LLM Infrastructure Setup and GPU Cluster Performance Tuning

1. Inference Infrastructure Selection

When deploying large language model inference services, performance and cost are the primary considerations. vLLM, with its PagedAttention memory management mechanism, achieves 2-4x throughput improvement over traditional solutions, making it our inference engine of choice.

2. vLLM Distributed Deployment

For models with over 70B parameters, single-GPU memory is insufficient. We use Tensor Parallelism to split the model across 4 A100-80G GPUs, with Ray for elastic scaling.

3. GPU Memory Optimization

Beyond vLLM’s own optimizations, we enabled FlashAttention-2, quantization inference (FP8/INT4), and Continuous Batching, increasing per-GPU throughput from 1200 tokens/s to 4800 tokens/s.

vLLM Deployment Script

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_name = "meta-llama/Llama-3.1-70B-Instruct"

llm = LLM(
    model=model_name,
    tensor_parallel_size=4,
    gpu_memory_utilization=0.90,
    max_model_len=8192,
    enable_prefix_caching=True,
    enforce_eager=False,
)

tokenizer = AutoTokenizer.from_pretrained(model_name)

sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.9,
    max_tokens=2048,
)

prompts = ["Explain distributed systems architecture in detail."]
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
    print(output.outputs[0].text)

GPU Monitoring Script

#!/bin/bash
INTERVAL=5

echo "Timestamp,GPU_ID,GPU_Util,Memory_Used(MB),Memory_Free(MB),Temperature(C),Power(W)"

while true; do
    TIMESTAMP=$(date +%Y-%m-%dT%H:%M:%S)
    nvidia-smi --query-gpu=index,utilization.gpu,memory.used,memory.free,temperature.gpu,power.draw \
               --format=csv,noheader,nounits \
    | while IFS=, read -r gpu_id util mem_used mem_free temp power; do
        echo "$TIMESTAMP,$gpu_id,$util,$mem_used,$mem_free,$temp,$power"
    done
    sleep $INTERVAL
done

4. NVIDIA MIG Partitioning

For smaller models or development environments, we use MIG to split A100 GPUs into multiple independent instances, improving GPU utilization and tenant isolation.

5. Summary

Building LLM inference infrastructure requires careful consideration of model characteristics, hardware configuration, and inference frameworks. Through the combination of vLLM, FlashAttention, and MIG, we achieved a 40% cost reduction while maintaining millisecond-level response latency.