How to Launch a Production Ready LLM API with GPU Auto Scaling in Under an Hour

Deploying large language models (LLMs) to production presents unique challenges: GPU resource management, model loading times, variable inference demands, and cost optimization. Traditional deployment approaches often struggle with the complexity of GPU orchestration and the dynamic scaling requirements of AI workloads.

In this comprehensive guide, we'll show you how to deploy a production-ready LLM serving API using Convox, complete with GPU auto-scaling, intelligent caching, and zero-downtime deployments. By the end, you'll have a fully functional LLM API that can handle everything from casual queries to high-volume inference workloads while automatically optimizing GPU resource usage and costs.

What We'll Build

We'll create a real-time text generation API using:

FastAPI for the web framework
Transformers for LLM inference with GPU acceleration
Microsoft DialoGPT-medium - a free, open-source conversational AI model
Convox for deployment, GPU scaling, and infrastructure management
Redis for caching responses and improving performance
NVIDIA GPUs for accelerated inference
Dedicated GPU Node Groups for cost-effective workload placement

No existing model required! This guide uses Microsoft's free DialoGPT model, which will be automatically downloaded during deployment. The final system will automatically scale GPU resources based on demand and can handle everything from a few queries per day to thousands of concurrent inference requests.

Prerequisites

Before we begin, you'll need:

A free Convox account
An AWS account with GPU instance access (we'll use g4dn or p3 instances)
Python 3.8+ for local testing (optional)
Docker installed locally (for local development)

About the Model

This guide uses Microsoft DialoGPT-medium, a free conversational AI model that:

✅ Requires no API keys or accounts - completely open source
✅ Downloads automatically during first deployment (~350MB)
✅ Runs efficiently on a single GPU
✅ Generates human-like conversational responses

Want to use a different model? Simply change the MODEL_NAME environment variable to any compatible Hugging Face model:

microsoft/DialoGPT-large (larger, more capable)
facebook/blenderbot-400M-distill (Facebook's conversational AI)
EleutherAI/gpt-neo-125M (GPT-style text generation)

Step 1: Set Up Your Convox Account and Runtime Integration

Create Your Convox Account

Sign up at console.convox.com/signup
Use your company email address and organization name
After logging in, you'll be prompted to create an Organization

Install Runtime Integration

In the Convox Console, click on Integrations
Click the + button in the Runtime section
Select AWS as your cloud provider
Follow the prompts to create the necessary IAM role in your AWS account
This integration allows Convox to manage GPU infrastructure in your AWS account securely

Step 2: Install a GPU-Enabled Production Rack

Navigate to Racks in the Convox Console
Click Install and select your AWS runtime integration
Configure your rack with basic GPU support:
- Name: ai-production
- Region: us-east-1 (or your preferred region with GPU availability)
- Template: Choose "Production" for optimized settings
- Advanced Parameters:
  - Set node_type=t3.medium (smaller standard nodes for non-GPU workloads)
  - Set build_node_type=c5.xlarge (larger CPU-optimized for Docker builds)
Click Install to begin rack creation (this takes 15-25 minutes)

Configure GPU Node Groups After Installation

Once your rack is installed, you'll need to configure dedicated GPU node groups and enable the NVIDIA device plugin. This ensures GPU workloads are isolated from standard services and have the necessary GPU drivers available.

Create a JSON file named gpu-nodes.json with the following configuration:

[
  {
    "id": 101,
    "type": "g4dn.xlarge",
    "capacity_type": "ON_DEMAND",
    "min_size": 0,
    "max_size": 5,
    "label": "gpu-inference",
    "dedicated": true,
    "tags": "workload=llm-inference,environment=production,cost-center=ai"
  },
  {
    "id": 102,
    "type": "g4dn.2xlarge",
    "capacity_type": "SPOT",
    "min_size": 0,
    "max_size": 3,
    "label": "gpu-inference-large",
    "dedicated": true,
    "tags": "workload=llm-inference-large,environment=production,cost-center=ai"
  }
]

This configuration creates:

Standard GPU nodes (g4dn.xlarge) for regular inference workloads
Large GPU nodes (g4dn.2xlarge) for high-performance inference using cost-effective spot instances
Scale-to-zero capability - GPU nodes only spin up when needed
Dedicated scheduling - prevents non-GPU services from consuming GPU resources
AWS tags for cost tracking and resource organization

Now apply both the NVIDIA device plugin and GPU node groups in a single command:

convox rack params set nvidia_device_plugin_enable=true additional_node_groups_config=./gpu-nodes.json -r ai-production

This command:

Enables the NVIDIA device plugin to expose GPU resources to Kubernetes
Creates dedicated GPU node groups with the specified configurations
Ensures proper isolation with the dedicated: true flag preventing non-GPU workloads from scheduling on expensive GPU nodes

While your rack configures the GPU infrastructure, let's build our LLM application.

