techthiyanes / GPU-Benchmarks-on-LLM-Inference

Multiple NVIDIA GPUs or Apple Silicon for Large Language Model Inference?

GPU-Benchmarks-on-LLM-Inference

Multiple NVIDIA GPUs or Apple Silicon for Large Language Model Inference? 🧐

Description

Use llama.cpp to test the LLaMA models inference speed of different GPUs on RunPod, 13-inch M1 MacBook Air, 14-inch M1 Max MacBook Pro, M2 Ultra Mac Studio and 16-inch M3 Max MacBook Pro for LLaMA 3.

Overview

Average speed (tokens/s) of generating 1024 tokens by GPUs on LLaMA 3. Higher speed is better.

GPU	8B Q4_K_M	8B F16	70B Q4_K_M	70B F16
3070 8GB	70.94	OOM	OOM	OOM
3080 10GB	106.40	OOM	OOM	OOM
3080 Ti 12GB	106.71	OOM	OOM	OOM
4070 Ti 12GB	82.21	OOM	OOM	OOM
4080 16GB	106.22	40.29	OOM	OOM
RTX 4000 Ada 20GB	58.59	20.85	OOM	OOM
3090 24GB	111.74	46.51	OOM	OOM
4090 24GB	127.74	54.34	OOM	OOM
RTX 5000 Ada 32GB	89.87	32.67	OOM	OOM
3090 24GB * 2	108.07	47.15	16.29	OOM
4090 24GB * 2	122.56	53.27	19.06	OOM
RTX A6000 48GB	102.22	40.25	14.58	OOM
RTX 6000 Ada 48GB	130.99	51.97	18.36	OOM
A40 48GB	88.95	33.95	12.08	OOM
L40S 48GB	113.60	43.42	15.31	OOM
RTX 4000 Ada 20GB * 4	56.14	20.58	7.33	OOM
A100 PCIe 80GB	138.31	54.56	22.11	OOM
A100 SXM 80GB	133.38	53.18	24.33	OOM
H100 PCIe 80GB	144.49	67.79	25.01	OOM
3090 24GB * 4	104.94	46.40	16.89	OOM
4090 24GB * 4	117.61	52.69	18.83	OOM
RTX 5000 Ada 32GB * 4	82.73	31.94	11.45	OOM
3090 24GB * 6	101.07	45.55	16.93	5.82
4090 24GB * 8	116.13	52.12	18.76	6.45
RTX A6000 48GB * 4	93.73	38.87	14.32	4.74
RTX 6000 Ada 48GB * 4	118.99	50.25	17.96	6.06
A40 48GB * 4	83.79	33.28	11.91	3.98
L40S 48GB * 4	105.72	42.48	14.99	5.03
A100 PCIe 80GB * 4	117.30	51.54	22.68	7.38
A100 SXM 80GB * 4	97.70	45.45	19.60	6.92
H100 PCIe 80GB * 4	118.14	62.90	26.20	9.63
M1 7‑Core GPU 8GB	9.72	OOM	OOM	OOM
M1 Max 32‑Core GPU 64GB	34.49	18.43	4.09	OOM
M2 Ultra 76-Core GPU 192GB	76.28	36.25	12.13	4.71
M3 Max 40‑Core GPU 64GB	50.74	22.39	7.53	OOM

Average 1024 tokens prompt eval speed (tokens/s) by GPUs on LLaMA 3.

