hermes-agent/skills/mlops/inference/tensorrt-llm/references/serving.md

Production Serving Guide

Comprehensive guide to deploying TensorRT-LLM in production environments.

Server Modes

trtllm-serve (Recommended)

Features:

• OpenAI-compatible API
• Automatic model download and compilation
• Built-in load balancing
• Prometheus metrics
• Health checks

Basic usage:

trtllm-serve meta-llama/Meta-Llama-3-8B \
    --tp_size 1 \
    --max_batch_size 256 \
    --port 8000

Advanced configuration:

trtllm-serve meta-llama/Meta-Llama-3-70B \
    --tp_size 4 \
    --dtype fp8 \
    --max_batch_size 256 \
    --max_num_tokens 4096 \
    --enable_chunked_context \
    --scheduler_policy max_utilization \
    --port 8000 \
    --api_key $API_KEY  # Optional authentication

Python LLM API (For embedding)

from tensorrt_llm import LLM

class LLMService:
    def __init__(self):
        self.llm = LLM(
            model="meta-llama/Meta-Llama-3-8B",
            dtype="fp8"
        )

    def generate(self, prompt, max_tokens=100):
        from tensorrt_llm import SamplingParams

        params = SamplingParams(
            max_tokens=max_tokens,
            temperature=0.7
        )
        outputs = self.llm.generate([prompt], params)
        return outputs[0].text

# Use in FastAPI, Flask, etc
from fastapi import FastAPI
app = FastAPI()
service = LLMService()

@app.post("/generate")
def generate(prompt: str):
    return {"response": service.generate(prompt)}

OpenAI-Compatible API

Chat Completions

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/Meta-Llama-3-8B",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Explain quantum computing"}
    ],
    "temperature": 0.7,
    "max_tokens": 500,
    "stream": false
  }'

Response:

{
  "id": "chat-abc123",
  "object": "chat.completion",
  "created": 1234567890,
  "model": "meta-llama/Meta-Llama-3-8B",
  "choices": [{
    "index": 0,
    "message": {
      "role": "assistant",
      "content": "Quantum computing is..."
    },
    "finish_reason": "stop"
  }],
  "usage": {
    "prompt_tokens": 25,
    "completion_tokens": 150,
    "total_tokens": 175
  }
}

Streaming

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/Meta-Llama-3-8B",
    "messages": [{"role": "user", "content": "Count to 10"}],
    "stream": true
  }'

Response (SSE stream):

data: {"choices":[{"delta":{"content":"1"}}]}

data: {"choices":[{"delta":{"content":", 2"}}]}

data: {"choices":[{"delta":{"content":", 3"}}]}

data: [DONE]

Completions

curl -X POST http://localhost:8000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/Meta-Llama-3-8B",
    "prompt": "The capital of France is",
    "max_tokens": 10,
    "temperature": 0.0
  }'

Monitoring

Prometheus Metrics

Enable metrics:

trtllm-serve meta-llama/Meta-Llama-3-8B \
    --enable_metrics \
    --metrics_port 9090

Key metrics:

# Scrape metrics
curl http://localhost:9090/metrics

# Important metrics:
# - trtllm_request_success_total - Total successful requests
# - trtllm_request_latency_seconds - Request latency histogram
# - trtllm_tokens_generated_total - Total tokens generated
# - trtllm_active_requests - Current active requests
# - trtllm_queue_size - Requests waiting in queue
# - trtllm_gpu_memory_usage_bytes - GPU memory usage
# - trtllm_kv_cache_usage_ratio - KV cache utilization

Health Checks

# Readiness probe
curl http://localhost:8000/health/ready

# Liveness probe
curl http://localhost:8000/health/live

# Model info
curl http://localhost:8000/v1/models

Kubernetes probes:

livenessProbe:
  httpGet:
    path: /health/live
    port: 8000
  initialDelaySeconds: 60
  periodSeconds: 10

readinessProbe:
  httpGet:
    path: /health/ready
    port: 8000
  initialDelaySeconds: 30
  periodSeconds: 5

Production Deployment

Docker Deployment

Dockerfile:

FROM nvidia/tensorrt_llm:latest

# Copy any custom configs
COPY config.yaml /app/config.yaml

# Expose ports
EXPOSE 8000 9090

# Start server
CMD ["trtllm-serve", "meta-llama/Meta-Llama-3-8B", \
     "--tp_size", "4", \
     "--dtype", "fp8", \
     "--max_batch_size", "256", \
     "--enable_metrics", \
     "--metrics_port", "9090"]

