Model Serving

Model Serving

Purpose

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

• 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

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:

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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

LLM Orchestration

LangChain

• General-purpose workflows, agents, RAG
• 100+ integrations (LLMs, vector DBs, tools)
• Use for: Most RAG and agent applications

LlamaIndex

• RAG-focused with advanced retrieval strategies
• 100+ data connectors (PDF, Notion, web)
• Use for: RAG is primary use case

Quick Start Examples

vLLM Server Setup

bashCopy code
# 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):

pythonCopy code
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):

typescriptCopy code
// Integration with ai-chat skill
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

pythonCopy code
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]

LangChain RAG Pipeline

pythonCopy code
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_community.vectorstores import Qdrant
from langchain.chains import RetrievalQA
from langchain.text_splitter import RecursiveCharacterTextSplitter

# Load and chunk documents
text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)
chunks = text_splitter.split_documents(documents)

# Create vector store
embeddings = OpenAIEmbeddings()
vectorstore = Qdrant.from_documents(
    chunks,
    embeddings,
    url="http://localhost:6333",
    collection_name="docs"
)

# Create retrieval chain
llm = ChatOpenAI(model="gpt-4o")
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=vectorstore.as_retriever(search_kwargs={"k": 3}),
    return_source_documents=True
)

# Query
result = qa_chain({"query": "What is PagedAttention?"})

Performance Optimization

GPU Memory Estimation

Rule of thumb for LLMs:

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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

bashCopy code
# 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):

pythonCopy code
@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

Kubernetes Deployment

See examples/k8s-vllm-deployment/ for complete YAML manifests.

Key considerations:

• GPU resource requests: nvidia.com/gpu: 1
• Health checks: /health endpoint
• Horizontal Pod Autoscaling based on queue depth
• Persistent volume for model caching

API Gateway Pattern

For production, add rate limiting, authentication, and monitoring:

Kong Configuration:

yamlCopy code
services:
  - name: vllm-service
    url: http://vllm-llama-8b:8000
    plugins:
      - name: rate-limiting
        config:
          minute: 60  # 60 requests per minute per API key
      - name: key-auth
      - name: prometheus

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:

pythonCopy code
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:

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

See references/streaming-sse.md for complete implementation patterns.

RAG with Vector Databases

Architecture:

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

See references/langchain-orchestration.md and examples/langchain-rag-qdrant/ for complete patterns.

Async Inference Queue

For batch processing or non-real-time inference:

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Client → API → Message Queue (Celery) → Workers (vLLM) → Results DB

Useful for:

• Batch document processing
• Background summarization
• Non-interactive workflows

Benchmarking

Use scripts/benchmark_inference.py to measure the deployment:

bashCopy code
python scripts/benchmark_inference.py \
  --endpoint http://localhost:8000/v1/chat/completions \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --concurrency 32 \
  --requests 1000

Outputs:

• Requests per second
• P50/P95/P99 latency
• Tokens per second
• GPU memory usage

Bundled Resources

Detailed Guides:

• references/vllm.md - vLLM setup, PagedAttention, optimization
• references/tgi.md - Text Generation Inference patterns
• references/bentoml.md - BentoML deployment patterns
• references/langchain-orchestration.md - LangChain RAG and agents
• references/inference-optimization.md - Quantization, batching, GPU tuning

Working Examples:

• examples/vllm-serving/ - Complete vLLM + FastAPI streaming setup
• examples/ollama-local/ - Local development with Ollama
• examples/langchain-agents/ - LangChain agent patterns

Utility Scripts:

• scripts/benchmark_inference.py - Throughput and latency benchmarking
• scripts/validate_model_config.py - Validate deployment configurations

Common Patterns

Migration from OpenAI API

vLLM provides OpenAI-compatible endpoints for easy migration:

pythonCopy code
# 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:

pythonCopy code
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:

pythonCopy code
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 scripts/benchmark_inference.py

Next Steps

1. Local Development: Start with examples/ollama-local/ for GPU-free testing
2. Production Setup: Deploy vLLM with examples/vllm-serving/
3. RAG Integration: Add vector DB with examples/langchain-rag-qdrant/
4. Kubernetes: Scale with examples/k8s-vllm-deployment/
5. Monitoring: Add metrics with Prometheus and Grafana