GPU	8B Q4_K_M	8B F16	70B Q4_K_M	70B F16
3070 8GB	2283.62	OOM	OOM	OOM
3080 10GB	3557.02	OOM	OOM	OOM
3080 Ti 12GB	3556.67	OOM	OOM	OOM
4070 Ti 12GB	3653.07	OOM	OOM	OOM
4080 16GB	5064.99	6758.90	OOM	OOM
RTX 4000 Ada 20GB	2310.53	2951.87	OOM	OOM
3090 24GB	3865.39	4239.64	OOM	OOM
4090 24GB	6898.71	9056.26	OOM	OOM
RTX 5000 Ada 32GB	4467.46	5835.41	OOM	OOM
3090 24GB * 2	4004.14	4690.50	393.89	OOM
4090 24GB * 2	8545.00	11094.51	905.38	OOM
RTX A6000 48GB	3621.81	4315.18	466.82	OOM
RTX 6000 Ada 48GB	5560.94	6205.44	547.03	OOM
A40 48GB	3240.95	4043.05	239.92	OOM
L40S 48GB	5908.52	2491.65	649.08	OOM
RTX 4000 Ada 20GB * 4	3369.24	4366.64	306.44	OOM
A100 PCIe 80GB	5800.48	7504.24	726.65	OOM
A100 SXM 80GB	5863.92	681.47	796.81	OOM
H100 PCIe 80GB	7760.16	10342.63	984.06	OOM
3090 24GB * 4	4653.93	5713.41	350.06	OOM
4090 24GB * 4	9609.29	12304.19	898.17	OOM
RTX 5000 Ada 32GB * 4	6530.78	2877.66	541.54	OOM
3090 24GB * 6	5153.05	5952.55	739.40	927.23
4090 24GB * 8	9706.82	11818.92	1336.26	1890.48
RTX A6000 48GB * 4	5340.10	6448.85	539.20	792.23
RTX 6000 Ada 48GB * 4	9679.55	12637.94	714.93	1270.39
A40 48GB * 4	4841.98	5931.06	263.36	900.79
L40S 48GB * 4	9008.27	2541.61	634.05	1478.83
A100 PCIe 80GB * 4	8889.35	11670.74	978.06	1733.41
A100 SXM 80GB * 4	7782.25	674.11	539.08	1834.16
H100 PCIe 80GB * 4	11560.23	15612.81	1133.23	2420.10
M1 7‑Core GPU 8GB	87.26	OOM	OOM	OOM
M1 Max 32‑Core GPU 64GB	355.45	418.77	33.01	OOM
M2 Ultra 76-Core GPU 192GB	1023.89	1202.74	117.76	145.82
M3 Max 40‑Core GPU 64GB	678.04	751.49	62.88	OOM

Model

Thanks to shawwn for LLaMA model weights (7B, 13B, 30B, 65B): llama-dl. Access LLaMA 2 from Meta AI. Access LLaMA 3 from Meta Llama 3 on Hugging Face or my Hugging Face repos: Xiongjie Dai.

Usage

Build

• For NVIDIA GPUs, this provides BLAS acceleration using the CUDA cores of your Nvidia GPU:

  !make clean && LLAMA_CUBLAS=1 make -j

• For Apple Silicon, Metal is enabled by default:

  !make clean && make -j

Text Completion

Use argument -ngl 0 to only use the CPU for inference and -ngl 10000 to ensure all layers are offloaded to the GPU.

!./main -ngl 10000 -m ./models/8B-v3/ggml-model-Q4_K_M.gguf --color --temp 1.1 --repeat_penalty 1.1 -c 0 -n 1024 -e -s 0 -p """\
First Citizen:\n\n\
Before we proceed any further, hear me speak.\n\n\
\n\n\
All:\n\n\
Speak, speak.\n\n\
\n\n\
First Citizen:\n\n\
You are all resolved rather to die than to famish?\n\n\
\n\n\
All:\n\n\
Resolved. resolved.\n\n\
\n\n\
First Citizen:\n\n\
First, you know Caius Marcius is chief enemy to the people.\n\n\
\n\n\
All:\n\n\
We know't, we know't.\n\n\
\n\n\
First Citizen:\n\n\
Let us kill him, and we'll have corn at our own price. Is't a verdict?\n\n\
\n\n\
All:\n\n\
No more talking on't; let it be done: away, away!\n\n\
\n\n\
Second Citizen:\n\n\
One word, good citizens.\n\n\
\n\n\
First Citizen:\n\n\
We are accounted poor citizens, the patricians good. What authority surfeits on would relieve us: if they would yield us but the superfluity, \
while it were wholesome, we might guess they relieved us humanely; but they think we are too dear: the leanness that afflicts us, the object of \
our misery, is as an inventory to particularise their abundance; our sufferance is a gain to them Let us revenge this with our pikes, \
ere we become rakes: for the gods know I speak this in hunger for bread, not in thirst for revenge.\n\n\
\n\n\
"""