Step 3: Get the LLM API Code

You can either clone our complete example repository or create the files manually.

Option A: Clone the Complete Example (Recommended)

git clone https://github.com/convox-examples/llm-gpu-api.git
cd llm-gpu-api

This repository contains all the files you need, plus additional examples and documentation.

Option B: Create Project Structure Manually

If you prefer to build from scratch:

mkdir llm-api
cd llm-api

Create the FastAPI Application

If you cloned the repository, you already have all these files. If you're building manually, create app.py:

import os
import json
import time
import logging
import hashlib
from typing import Optional
import torch
import redis
from transformers import (
    AutoTokenizer, 
    AutoModelForCausalLM, 
    BitsAndBytesConfig,
    pipeline
)
from fastapi import FastAPI, HTTPException, BackgroundTasks
from fastapi.responses import JSONResponse
from pydantic import BaseModel, Field
import uvicorn

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = FastAPI(
    title="LLM API", 
    version="1.0.0",
    description="Production-ready Large Language Model API with GPU acceleration"
)

class GenerationRequest(BaseModel):
    prompt: str = Field(..., min_length=1, max_length=2000)
    max_new_tokens: int = Field(default=100, ge=1, le=500)
    temperature: float = Field(default=0.7, ge=0.1, le=2.0)
    top_p: float = Field(default=0.9, ge=0.1, le=1.0)
    do_sample: bool = Field(default=True)
    stream: bool = Field(default=False)

class GenerationResponse(BaseModel):
    prompt: str
    generated_text: str
    processing_time: float
    device_used: str
    cached: bool
    tokens_generated: int

# Initialize Redis
redis_client = None
try:
    redis_url = os.getenv('CACHE_URL')
    if redis_url:
        redis_client = redis.from_url(redis_url, decode_responses=True)
        redis_client.ping()
        logger.info("Redis cache connected")
except Exception as e:
    logger.warning(f"Redis not available: {e}")

MODEL_NAME = os.getenv('MODEL_NAME', 'microsoft/DialoGPT-medium')
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
MAX_MEMORY_GB = int(os.getenv('MAX_MEMORY_GB', '12'))

tokenizer = None
model = None
text_generator = None

def initialize_model():
    """Initialize and load the LLM model"""
    global tokenizer, model, text_generator

    logger.info(f"Initializing model {MODEL_NAME} on {DEVICE}")

    if torch.cuda.is_available():
        gpu_name = torch.cuda.get_device_name(0)
        gpu_memory = torch.cuda.get_device_properties(0).total_memory / 1024**3
        logger.info(f"GPU: {gpu_name}, Memory: {gpu_memory:.1f}GB")

    try:
        tokenizer = AutoTokenizer.from_pretrained(
            MODEL_NAME,
            padding_side='left',
            cache_dir='/tmp/.transformers'
        )

        if tokenizer.pad_token is None:
            tokenizer.pad_token = tokenizer.eos_token

        model_kwargs = {
            "cache_dir": '/tmp/.transformers',
            "torch_dtype": torch.float16 if DEVICE == "cuda" else torch.float32,
        }

        if DEVICE == "cuda":
            model_kwargs["quantization_config"] = BitsAndBytesConfig(
                load_in_8bit=True,
                llm_int8_enable_fp32_cpu_offload=True
            )

        model = AutoModelForCausalLM.from_pretrained(
            MODEL_NAME,
            **model_kwargs
        )

