LLM Infrastructure Setup and GPU Cluster Performance Tuning

Wed, 22 Jul 2026 00:00:00 +0000

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

 
 
 
 

 python
 
 
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

 
 
 
 

 bash
 
 
#!/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.