Note: For Apple Silicon, check the recommendedMaxWorkingSetSize in the result to see how much memory can be allocated on the GPU and maintain its performance. Only 70% of unified memory can be allocated to the GPU on 32GB M1 Max right now, and we expect around 78% of usable memory for the GPU on larger memory. (Source: https://developer.apple.com/videos/play/tech-talks/10580/?time=346) To utilize the whole memory, use -ngl 0 to only use the CPU for inference. (Thanks to: ggerganov/llama.cpp#1826)

Chat template for LLaMA 3 🦙🦙🦙

!./main -ngl 10000 -m ./models/8B-v3-instruct/ggml-model-Q4_K_M.gguf --color -c 0 -n -2 -e -s 0 --mirostat 2 -i --no-display-prompt --keep -1 \
-r '<|eot_id|>' -p '<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHi!<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n' \
--in-prefix '<|start_header_id|>user<|end_header_id|>\n\n' --in-suffix '<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n'

Benchmark

!./llama-bench -p 512,1024,4096,8192 -n 512,1024,4096,8192 -m ./models/8B-v3/ggml-model-Q4_K_M.gguf

Total VRAM Requirements

Model	Quantized size (Q4_K_M)	Original size (f16)
8B	4.58 GB	14.96 GB
70B	39.59 GB	131.42 GB

You may estimate that VRAM requirement using this tool: LLM RAM Calculator

Perplexity table on LLaMA 3 70B

Less perplexity is better. (credit to: dranger003)

Quantization	Size (GiB)	Perplexity (wiki.test)	Delta (FP16)
IQ1_S	14.29	9.8655 +/- 0.0625	248.51%
IQ1_M	15.60	8.5193 +/- 0.0530	201.94%
IQ2_XXS	17.79	6.6705 +/- 0.0405	135.64%
IQ2_XS	19.69	5.7486 +/- 0.0345	103.07%
IQ2_S	20.71	5.5215 +/- 0.0318	95.05%
Q2_K_S	22.79	5.4334 +/- 0.0325	91.94%
IQ2_M	22.46	4.8959 +/- 0.0276	72.35%
Q2_K	24.56	4.7763 +/- 0.0274	68.73%
IQ3_XXS	25.58	3.9671 +/- 0.0211	40.14%
IQ3_XS	27.29	3.7210 +/- 0.0191	31.45%
Q3_K_S	28.79	3.6502 +/- 0.0192	28.95%
IQ3_S	28.79	3.4698 +/- 0.0174	22.57%
IQ3_M	29.74	3.4402 +/- 0.0171	21.53%
Q3_K_M	31.91	3.3617 +/- 0.0172	18.75%
Q3_K_L	34.59	3.3016 +/- 0.0168	16.63%
IQ4_XS	35.30	3.0310 +/- 0.0149	7.07%
IQ4_NL	37.30	3.0261 +/- 0.0149	6.90%
Q4_K_S	37.58	3.0050 +/- 0.0148	6.15%
Q4_K_M	39.60	2.9674 +/- 0.0146	4.83%
Q5_K_S	45.32	2.8843 +/- 0.0141	1.89%
Q5_K_M	46.52	2.8656 +/- 0.0139	1.23%
Q6_K	53.91	2.8441 +/- 0.0138	0.47%
Q8_0	69.83	2.8316 +/- 0.0138	0.03%
F16	131.43	2.8308 +/- 0.0138	0.00%

Benchmarks

TG means "text-generation," and PP means "prompt processing." # for total generated/processing tokens. OOM means out of memory. Average speed in tokens/s.

LLaMA 3 🦙🦙🦙:

NVIDIA Gaming GPUs (OS: Ubuntu 22.04.2 LTS, pytorch:2.2.0, py: 3.10, cuda: 12.1.1 on RunPod) (snapshots in May 2024)

