llama.cpp/examples/llama-bench

llama.cpp/example/llama-bench

Performance testing tool for llama.cpp.

Table of contents

1. Syntax
2. Examples
   1. Text generation with different models
   2. Prompt processing with different batch sizes
   3. Different numbers of threads
   4. Different numbers of layers offloaded to the GPU
3. Output formats
   1. Markdown
   2. CSV
   3. JSON
   4. SQL

Syntax

usage: ./llama-bench [options]

options:
  -h, --help
  -m, --model <filename>            (default: models/7B/ggml-model-q4_0.gguf)
  -p, --n-prompt <n>                (default: 512)
  -n, --n-gen <n>                   (default: 128)
  -b, --batch-size <n>              (default: 512)
  --memory-f32 <0|1>                (default: 0)
  -t, --threads <n>                 (default: 16)
  -ngl N, --n-gpu-layers <n>        (default: 99)
  -mg i, --main-gpu <i>             (default: 0)
  -mmq, --mul-mat-q <0|1>           (default: 1)
  -ts, --tensor_split <ts0/ts1/..>
  -r, --repetitions <n>             (default: 5)
  -o, --output <csv|json|md|sql>    (default: md)
  -v, --verbose                     (default: 0)

Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.

llama-bench can perform two types of tests:

• Prompt processing (pp): processing a prompt in batches (-p)
• Text generation (tg): generating a sequence of tokens (-n)

With the exception of -r, -o and -v, all options can be specified multiple times to run multiple tests. Each pp and tg test is run with all combinations of the specified options. To specify multiple values for an option, the values can be separated by commas (e.g. -n 16,32), or the option can be specified multiple times (e.g. -n 16 -n 32).

Each test is repeated the number of times given by -r, and the results are averaged. The results are given in average tokens per second (t/s) and standard deviation. Some output formats (e.g. json) also include the individual results of each repetition.

For a description of the other options, see the main example.

Examples

Text generation with different models

$ ./llama-bench -m models/7B/ggml-model-q4_0.gguf -m models/13B/ggml-model-q4_0.gguf -p 0 -n 128,256,512

model	size	params	backend	ngl	test	t/s
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	99	tg 128	132.19 ± 0.55
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	99	tg 256	129.37 ± 0.54
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	99	tg 512	123.83 ± 0.25
llama 13B mostly Q4_0	6.86 GiB	13.02 B	CUDA	99	tg 128	82.17 ± 0.31
llama 13B mostly Q4_0	6.86 GiB	13.02 B	CUDA	99	tg 256	80.74 ± 0.23
llama 13B mostly Q4_0	6.86 GiB	13.02 B	CUDA	99	tg 512	78.08 ± 0.07

Prompt processing with different batch sizes

$ ./llama-bench -n 0 -p 1024 -b 128,256,512,1024

model	size	params	backend	ngl	n_batch	test	t/s
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	99	128	pp 1024	1436.51 ± 3.66
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	99	256	pp 1024	1932.43 ± 23.48
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	99	512	pp 1024	2254.45 ± 15.59
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	99	1024	pp 1024	2498.61 ± 13.58

Different numbers of threads

$ ./llama-bench -n 0 -n 16 -p 64 -t 1,2,4,8,16,32

model	size	params	backend	threads	test	t/s
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CPU	1	pp 64	6.17 ± 0.07
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CPU	1	tg 16	4.05 ± 0.02
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CPU	2	pp 64	12.31 ± 0.13
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CPU	2	tg 16	7.80 ± 0.07
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CPU	4	pp 64	23.18 ± 0.06
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CPU	4	tg 16	12.22 ± 0.07
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CPU	8	pp 64	32.29 ± 1.21
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CPU	8	tg 16	16.71 ± 0.66
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CPU	16	pp 64	33.52 ± 0.03
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CPU	16	tg 16	15.32 ± 0.05
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CPU	32	pp 64	59.00 ± 1.11
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CPU	32	tg 16	16.41 ± 0.79

Different numbers of layers offloaded to the GPU

$ ./llama-bench -ngl 10,20,30,31,32,33,34,35

model	size	params	backend	ngl	test	t/s
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	10	pp 512	373.36 ± 2.25
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	10	tg 128	13.45 ± 0.93
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	20	pp 512	472.65 ± 1.25
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	20	tg 128	21.36 ± 1.94
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	30	pp 512	631.87 ± 11.25
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	30	tg 128	40.04 ± 1.82
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	31	pp 512	657.89 ± 5.08
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	31	tg 128	48.19 ± 0.81
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	32	pp 512	688.26 ± 3.29
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	32	tg 128	54.78 ± 0.65
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	33	pp 512	704.27 ± 2.24
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	33	tg 128	60.62 ± 1.76
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	34	pp 512	881.34 ± 5.40
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	34	tg 128	71.76 ± 0.23
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	35	pp 512	2400.01 ± 7.72
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	35	tg 128	131.66 ± 0.49

Output formats

By default, llama-bench outputs the results in markdown format. The results can be output in other formats by using the -o option.

