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Model Serving

Deploy LLM and ML models for production inference with optimized serving engines, streaming response patterns, and orchestration frameworks. Focuses on self-hosted model serving, GPU optimization, and integration with frontend applications.

When to Use​

Use when:

  • Deploying LLMs for production (self-hosted Llama, Mistral, Qwen)
  • Building AI APIs with streaming responses
  • Serving traditional ML models (scikit-learn, XGBoost, PyTorch)
  • Implementing RAG pipelines with vector databases
  • Optimizing inference throughput and latency
  • Integrating LLM serving with frontend chat interfaces

Multi-Language Support​

This skill provides patterns for:

  • Python: vLLM, BentoML, LangChain (primary for model serving)
  • Rust: Candle (Hugging Face), optimized inference
  • Go: High-performance API gateways
  • TypeScript: Frontend integration, API consumption

Model Serving Selection​

LLM Serving Engines​

vLLM (Recommended Primary)

  • PagedAttention memory management (20-30x throughput improvement)
  • Continuous batching for dynamic request handling
  • OpenAI-compatible API endpoints
  • Use for: Most self-hosted LLM deployments

TensorRT-LLM

  • Maximum GPU efficiency (2-8x faster than vLLM)
  • Requires model conversion and optimization
  • Use for: Production workloads needing absolute maximum throughput

Ollama

  • Local development without GPUs
  • Simple CLI interface
  • Use for: Prototyping, laptop development, educational purposes

Decision Framework:

Self-hosted LLM deployment needed?
├─ Yes, need maximum throughput → vLLM
├─ Yes, need absolute max GPU efficiency → TensorRT-LLM
├─ Yes, local development only → Ollama
└─ No, use managed API (OpenAI, Anthropic) → No serving layer needed

ML Model Serving (Non-LLM)​

BentoML (Recommended)

  • Python-native, easy deployment
  • Adaptive batching for throughput
  • Multi-framework support (scikit-learn, PyTorch, XGBoost)
  • Use for: Most traditional ML model deployments

Triton Inference Server

  • Multi-model serving on same GPU
  • Model ensembles (chain multiple models)
  • Use for: NVIDIA GPU optimization, serving 10+ models

Quick Start: vLLM Server​

# Install
pip install vllm

# Serve a model (OpenAI-compatible API)
vllm serve meta-llama/Llama-3.1-8B-Instruct \
  --dtype auto \
  --max-model-len 4096 \
  --gpu-memory-utilization 0.9 \
  --port 8000

Key Parameters:

  • --dtype: Model precision (auto, float16, bfloat16)
  • --max-model-len: Context window size
  • --gpu-memory-utilization: GPU memory fraction (0.8-0.95)
  • --tensor-parallel-size: Number of GPUs for model parallelism

Streaming Responses (SSE Pattern)​

Backend (FastAPI):

from fastapi import FastAPI
from fastapi.responses import StreamingResponse
from openai import OpenAI
import json

app = FastAPI()
client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")

@app.post("/chat/stream")
async def chat_stream(message: str):
    async def generate():
        stream = client.chat.completions.create(
            model="meta-llama/Llama-3.1-8B-Instruct",
            messages=[{"role": "user", "content": message}],
            stream=True,
            max_tokens=512
        )

        for chunk in stream:
            if chunk.choices[0].delta.content:
                token = chunk.choices[0].delta.content
                yield f"data: {json.dumps({'token': token})}\n\n"

        yield f"data: {json.dumps({'done': True})}\n\n"

    return StreamingResponse(
        generate(),
        media_type="text/event-stream",
        headers={"Cache-Control": "no-cache"}
    )

Frontend (React):

const sendMessage = async (message: string) => {
  const response = await fetch('/chat/stream', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ message })
  })

  const reader = response.body!.getReader()
  const decoder = new TextDecoder()

  while (true) {
    const { done, value } = await reader.read()
    if (done) break

    const chunk = decoder.decode(value)
    const lines = chunk.split('\n\n')

    for (const line of lines) {
      if (line.startsWith('data: ')) {
        const data = JSON.parse(line.slice(6))
        if (data.token) {
          setResponse(prev => prev + data.token)
        }
      }
    }
  }
}

BentoML Service​

import bentoml
from bentoml.io import JSON
import numpy as np

@bentoml.service(
    resources={"cpu": "2", "memory": "4Gi"},
    traffic={"timeout": 10}
)
class IrisClassifier:
    model_ref = bentoml.models.get("iris_classifier:latest")

    def __init__(self):
        self.model = bentoml.sklearn.load_model(self.model_ref)

    @bentoml.api(batchable=True, max_batch_size=32)
    def classify(self, features: list[dict]) -> list[str]:
        X = np.array([[f['sepal_length'], f['sepal_width'],
                       f['petal_length'], f['petal_width']] for f in features])
        predictions = self.model.predict(X)
        return ['setosa', 'versicolor', 'virginica'][predictions]

