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.

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

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: https://github.com/ggerganov/llama.cpp/pull/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

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)

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)

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)

Apple Silicon (snapshots in May 2024)

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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