GPU	Model	tg 512	tg 1024	tg 4096	tg 8192	pp 512	pp 1024	pp 4096	pp 8192
3070 8GB	8B Q4_K_M	72.79	70.94	67.01	61.64	2402.51	2283.62	1826.59	1419.97
3080 10GB	8B Q4_K_M	109.57	106.40	98.67	89.90	3728.86	3557.02	2852.06	2232.21
3080 Ti 12GB	8B Q4_K_M	110.60	106.71	98.34	88.63	3690.30	3556.67	2947.11	2381.52
4070 Ti 12GB	8B Q4_K_M	83.50	82.21	78.59	73.46	3936.29	3653.07	2729.71	2019.71
4080 16GB	8B Q4_K_M	108.15	106.22	100.44	93.71	5389.74	5064.99	3790.96	2882.03
	8B F16	40.58	40.29	39.44	OOM	7246.97	6758.90	4720.22	OOM
3090 24GB	8B Q4_K_M	115.42	111.74	97.31	87.49	4030.40	3865.39	3169.91	2527.40
	8B F16	47.40	46.51	44.79	42.62	4444.65	4239.64	3410.47	2667.14
4090 24GB	8B Q4_K_M	130.58	127.74	119.44	110.66	7138.99	6898.71	5265.68	4039.68
	8B F16	54.84	54.34	52.63	50.88	9382.00	9056.26	6531.36	4744.18
3090 24GB * 2	8B Q4_K_M	111.67	108.07	99.60	90.77	3336.37	4004.14	4013.34	3433.59
	8B F16	47.72	47.15	45.56	43.61	4122.66	4690.50	4788.60	3851.37
	70B Q4_K_M	16.57	16.29	15.36	14.34	357.32	393.89	379.52	338.82
4090 24GB * 2	8B Q4_K_M	124.65	122.56	114.32	106.18	7003.51	8545.00	8422.04	6895.68
	8B F16	53.64	53.27	51.64	49.83	9177.92	11094.51	10329.29	8067.29
	70B Q4_K_M	19.22	19.06	18.54	17.92	839.43	905.38	846.38	723.24
3090 24GB * 4	8B Q4_K_M	108.66	104.94	97.09	88.35	3742.66	4653.93	5826.91	4913.40
	8B F16	47.07	46.40	44.76	42.81	4608.40	5713.41	6596.17	5361.52
	70B Q4_K_M	17.07	16.89	16.24	15.39	300.79	350.06	367.75	331.37
4090 24GB * 4	8B Q4_K_M	120.32	117.61	110.52	103.13	6748.96	9609.29	12491.10	10993.75
	8B F16	53.10	52.69	51.00	49.21	8750.57	12304.19	15143.84	12919.74
	70B Q4_K_M	19.80	18.83	18.35	17.66	834.74	898.17	839.97	718.01
3090 24GB * 6	8B Q4_K_M	104.17	101.07	94.06	85.93	3359.99	5153.05	7690.65	7084.44
	8B F16	46.23	45.55	43.99	42.15	3875.97	5952.55	9437.91	8780.49
	70B Q4_K_M	17.09	16.93	16.32	15.45	456.95	739.40	786.79	695.44
	70B F16	5.85	5.82	5.76	5.53	579.00	927.23	998.79	813.99
4090 24GB * 8	8B Q4_K_M	118.09	116.13	108.37	100.95	6172.06	9706.82	15089.45	13802.08
	8B F16	52.51	52.12	50.39	48.72	7889.26	11818.92	16462.18	14300.98
	70B Q4_K_M	18.94	18.76	18.23	17.57	812.95	1336.26	1488.36	1320.36
	70B F16	6.47	6.45	6.39	6.31	1183.87	1890.48	2311.43	1995.85

NVIDIA Professional GPUs (OS: Ubuntu 22.04.2 LTS, pytorch:2.2.0, py: 3.10, cuda: 12.1.1 on RunPod) (snapshots in May 2024)

