[Survey] Supported Hardwares and Speed

[junrushao]

UPDATE (08/09/2023):

We have done major performance overhaul in the past few months, and now I'm happy to share the latest results:

• SOTA performance on CUDA: https://github.com/mlc-ai/llm-perf-bench
• SOTA performance on ROCm: https://blog.mlc.ai/2023/08/09/Making-AMD-GPUs-competitive-for-LLM-inference

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

Hi everyone,

We are looking to gather data points on running MLC-LLM on different hardwares and platforms. Our goal is to create a comprehensive reference for new users. Please share your own experiences in this thread! Thank you for your help!

NOTE: for benchmarking, we highly recommended a device of at least 6GB memory, because the model itself takes 2.9G already. For this reason, it is known that the iOS app will crash on a 4GB iPhone.

AMD GPUs

Hardware/GPU	OS	Tokens/sec	Source	Notes
RX 6600XT (8G)	N/A	28.3	GitHub
RX 6750XT	openSUSE TumbleWeed	8.9 - 154.3	GitHub
RX 6700XT	Windows 11	33.7	GitHub
APU 5800H	Windows 11	8.5	GitHub
Raden RX 470 (4G)	AlmaLinux 9.1	9.4	GitHub
Raden Pro 5300M	macOS Venture	12.6	@junrushao	Intel MBP 16" (late 2019)
AMD GPU on Steam Deck	Steam Deck's Linux	TBD	Reddit
RX6800 16G VRAM	macOS Ventura	22.5	GitHub	Intel MBP 13'' (2020)
Radeon RX 6600 (8GB)	Ubuntu 22.04	7.0	Reddit
RX 7900 xtx			Reddit

Macbook

Hardware/GPU	OS	Tokens/sec	Source	Notes
2020 MacBook Pro M1 (8G)	macOS	11.4	GitHub
2021 MacBook Pro M1Pro (16G)	macOS Ventura	17.1	GitHub
M1 Max Mac Studio (64G)	N/A	18.6	GitHub
2021 MacBook Pro M1 Max (32G)	macOS Monterey	21.0	GitHub
MacBook Pro M2 (16G)	macOS Ventura	22.5	GitHub
2021 MacBook M1Pro (32G)	macOS Ventura	19.3	GitHub

Intel GPUs

Hardware/GPU	OS	Tokens/sec	Source	Notes
Arc A770	N/A	3.1 - 118.6	GitHub	perf issues in decoding needs investigation
UHD Graphics (Comet Lake-U GT2) 1G	Windows 10	2.2	GitHub
UHD Graphics 630	macOS Ventura	2.3	@junrushao	Integrated GPU. Intel MBP 16" (late 2019)
Iris Plus Graphics 1536 MB	macOS Ventura	2.6	GitHub	Integrated GPU on MBP
Iris Plus Graphics 645 1536 MB	macOS Ventura	2.9	GitHub	Integrated GPU on MBP

NVIDIA GPUs

Hardware/GPU	OS	Tokens/sec	Source	Notes
GTX 1650 ti (4GB)	Fedora	15.6	GitHub
GTX 1060 (6GB)	Windows 10	16.7	GitHub
RTX 3080	Windows 11	26.0	GitHub
RTX 3060	Debian bookworm	21.3	GitHub
RTX 2080Ti	Windows 10	24.5	GitHub
RTX 3090	N/A	25.7	GitHub
GTX 1660ti	N/A	23.9	GitHub
RTX 3070	N/A	23.3	GitHub

iOS

Hardware/GPU	OS	Tokens/sec	Source	Notes
iPhone 14 Pro	iOS 16.4.1	7.2	@junrushao
iPad Pro 11' with M1	iPadOS 16.1	10.6	GitHub
iPad Pro 11' A12Z	N/A	4.1	GitHub
iPad Pro 11' with M2 (4-th gen)	iPadOS 16.5	14.1	GitHub

Android

Hardware/GPU	OS	Tokens/sec	Link	Notes

[maxtheman]

@junrushao how can we find tokens/sec? I'd say 'quite fast' fastest LLM I've run on this 2020 MacBook Pro M1 8G. 10x faster than your WebGPU demo running with less overall memory usage.

All reports out is the text?

[tqchen]

We just added a new updates #14 which should ship to conda by now, you can type /stats after a conversation to get the measured speed

[maxtheman]

Killer, I'm at encode: 31.9 tok/s, decode: 11.4 tok/s for 2020 MacBook Pro M1 8G with the default vicuna 6b. For reference my decode on the WebGPU demo is like, 0.5/sec.

[Kimiko-AI]

OOM on gtx 1650. Load the model fine, but OOM when generate the first message

[junrushao]

@nRuaif 4GB memory wouldn't be enough. A 6GB one should work

[y-lee]

On iPhone 13, crashes after a few seconds of [System] Initialize.... Phone has 4GB of RAM, which I presume is the cause.

[junrushao]

@y-lee That's correct. The model we are using so far requires 6GB RAM to run smoothly

[jolonf]

On the iPad Pro 11” with M1 I am getting decode of 10.6 tok/s (I have seen slightly higher and lower). It is running iPadOS 16.1.

[Hzfengsy]

encode: 39.5 tok/s, decode: 26.0 tok/s on Windows 11 with RTX-3080
encode: 32.5 tok/s, decode: 17.1 tok/s on Macbook Pro with M1Pro (16 GPUs) and macOS Ventura 13.3.1

[juodumas]

Hardware/GPU	OS	Tokens/sec	Source	Model	Notes
RTX 3060 (12GB)	Debian bookworm	21		vicuna-v1-7b	3644MiB GPU memory used

• /stats after /reset: encode: 72.2 tok/s, decode: 23.2 tok/s
• /stats for 2nd and later messages: encode: 39.3 tok/s, decode: 21.3 tok/s

>>nvidia-smi --query-gpu=memory.used --format=csv     
memory.used [MiB]
3644 MiB