High-Performance AI Model Serving

The Inference Challenge

Production LLM serving requires:

Low latency (< 1 second first token)

High throughput (100+ requests/second)

Efficient GPU utilization

Cost-effective scaling

vLLM: Efficient LLM Serving

python
from vllm import LLM, SamplingParams
Initialize with PagedAttention for memory efficiency
llm = LLM(
    model="meta-llama/Llama-3-8B-Instruct",
    tensor_parallel_size=2,  # Use 2 GPUs
    gpu_memory_utilization=0.90,
    max_model_len=4096
)
sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.9,
    max_tokens=512
)
Batch inference (much more efficient than sequential)
prompts = ["Explain quantum computing", "What is machine learning?"]
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
    print(output.outputs[0].text)

vLLM OpenAI-Compatible Server

bash
Start OpenAI-compatible server
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3-8B-Instruct \
    --tensor-parallel-size 2 \
    --port 8000

python
Connect like OpenAI API
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="dummy")
response = client.chat.completions.create(
    model="meta-llama/Llama-3-8B-Instruct",
    messages=[{"role": "user", "content": "Hello!"}]
)

NVIDIA Triton Inference Server

python
import tritonclient.http as httpclient
import numpy as np
client = httpclient.InferenceServerClient("localhost:8000")
Prepare input
text_input = np.array([["Your prompt here"]], dtype=object)
inputs = [httpclient.InferInput("text_input", text_input.shape, "BYTES")]
inputs[0].set_data_from_numpy(text_input)
Run inference
outputs = [httpclient.InferRequestedOutput("text_output")]
response = client.infer("ensemble_model", inputs=inputs, outputs=outputs)
result = response.as_numpy("text_output")

Optimization Strategies

Continuous Batching

vLLM automatically batches requests as they arrive, dramatically improving GPU utilization.

Speculative Decoding

Use a smaller "draft" model to generate token candidates, verified by the larger model:

2-3x speedup with minimal quality loss

Quantization for Cost Reduction

python
llm = LLM(
    model="meta-llama/Llama-3-8B-Instruct",
    quantization="awq",  # 4-bit quantization
    dtype="float16"
)

Benchmarking Your Setup

bash
Benchmark with locust
locust -f llm_loadtest.py --headless -u 50 -r 5 --run-time 60s