        # Do NOT pass device= when using Accelerate or quantized models
        text_generator = pipeline(
            "text-generation",
            model=model,
            tokenizer=tokenizer,
            torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32
        )

        logger.info("Model initialized successfully")

    except Exception as e:
        logger.error(f"Model initialization failed: {e}")
        raise

def get_cache_key(prompt: str, params: dict) -> str:
    cache_data = f"{prompt}:{json.dumps(params, sort_keys=True)}"
    return f"llm:{hashlib.md5(cache_data.encode()).hexdigest()}"

def cache_response(key: str, response: dict, ttl: int = 3600):
    if redis_client:
        try:
            redis_client.setex(key, ttl, json.dumps(response))
        except Exception as e:
            logger.warning(f"Cache write failed: {e}")

def get_cached_response(key: str) -> Optional[dict]:
    if redis_client:
        try:
            cached = redis_client.get(key)
            if cached:
                return json.loads(cached)
        except Exception as e:
            logger.warning(f"Cache read failed: {e}")
    return None

@app.on_event("startup")
async def startup_event():
    initialize_model()

@app.get("/health")
async def health_check():
    health = {
        "status": "healthy",
        "model": MODEL_NAME,
        "device": DEVICE,
        "gpu_available": torch.cuda.is_available(),
        "model_loaded": model is not None,
        "cache_available": redis_client is not None
    }

    if torch.cuda.is_available():
        health.update({
            "gpu_name": torch.cuda.get_device_name(0),
            "gpu_memory_allocated": f"{torch.cuda.memory_allocated(0) / 1024**3:.2f}GB",
            "gpu_memory_reserved": f"{torch.cuda.memory_reserved(0) / 1024**3:.2f}GB"
        })

    return health

@app.get("/")
async def root():
    return {
        "message": "LLM API",
        "version": "1.0.0",
        "model": MODEL_NAME,
        "device": DEVICE,
        "endpoints": {
            "generate": "/generate",
            "health": "/health",
            "metrics": "/metrics"
        }
    }

@app.get("/metrics")
async def metrics():
    data = {
        "model": MODEL_NAME,
        "device": DEVICE,
        "requests_cached": 0
    }

    if torch.cuda.is_available():
        data.update({
            "gpu_memory_used": f"{torch.cuda.memory_allocated(0) / 1024**3:.2f}GB",
            "gpu_temperature": "N/A"
        })

    return data

@app.post("/generate", response_model=GenerationResponse)
async def generate_text(request: GenerationRequest, background_tasks: BackgroundTasks):
    start = time.time()

    if model is None or tokenizer is None:
        raise HTTPException(status_code=503, detail="Model not loaded")

    cache_params = {
        "max_new_tokens": request.max_new_tokens,
        "temperature": request.temperature,
        "top_p": request.top_p,
        "do_sample": request.do_sample
    }
    cache_key = get_cache_key(request.prompt, cache_params)

    cached = get_cached_response(cache_key)
    if cached:
        logger.info("Serving from cache")
        cached["processing_time"] = time.time() - start
        cached["cached"] = True
        return GenerationResponse(**cached)

    try:
        gen_args = {
            "max_new_tokens": request.max_new_tokens,
            "temperature": request.temperature,
            "top_p": request.top_p,
            "do_sample": request.do_sample,
            "pad_token_id": tokenizer.eos_token_id,
            "return_full_text": False
        }

        result = text_generator(request.prompt, **gen_args)[0]
        text = result["generated_text"]
        tokens = len(tokenizer.encode(text))

        response = {
            "prompt": request.prompt,
            "generated_text": text,
            "processing_time": time.time() - start,
            "device_used": DEVICE,
            "cached": False,
            "tokens_generated": tokens
        }

        background_tasks.add_task(cache_response, cache_key, response, 3600)
        logger.info(f"Generated {tokens} tokens in {response['processing_time']:.2f}s")
        return GenerationResponse(**response)

    except Exception as e:
        logger.error(f"Generation error: {e}")
        if "out of memory" in str(e).lower():
            raise HTTPException(status_code=507, detail="GPU memory insufficient")
        raise HTTPException(status_code=500, detail=f"Generation failed: {str(e)}")

if __name__ == "__main__":
    port = int(os.getenv("PORT", 8000))
    uvicorn.run(app, host="0.0.0.0", port=port)

Create Requirements File

Create requirements.txt:

fastapi==0.104.1
uvicorn[standard]==0.24.0
torch==2.1.0
transformers==4.35.2
accelerate==0.24.1
bitsandbytes==0.41.3
redis==5.0.1
python-multipart==0.0.6
pydantic==2.5.0
nvidia-ml-py3==7.352.0
scipy==1.10.1

Create the GPU-Optimized Dockerfile

Create Dockerfile:

FROM nvidia/cuda:11.8.0-devel-ubuntu20.04

# Prevent interactive prompts during installation
ENV DEBIAN_FRONTEND=noninteractive

# Set working directory
WORKDIR /app

# Install system dependencies
RUN apt-get update && apt-get install -y \
    python3 \
    python3-pip \
    python3-dev \
    curl \
    git \
    build-essential \
    && rm -rf /var/lib/apt/lists/*

# Create symbolic link for python
RUN ln -s /usr/bin/python3 /usr/bin/python

# Upgrade pip and build tools
RUN pip install --upgrade pip setuptools wheel

# Copy requirements first for better caching
COPY requirements.txt .

# Install Python dependencies
RUN pip install --no-cache-dir -r requirements.txt

# Copy application code
COPY . .

# Create cache directories
RUN mkdir -p /tmp/.transformers /tmp/.torch

# Set environment variables
ENV TRANSFORMERS_CACHE=/tmp/.transformers
ENV TORCH_HOME=/tmp/.torch
ENV CUDA_VISIBLE_DEVICES=0

# Expose port
EXPOSE 8000

# Health check with longer timeout for GPU models
HEALTHCHECK --interval=30s --timeout=60s --start-period=180s --retries=3 \
    CMD curl -f http://localhost:8000/health || exit 1

# Run the application
CMD ["python", "app.py"]

Step 4: Configure Convox Deployment with GPU Workload Placement

Create convox.yml:

environment:
  - PORT=8000
  - MODEL_NAME=microsoft/DialoGPT-medium
  - MAX_MEMORY_GB=12
  - CUDA_VISIBLE_DEVICES=0
  - TRANSFORMERS_CACHE=/tmp/.transformers
  - TORCH_HOME=/tmp/.torch

resources:
  cache:
    type: redis
  # For production systems that need persistent model storage
  # Uncomment and configure these resources:
  # database:
  #   type: postgres  # For training data, user interactions, model metadata
  # storage:
  #   type: s3  # For model checkpoints, datasets, fine-tuned models

services:
  api:
    build: .
    port: 8000
    health:
      path: /health
      grace: 180  # GPU models need more time to load
      timeout: 60
      interval: 30
    resources:
      - cache
    nodeSelectorLabels:
      convox.io/label: gpu-inference   # Target GPU nodes for inference workloads
    scale:
      count: 1-5
      cpu: 3500    # 3.5 CPU cores
      memory: 14336 # 14GB RAM
      gpu: 1       # 1 GPU per instance
      targets:
        cpu: 70
        memory: 75
    termination:
      grace: 90
    timeout: 600  # Longer timeout for LLM inference

This configuration includes several key workload placement features:

nodeSelectorLabels: Directs the API service to run specifically on nodes labeled gpu-inference
GPU resource requests: Ensures each instance gets dedicated GPU access
Proper resource allocation: CPU and memory sized appropriately for GPU workloads
Extended timeouts: Accounts for model loading and inference times

Step 5: Install the Convox CLI and Deploy

Now that our GPU infrastructure is configured and our application is ready, we need to install the Convox CLI and deploy the application.

Install the Convox CLI

Linux (x86_64/amd64):

curl -L https://github.com/convox/convox/releases/latest/download/convox-linux -o /tmp/convox
sudo mv /tmp/convox /usr/local/bin/convox
sudo chmod 755 /usr/local/bin/convox

macOS (Intel):

curl -L https://github.com/convox/convox/releases/latest/download/convox-macos -o /tmp/convox
sudo mv /tmp/convox /usr/local/bin/convox
sudo chmod 755 /usr/local/bin/convox

macOS (M1/ARM64):

curl -L https://github.com/convox/convox/releases/latest/download/convox-macos-arm64 -o /tmp/convox
sudo mv /tmp/convox /usr/local/bin/convox
sudo chmod 755 /usr/local/bin/convox

Login to Convox

Go to your Convox Console account page
Copy the CLI login command
Run it in your terminal:

convox login console.convox.com -t YOUR_API_KEY_HERE

Switch to Your GPU Rack

convox switch ai-production

Create and Deploy the Application

# Create the app
convox apps create llm-api

# Deploy the application
convox deploy

The deployment process will build your GPU-enabled Docker image using the dedicated build node, push it to Convox's registry, deploy to GPU-enabled nodes with the specified scaling configuration, set up load balancing and health checks optimized for LLM workloads, and automatically scale GPU instances based on demand.