GPU	Model	tg 512	tg 1024	tg 4096	tg 8192	pp 512	pp 1024	pp 4096	pp 8192
RTX 4000 Ada 20GB	8B Q4_K_M	59.15	58.59	55.94	52.39	2451.93	2310.53	1798.01	1337.15
	8B F16	20.92	20.85	20.50	20.01	3121.67	2951.87	2200.58	1557.00
RTX 5000 Ada 32GB	8B Q4_K_M	91.39	89.87	85.01	80.00	4761.12	4467.46	3272.94	2422.33
	8B F16	32.84	32.67	32.04	31.27	6160.57	5835.41	4008.30	2808.89
RTX A6000 48GB	8B Q4_K_M	105.39	102.22	94.82	86.73	3780.55	3621.81	2917.23	2292.61
	8B F16	40.71	40.25	39.14	37.73	4511.02	4315.18	3365.79	2566.46
	70B Q4_K_M	14.71	14.58	14.09	13.42	482.19	466.82	404.61	340.73
RTX 6000 Ada 48GB	8B Q4_K_M	133.44	130.99	120.74	111.57	5791.74	5560.94	4495.19	3542.57
	8B F16	52.32	51.97	50.21	48.79	6663.13	6205.44	4969.46	3915.81
	70B Q4_K_M	18.52	18.36	17.80	16.97	565.98	547.03	481.59	419.76
A40 48GB	8B Q4_K_M	91.27	88.95	83.10	76.45	3324.98	3240.95	2586.50	2013.34
	8B F16	34.26	33.95	33.06	31.93	4203.75	4043.05	3069.98	2295.02
	70B Q4_K_M	11.60	12.08	11.68	11.26	209.38	239.92	268.89	291.13
L40S 48GB	8B Q4_K_M	115.55	113.60	105.50	97.98	6035.24	5908.52	4335.18	3192.70
	8B F16	43.69	43.42	42.22	41.05	2253.93	2491.65	2887.70	3312.16
	70B Q4_K_M	15.46	15.31	14.92	14.45	673.63	649.08	542.29	446.48
RTX 4000 Ada 20GB * 4	8B Q4_K_M	56.64	56.14	53.58	50.19	2413.07	3369.24	4404.45	3733.15
	8B F16	20.65	20.58	20.24	19.74	3220.21	4366.64	5366.39	4323.70
	70B Q4_K_M	7.36	7.33	7.12	6.84	282.28	306.44	290.70	243.45
A100 PCIe 80GB	8B Q4_K_M	140.62	138.31	127.22	117.60	5981.04	5800.48	4959.84	4083.37
	8B F16	54.84	54.56	53.02	51.24	7741.34	7504.24	6137.54	4849.11
	70B Q4_K_M	22.31	22.11	20.93	19.53	744.12	726.65	653.20	573.95
A100 SXM 80GB	8B Q4_K_M	135.04	133.38	125.09	115.92	5947.64	5863.92	5121.60	4137.08
	8B F16	53.49	53.18	52.03	50.52	603.76	681.47	866.13	1323.07
	70B Q4_K_M	24.61	24.33	22.91	21.32	817.58	796.81	714.07	625.66
H100 PCIe 80GB	8B Q4_K_M	145.55	144.49	136.06	126.83	8125.45	7760.16	6423.31	5185.03
	8B F16	68.03	67.79	65.97	63.55	10815.51	10342.63	8106.53	6191.45
	70B Q4_K_M	25.03	25.01	23.82	22.39	1012.73	984.06	863.37	741.52
RTX 5000 Ada 32GB * 4	8B Q4_K_M	84.07	82.73	78.45	74.11	4671.34	6530.78	8004.94	6790.82
	8B F16	32.10	31.94	31.32	30.58	2427.96	2877.66	3836.89	5235.00
	70B Q4_K_M	11.51	11.45	11.24	10.94	502.37	541.54	504.23	424.29
RTX A6000 48GB * 4	8B Q4_K_M	96.48	93.73	87.72	80.88	3712.99	5340.10	7126.45	6438.82
	8B F16	39.34	38.87	37.81	36.51	4508.60	6448.85	8327.16	7298.18
	70B Q4_K_M	14.44	14.32	13.91	13.32	496.08	539.20	511.22	434.31
	70B F16	4.76	4.74	4.70	4.63	510.31	792.23	751.37	748.06
RTX 6000 Ada 48GB * 4	8B Q4_K_M	121.21	118.99	110.65	103.18	6640.86	9679.55	11734.85	10278.14
	8B F16	50.61	50.25	48.69	47.18	8953.30	12637.94	13971.34	11702.36
	70B Q4_K_M	18.13	17.96	17.49	16.89	656.61	714.93	697.10	612.54
	70B F16	6.08	6.06	6.01	5.94	864.12	1270.39	1363.75	1182.28
A40 48GB * 4	8B Q4_K_M	85.91	83.79	78.56	72.70	3321.27	4841.98	6442.38	5742.84
	8B F16	33.60	33.28	32.42	31.38	4144.88	5931.06	7544.92	6516.60
	70B Q4_K_M	11.99	11.91	11.60	11.17	236.86	263.36	300.57	312.31
	70B F16	3.99	3.98	3.95	3.90	610.51	900.79	893.28	735.16
L40S 48GB * 4	8B Q4_K_M	107.53	105.72	98.59	92.20	6125.69	9008.27	10566.97	9017.90
	8B F16	42.70	42.48	41.33	40.19	2211.45	2541.61	3093.33	4336.81
	70B Q4_K_M	15.12	14.99	14.63	14.17	591.05	634.05	605.66	541.67
	70B F16	5.05	5.03	4.99	4.94	1042.13	1478.83	1427.77	1150.63
A100 PCIe 80GB * 4	8B Q4_K_M	119.28	117.30	110.75	103.87	6076.58	8889.35	12724.54	11803.39
	8B F16	51.63	51.54	50.20	48.73	8088.79	11670.74	16025.11	14269.17
	70B Q4_K_M	22.91	22.68	21.41	19.96	771.28	978.06	1138.60	1043.15
	70B F16	7.40	7.38	7.23	7.06	1172.14	1733.41	1846.36	1592.37
A100 SXM 80GB * 4	8B Q4_K_M	99.73	97.70	92.09	86.27	4850.88	7782.25	12242.53	11535.66
	8B F16	45.53	45.45	44.33	43.09	626.75	674.11	1003.37	1612.05
	70B Q4_K_M	19.87	19.60	18.48	17.19	468.86	539.08	712.08	802.23
	70B F16	6.95	6.92	6.77	6.58	1233.31	1834.16	1972.48	1699.56
H100 PCIe 80GB * 4	8B Q4_K_M	123.08	118.14	113.12	110.34	8054.58	11560.23	16128.27	14682.97
	8B F16	64.00	62.90	61.45	59.72	11107.40	15612.81	20561.03	17762.96
	70B Q4_K_M	26.40	26.20	24.60	23.68	1048.29	1133.23	1088.99	950.92
	70B F16	9.67	9.63	9.46	9.23	1681.45	2420.10	2437.53	2031.77