Run container:

docker run --gpus all -p 8000:8000 -p 9090:9090 \
    tensorrt-llm:latest

Kubernetes Deployment

Complete deployment:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: tensorrt-llm
spec:
  replicas: 2  # Multiple replicas for HA
  selector:
    matchLabels:
      app: tensorrt-llm
  template:
    metadata:
      labels:
        app: tensorrt-llm
    spec:
      containers:
      - name: trtllm
        image: nvidia/tensorrt_llm:latest
        command:
          - trtllm-serve
          - meta-llama/Meta-Llama-3-70B
          - --tp_size=4
          - --dtype=fp8
          - --max_batch_size=256
          - --enable_metrics
        ports:
        - containerPort: 8000
          name: http
        - containerPort: 9090
          name: metrics
        resources:
          limits:
            nvidia.com/gpu: 4
        livenessProbe:
          httpGet:
            path: /health/live
            port: 8000
        readinessProbe:
          httpGet:
            path: /health/ready
            port: 8000
---
apiVersion: v1
kind: Service
metadata:
  name: tensorrt-llm
spec:
  selector:
    app: tensorrt-llm
  ports:
  - name: http
    port: 80
    targetPort: 8000
  - name: metrics
    port: 9090
    targetPort: 9090
  type: LoadBalancer

Load Balancing

NGINX configuration:

upstream tensorrt_llm {
    least_conn;  # Route to least busy server
    server trtllm-1:8000 max_fails=3 fail_timeout=30s;
    server trtllm-2:8000 max_fails=3 fail_timeout=30s;
    server trtllm-3:8000 max_fails=3 fail_timeout=30s;
}

server {
    listen 80;
    location / {
        proxy_pass http://tensorrt_llm;
        proxy_read_timeout 300s;  # Long timeout for slow generations
        proxy_connect_timeout 10s;
    }
}

Autoscaling

Horizontal Pod Autoscaler (HPA)

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: tensorrt-llm-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: tensorrt-llm
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Pods
    pods:
      metric:
        name: trtllm_active_requests
      target:
        type: AverageValue
        averageValue: "50"  # Scale when avg >50 active requests

Custom Metrics

# Scale based on queue size
- type: Pods
  pods:
    metric:
      name: trtllm_queue_size
    target:
      type: AverageValue
      averageValue: "10"

Cost Optimization

GPU Selection

A100 80GB ($3-4/hour):

• Use for: 70B models with FP8
• Throughput: 10,000-15,000 tok/s (TP=4)
• Cost per 1M tokens: $0.20-0.30

H100 80GB ($6-8/hour):

• Use for: 70B models with FP8, 405B models
• Throughput: 20,000-30,000 tok/s (TP=4)
• Cost per 1M tokens: $0.15-0.25 (2× faster = lower cost)

L4 ($0.50-1/hour):

• Use for: 7-8B models
• Throughput: 1,000-2,000 tok/s
• Cost per 1M tokens: $0.25-0.50

Batch Size Tuning

Impact on cost:

• Batch size 1: 1,000 tok/s → $3/hour per 1M = $3/M tokens
• Batch size 64: 5,000 tok/s → $3/hour per 5M = $0.60/M tokens
• 5× cost reduction with batching

Recommendation: Target batch size 32-128 for cost efficiency.

Security

API Authentication

# Generate API key
export API_KEY=$(openssl rand -hex 32)

# Start server with authentication
trtllm-serve meta-llama/Meta-Llama-3-8B \
    --api_key $API_KEY

# Client request
curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "...", "messages": [...]}'

Network Policies

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: tensorrt-llm-policy
spec:
  podSelector:
    matchLabels:
      app: tensorrt-llm
  policyTypes:
  - Ingress
  ingress:
  - from:
    - podSelector:
        matchLabels:
          app: api-gateway  # Only allow from gateway
    ports:
    - protocol: TCP
      port: 8000

Troubleshooting

High latency

Diagnosis:

# Check queue size
curl http://localhost:9090/metrics | grep queue_size

# Check active requests
curl http://localhost:9090/metrics | grep active_requests

Solutions:

• Scale horizontally (more replicas)
• Increase batch size (if GPU underutilized)
• Enable chunked context (if long prompts)
• Use FP8 quantization

OOM crashes

Solutions:

• Reduce max_batch_size
• Reduce max_num_tokens
• Enable FP8 or INT4 quantization
• Increase tensor_parallel_size

Timeout errors

NGINX config:

proxy_read_timeout 600s;  # 10 minutes for very long generations
proxy_send_timeout 600s;

Best Practices

1. Use FP8 on H100 for 2× speedup and 50% cost reduction
2. Monitor metrics - Set up Prometheus + Grafana
3. Set readiness probes - Prevent routing to unhealthy pods
4. Use load balancing - Distribute load across replicas
5. Tune batch size - Balance latency and throughput
6. Enable streaming - Better UX for chat applications
7. Set up autoscaling - Handle traffic spikes
8. Use persistent volumes - Cache compiled models
9. Implement retries - Handle transient failures
10. Monitor costs - Track cost per token