Step 6: Test Your LLM API

Get the Application URL

convox services

You'll see output like:

SERVICE  DOMAIN                                           PORTS
api      api.llm-api.0a1b2c3d4e5f.convox.cloud           443:8000

Test the Health Check

curl https://api.llm-api.0a1b2c3d4e5f.convox.cloud/health

Test Text Generation

# Test basic text generation
curl -X POST \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "The future of artificial intelligence is",
    "max_new_tokens": 50,
    "temperature": 0.7
  }' \
  https://api.llm-api.0a1b2c3d4e5f.convox.cloud/generate

You should get a response like:

{
  "prompt": "The future of artificial intelligence is",
  "generated_text": "bright and full of possibilities. As we continue to advance machine learning...",
  "processing_time": 1.234,
  "device_used": "cuda",
  "cached": false,
  "tokens_generated": 45
}

Test with Various Parameters

# Test with higher creativity
curl -X POST \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "Write a short story about a robot:",
    "max_new_tokens": 100,
    "temperature": 1.2,
    "top_p": 0.95
  }' \
  https://api.llm-api.0a1b2c3d4e5f.convox.cloud/generate

Step 7: Monitor GPU Usage and Scaling

View Application Logs

convox logs

Monitor GPU Scaling

convox ps

You'll see your running processes. As inference load increases, Convox will automatically start additional GPU instances on the dedicated GPU node groups.

Check GPU Resource Usage

convox apps info

Verify GPU Node Targeting

# Check that your workloads are running on the correct nodes
convox rack kubeconfig > ~/.kube/convox-config
export KUBECONFIG=~/.kube/convox-config

# View which nodes your pods are running on
kubectl get pods -n ai-production-llm-api -o wide

Check Metrics Endpoint

curl https://api.llm-api.0a1b2c3d4e5f.convox.cloud/metrics

Scale Manually (if needed)

# Scale to a specific count
convox scale api --count=3

# Or update scaling parameters in convox.yml and redeploy
convox deploy

Step 8: Advanced GPU Optimization and Workload Management

Using Larger Models with Dedicated GPU Nodes

For more powerful models, you can create additional GPU node groups and target them specifically:

Create large-gpu-nodes.json:

[
  {
    "id": 103,
    "type": "p3.2xlarge",
    "capacity_type": "ON_DEMAND",
    "min_size": 0,
    "max_size": 2,
    "label": "gpu-inference-v100",
    "dedicated": true,
    "tags": "workload=llm-large-inference,gpu=v100,environment=production"
  }
]

Update your rack configuration:

convox rack params set additional_node_groups_config=/path/to/combined-gpu-nodes.json -r ai-production

Then update your convox.yml for larger models:

services:
  api-large:
    build: .
    port: 8000
    environment:
      - MODEL_NAME=microsoft/DialoGPT-large
      - MAX_MEMORY_GB=24
    scale:
      count: 1-2  # Fewer instances for larger models
      cpu: 8000   # More CPU for larger models
      memory: 32768 # 32GB RAM
      gpu: 1
    # Target V100 GPU nodes for large models
    nodeSelectorLabels:
      convox.io/label: gpu-inference-v100

Multi-GPU Support for Very Large Models

For models requiring multiple GPUs:

services:
  api-multi-gpu:
    build: .
    scale:
      gpu: 2  # Request 2 GPUs per instance
      count: 1-2  # Fewer instances due to resource requirements
      cpu: 8000
      memory: 32768
    nodeSelectorLabels:
      convox.io/label: gpu-inference-v100

Cost Optimization with Workload Placement

The workload placement strategy we've configured provides several cost benefits:

Scale-to-Zero GPU Nodes: GPU instances only run when needed
Mixed Instance Types: Use spot instances for cost savings where appropriate
Standard Nodes for Non-GPU Workloads: Keep your main cluster on cost-effective standard instances
Resource Right-Sizing: Match GPU instance types to model requirements
Dedicated Scheduling: Prevents expensive GPU resources from being consumed by non-GPU workloads

Monitor your costs using the AWS tags we configured:

# View cost allocation by workload
aws ce get-cost-and-usage \
  --time-period Start=2024-01-01,End=2024-01-31 \
  --group-by Type=DIMENSION,Key=SERVICE \
  --granularity MONTHLY \
  --metrics BlendedCost \
  --group-by Type=TAG,Key=workload

Step 9: Production Considerations

Data Persistence for Training and Fine-tuning

Current Setup: This guide focuses on inference-only deployment. The model is read-only and Redis only caches responses for performance.