Markdown

$ ./llama-bench -o md

model	size	params	backend	ngl	test	t/s
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	99	pp 512	2368.80 ± 93.24
llama 7B mostly Q4_0	3.56 GiB	6.74 B	CUDA	99	tg 128	131.42 ± 0.59

CSV

$ ./llama-bench -o csv

build_commit,build_number,cuda,opencl,metal,gpu_blas,blas,cpu_info,gpu_info,model_filename,model_type,model_size,model_n_params,n_batch,n_threads,f16_kv,n_gpu_layers,main_gpu,mul_mat_q,tensor_split,n_prompt,n_gen,test_time,avg_ns,stddev_ns,avg_ts,stddev_ts
"3469684","1275","1","0","0","1","1","13th Gen Intel(R) Core(TM) i9-13900K","NVIDIA GeForce RTX 3090 Ti","models/7B/ggml-model-q4_0.gguf","llama 7B mostly Q4_0","3825065984","6738415616","512","16","1","99","0","1","0.00","512","0","2023-09-23T12:09:01Z","212155977","732372","2413.341687","8.305961"
"3469684","1275","1","0","0","1","1","13th Gen Intel(R) Core(TM) i9-13900K","NVIDIA GeForce RTX 3090 Ti","models/7B/ggml-model-q4_0.gguf","llama 7B mostly Q4_0","3825065984","6738415616","512","16","1","99","0","1","0.00","0","128","2023-09-23T12:09:02Z","969320879","2728399","132.052051","0.371342"

JSON

$ ./llama-bench -o json

[
  {
    "build_commit": "3469684",
    "build_number": 1275,
    "cuda": true,
    "opencl": false,
    "metal": false,
    "gpu_blas": true,
    "blas": true,
    "cpu_info": "13th Gen Intel(R) Core(TM) i9-13900K",
    "gpu_info": "NVIDIA GeForce RTX 3090 Ti",
    "model_filename": "models/7B/ggml-model-q4_0.gguf",
    "model_type": "llama 7B mostly Q4_0",
    "model_size": 3825065984,
    "model_n_params": 6738415616,
    "n_batch": 512,
    "n_threads": 16,
    "f16_kv": true,
    "n_gpu_layers": 99,
    "main_gpu": 0,
    "mul_mat_q": true,
    "tensor_split": "0.00",
    "n_prompt": 512,
    "n_gen": 0,
    "test_time": "2023-09-23T12:09:57Z",
    "avg_ns": 212365953,
    "stddev_ns": 985423,
    "avg_ts": 2410.974041,
    "stddev_ts": 11.163766,
    "samples_ns": [ 213837238, 211635853, 212328053, 211329715, 212698907 ],
    "samples_ts": [ 2394.34, 2419.25, 2411.36, 2422.75, 2407.16 ]
  },
  {
    "build_commit": "3469684",
    "build_number": 1275,
    "cuda": true,
    "opencl": false,
    "metal": false,
    "gpu_blas": true,
    "blas": true,
    "cpu_info": "13th Gen Intel(R) Core(TM) i9-13900K",
    "gpu_info": "NVIDIA GeForce RTX 3090 Ti",
    "model_filename": "models/7B/ggml-model-q4_0.gguf",
    "model_type": "llama 7B mostly Q4_0",
    "model_size": 3825065984,
    "model_n_params": 6738415616,
    "n_batch": 512,
    "n_threads": 16,
    "f16_kv": true,
    "n_gpu_layers": 99,
    "main_gpu": 0,
    "mul_mat_q": true,
    "tensor_split": "0.00",
    "n_prompt": 0,
    "n_gen": 128,
    "test_time": "2023-09-23T12:09:59Z",
    "avg_ns": 977425219,
    "stddev_ns": 9268593,
    "avg_ts": 130.965708,
    "stddev_ts": 1.238924,
    "samples_ns": [ 984472709, 974901233, 989474741, 970729355, 967548060 ],
    "samples_ts": [ 130.019, 131.295, 129.362, 131.86, 132.293 ]
  }
]

SQL

SQL output is suitable for importing into a SQLite database. The output can be piped into the sqlite3 command line tool to add the results to a database.

$ ./llama-bench -o sql

CREATE TABLE IF NOT EXISTS test (
  build_commit TEXT,
  build_number INTEGER,
  cuda INTEGER,
  opencl INTEGER,
  metal INTEGER,
  gpu_blas INTEGER,
  blas INTEGER,
  cpu_info TEXT,
  gpu_info TEXT,
  model_filename TEXT,
  model_type TEXT,
  model_size INTEGER,
  model_n_params INTEGER,
  n_batch INTEGER,
  n_threads INTEGER,
  f16_kv INTEGER,
  n_gpu_layers INTEGER,
  main_gpu INTEGER,
  mul_mat_q INTEGER,
  tensor_split TEXT,
  n_prompt INTEGER,
  n_gen INTEGER,
  test_time TEXT,
  avg_ns INTEGER,
  stddev_ns INTEGER,
  avg_ts REAL,
  stddev_ts REAL
);

INSERT INTO test (build_commit, build_number, cuda, opencl, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '512', '0', '2023-09-23T12:10:30Z', '212693772', '743623', '2407.240204', '8.409634');
INSERT INTO test (build_commit, build_number, cuda, opencl, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '0', '128', '2023-09-23T12:10:31Z', '977925003', '4037361', '130.891159', '0.537692');