Performance Optimization​

GPU Memory Estimation​

Rule of thumb for LLMs:

GPU Memory (GB) = Model Parameters (B) × Precision (bytes) × 1.2

Examples:

  • Llama-3.1-8B (FP16): 8B × 2 bytes × 1.2 = 19.2 GB
  • Llama-3.1-70B (FP16): 70B × 2 bytes × 1.2 = 168 GB (requires 2-4 A100s)

Quantization reduces memory:

  • FP16: 2 bytes per parameter
  • INT8: 1 byte per parameter (2x memory reduction)
  • INT4: 0.5 bytes per parameter (4x memory reduction)

vLLM Optimization​

# Enable quantization (AWQ for 4-bit)
vllm serve TheBloke/Llama-3.1-8B-AWQ \
  --quantization awq \
  --gpu-memory-utilization 0.9

# Multi-GPU deployment (tensor parallelism)
vllm serve meta-llama/Llama-3.1-70B-Instruct \
  --tensor-parallel-size 4 \
  --gpu-memory-utilization 0.9

Batching Strategies​

Continuous batching (vLLM default):

  • Dynamically adds/removes requests from batch
  • Higher throughput than static batching
  • No configuration needed

Adaptive batching (BentoML):

@bentoml.api(
    batchable=True,
    max_batch_size=32,
    max_latency_ms=1000  # Wait max 1s to fill batch
)
def predict(self, inputs: list[np.ndarray]) -> list[float]:
    # BentoML automatically batches requests
    return self.model.predict(np.array(inputs))

Production Deployment​

Monitoring Metrics​

Essential LLM metrics:

  • Tokens per second (throughput)
  • Time to first token (TTFT)
  • Inter-token latency
  • GPU utilization and memory
  • Queue depth

Prometheus instrumentation:

from prometheus_client import Counter, Histogram

requests_total = Counter('llm_requests_total', 'Total requests')
tokens_generated = Counter('llm_tokens_generated', 'Total tokens')
request_duration = Histogram('llm_request_duration_seconds', 'Request duration')

@app.post("/chat")
async def chat(request):
    requests_total.inc()
    start = time.time()
    response = await generate(request)
    tokens_generated.inc(len(response.tokens))
    request_duration.observe(time.time() - start)
    return response

Integration Patterns​

Frontend (ai-chat) Integration​

This skill provides the backend serving layer for the ai-chat skill.

Flow:

Frontend (React) → API Gateway → vLLM Server → GPU Inference
     ↑                                                  ↓
     └─────────── SSE Stream (tokens) ─────────────────┘

RAG with Vector Databases​

Architecture:

User Query → LangChain
              ├─> Vector DB (Qdrant) for retrieval
              ├─> Combine context + query
              └─> LLM (vLLM) for generation

Migration from OpenAI API​

vLLM provides OpenAI-compatible endpoints for easy migration:

# Before (OpenAI)
from openai import OpenAI
client = OpenAI(api_key="sk-...")

# After (vLLM)
from openai import OpenAI
client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="not-needed"
)

# Same API calls work!
response = client.chat.completions.create(
    model="meta-llama/Llama-3.1-8B-Instruct",
    messages=[{"role": "user", "content": "Hello"}]
)

Multi-Model Serving​

Route requests to different models based on task:

MODEL_ROUTING = {
    "small": "meta-llama/Llama-3.1-8B-Instruct",  # Fast, cheap
    "large": "meta-llama/Llama-3.1-70B-Instruct", # Accurate, expensive
    "code": "codellama/CodeLlama-34b-Instruct"    # Code-specific
}

@app.post("/chat")
async def chat(message: str, task: str = "small"):
    model = MODEL_ROUTING[task]
    # Route to appropriate vLLM instance

Cost Optimization​

Track token usage:

import tiktoken

def estimate_cost(text: str, model: str, price_per_1k: float):
    encoding = tiktoken.encoding_for_model(model)
    tokens = len(encoding.encode(text))
    return (tokens / 1000) * price_per_1k

# Compare costs
openai_cost = estimate_cost(text, "gpt-4o", 0.005)  # $5 per 1M tokens
self_hosted_cost = 0  # Fixed GPU cost, unlimited tokens

Troubleshooting​

Out of GPU memory:

  • Reduce --max-model-len
  • Lower --gpu-memory-utilization (try 0.8)
  • Enable quantization (--quantization awq)
  • Use smaller model variant

Low throughput:

  • Increase --gpu-memory-utilization (try 0.95)
  • Enable continuous batching (vLLM default)
  • Check GPU utilization (should be >80%)
  • Consider tensor parallelism for multi-GPU

High latency:

  • Reduce batch size if using static batching
  • Check network latency to GPU server
  • Profile with benchmarking scripts

References​