Apple Silicon (snapshots in May 2024)

GPU	Model	tg 512	tg 1024	tg 4096	tg 8192	pp 512	pp 1024	pp 4096	pp 8192
M1 7‑Core GPU 8GB	8B Q4_K_M	10.20	9.72	11.77	OOM	94.48	87.26	96.53	OOM
M1 Max 32‑Core GPU 64GB	8B Q4_K_M	35.73	34.49	31.18	26.84	408.23	355.45	329.84	302.92
	8B F16	18.75	18.43	16.33	15.03	517.34	418.77	374.09	351.46
	70B Q4_K_M	4.34	4.09	4.09	3.71	34.96	33.01	32.64	30.97
M2 Ultra 76-Core GPU 192GB	8B Q4_K_M	78.81	76.28	64.58	54.13	994.04	1023.89	979.47	913.55
	8B F16	36.90	36.25	33.67	30.68	1175.40	1202.74	1194.21	1103.44
	70B Q4_K_M	12.48	12.13	10.75	9.34	118.79	117.76	109.53	108.57
	70B F16	4.76	4.71	4.48	4.23	147.58	145.82	133.75	135.15
M3 Max 40‑Core GPU 64GB	8B Q4_K_M	48.97	50.74	44.21	36.12	693.32	678.04	573.09	505.32
	8B F16	22.04	22.39	20.72	18.74	769.84	751.49	609.97	515.15
	70B Q4_K_M	7.65	7.53	6.58	5.60	70.19	62.88	64.90	61.96

Conclusion

Same performance under the same size and quantization models. Multiple NVIDIA GPUs might affect text-generation performance but can still boost the prompt processing speed.

Buy NVIDIA gaming GPUs to save money. Buy professional GPUs for your business. Buy a Mac if you want to put your computer on your desk, save energy, be quiet, don't wanna maintenance, and have more fun. 😇

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