfloat16 \
    --output_dir /tmp/meta-llama/Meta-Llama-3-70B/engine \
    --max_batch_size 1 \
    --max_seq_len $(($isl+$osl)) \
    --reduce_fusion enable \
    --gemm_plugin fp8 \
    --workers $tp_size \
    --use_fp8_context_fmha enable \
    --max_num_tokens $isl \
    --use_paged_context_fmha disable \
    --multiple_profiles enable

After the engine is built, run the low-latency benchmark:

env TRTLLM_ENABLE_MMHA_MULTI_BLOCK_DEBUG=1 \
  TRTLLM_MMHA_KERNEL_BLOCK_SIZE=256 \
  TRTLLM_MMHA_BLOCKS_PER_SEQUENCE=32 \
  FORCE_MULTI_BLOCK_MODE=ON \
  TRTLLM_ENABLE_PDL=1 \
  trtllm-bench --model meta-llama/Meta-Llama-3-70B \
  latency \
  --dataset $DATASET_PATH \
  --engine_dir /tmp/meta-llama/Meta-Llama-3-70B/engine

Building a Medusa Low-Latency Engine

To build a Medusa-enabled engine requires checkpoints that contain Medusa heads. NVIDIA provides TensorRT-LLM checkpoints on the NVIDIA page on Hugging Face. The checkpoints are pre-quantized and can be directly built after downloading them with the huggingface-cli. After you download the checkpoints, run the following command. Make sure to specify the $tp_size supported by your Medusa checkpoint and the path to its stored location $checkpoint_dir. Additionally, $max_seq_len should be set to the model’s maximum position embedding.

Using Llama-3.1 70B as an example, for a tensor parallel 8 and bfloat16 dtype:

tp_size=8
max_seq_len=131072
trtllm-build --checkpoint_dir $checkpoint_dir \
    --speculative_decoding_mode medusa \
    --max_batch_size 1 \
    --gpt_attention_plugin bfloat16 \
    --max_seq_len $max_seq_len \
    --output_dir /tmp/meta-llama/Meta-Llama-3.1-70B/medusa/engine \
    --use_fused_mlp enable \
    --paged_kv_cache enable \
    --use_paged_context_fmha disable \
    --multiple_profiles enable \
    --reduce_fusion enable \
    --use_fp8_context_fmha enable \
    --workers $tp_size \
    --low_latency_gemm_plugin fp8

After the engine is built, you need to define the Medusa choices. The choices are specified with a YAML file like the following example (medusa.yaml):

- [0]
- [0, 0]
- [1]
- [0, 1]
- [2]
- [0, 0, 0]
- [1, 0]
- [0, 2]
- [3]
- [0, 3]
- [4]
- [0, 4]
- [2, 0]
- [0, 5]
- [0, 0, 1]

To run the Medusa-enabled engine, run the following command:

env TRTLLM_ENABLE_PDL=1 \
  UB_ONESHOT=1 \
  UB_TP_SIZE=$tp_size \
  TRTLLM_ENABLE_PDL=1 \
  TRTLLM_PDL_OVERLAP_RATIO=0.15 \
  TRTLLM_PREFETCH_RATIO=-1 \
  trtllm-bench --model meta-llama/Meta-Llama-3-70B \
  latency \
  --dataset $DATASET_PATH \
  --engine_dir /tmp/meta-llama/Meta-Llama-3-70B/medusa/engine \
  --medusa_choices medusa.yml

Summary

The following table summarizes the commands needed for running benchmarks:

Scenario	Phase	Command
Dataset	Preparation	python benchmarks/cpp/prepare_dataset.py --stdout --tokenizer $HF_MODEL token-norm-dist --input-mean $ISL --output-mean $OSL --input-stdev 0 --output-stdev 0 --num-requests $NUM_REQUESTS > $DATASET_PATH
Throughput	Build	trtllm-bench --model $HF_MODEL build --dataset $DATASET_PATH
Throughput	Benchmark	trtllm-bench --model $HF_MODEL throughput --dataset $DATASET_PATH --engine_dir $ENGINE_DIR
Latency	Build	See section about building low latency engines
Non-Medusa Latency	Benchmark	trtllm-bench --model $HF_MODEL latency --dataset $DATASET_PATH --engine_dir $ENGINE_DIR
Medusa Latency	Benchmark	trtllm-bench --model $HF_MODEL latency --dataset $DATASET_PATH --engine_dir $ENGINE_DIR --medusa_choices $MEDUSA_CHOICES

where,

$HF_MODEL

The Hugging Face name of a model.

$NUM_REQUESTS

The number of requests to generate.

$DATASET_PATH

The path where the dataset was written when preparing the dataset.

$ENGINE_DIR

The engine directory as printed by trtllm-bench build.

$MEDUSA_CHOICES

A YAML config representing the Medusa tree for the benchmark.