For Production AI Systems That Learn Over Time:

1. Add Persistent Database Storage:

resources:
  database:
    type: postgres
    options:
      storage: 100
  cache:
    type: redis

2. Add Model Storage:

# Add to convox.yml
environment:
  - MODEL_STORAGE_PATH=s3://your-bucket/models/
  - TRAINING_DATA_PATH=s3://your-bucket/training-data/

3. Enable Volume Mounting for Model Persistence:

services:
  api:
    volumeOptions:
      - awsEfs:
          id: "model-storage"
          accessMode: ReadWriteMany
          mountPath: "/app/models"
    # Still target GPU nodes
    nodeSelectorLabels:
      convox.io/label: gpu-inference

Environment-Specific Configuration

Use different model configurations for different environments:

# Production - use larger, more capable model on V100 nodes
convox env set MODEL_NAME=microsoft/DialoGPT-large -r ai-production

# Staging - use smaller model on standard GPU nodes
convox env set MODEL_NAME=microsoft/DialoGPT-medium -r ai-staging

Monitoring and Alerts

Set up monitoring through the Convox Console:

Navigate to Monitoring in your app dashboard
Monitor cost allocation using the configured AWS tags

Advanced Configuration

Custom Model Loading

You can modify the app to support custom models while maintaining proper GPU node targeting:

# Add to app.py
CUSTOM_MODEL_PATH = os.getenv('CUSTOM_MODEL_PATH')
if CUSTOM_MODEL_PATH:
    MODEL_NAME = CUSTOM_MODEL_PATH
    logger.info(f"Loading custom model from {CUSTOM_MODEL_PATH}")

Batch Processing Support

For handling multiple requests efficiently:

@app.post("/generate_batch")
async def generate_batch(requests: List[GenerationRequest]):
    """Handle multiple generation requests in a single batch"""
    # Implementation for batched inference
    pass

Model Quantization Options

For memory-constrained deployments:

# Add different quantization options
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,  # Even more memory efficient
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,
)

Troubleshooting

Common GPU Issues

GPU Not Available:

# Check if NVIDIA plugin is enabled
convox rack params
# Should show nvidia_device_plugin_enable=true

Workloads Not Targeting GPU Nodes:

# Verify node labels exist
kubectl get nodes --show-labels | grep gpu-inference

# Check pod placement
kubectl get pods -n ai-production-llm-api -o wide

Out of Memory Errors:

Reduce max_new_tokens in requests
Enable quantization (already configured)
Scale down to fewer concurrent instances
Consider using larger GPU instances (p3.2xlarge, p3.8xlarge)

Slow Model Loading:

Increase health check grace period
Consider model caching strategies
Pre-warm models on container start

Performance Optimization

Cold Start Issues:

# Keep minimum instances running
convox scale api --count=2  # Always keep 2 instances warm

Cache Hit Rate:

# Monitor cache performance
curl https://api.llm-api.0a1b2c3d4e5f.convox.cloud/metrics

Node Group Scaling:

# Monitor node group scaling
kubectl get nodes -l convox.io/label=gpu-inference

Conclusion

You now have a production-ready GPU-accelerated LLM API that:

✅ Auto-scales with dedicated GPU resources based on demand
✅ Optimizes costs by using scale-to-zero GPU node groups with dedicated scheduling
✅ Handles variable inference loads efficiently
✅ Includes intelligent caching for improved performance
✅ Zero-downtime deployments for model updates
✅ Production monitoring and health checks
✅ Workload placement to prevent unnecessary GPU costs on standard workloads
✅ Enterprise-grade security and compliance

This foundation can serve as the basis for more advanced AI systems. You can extend it by supporting multiple model architectures, implementing streaming responses for real-time chat, adding fine-tuning capabilities, integrating with vector databases for RAG applications, supporting multi-modal inputs (text + images), and creating specialized node groups for different model types.

Next Steps

Core Enhancements:

Add Authentication: Implement API keys or OAuth for production use
Model Versioning: Deploy multiple model versions simultaneously with different node targeting
Streaming Support: Add real-time streaming responses
Monitoring: Integrate with monitoring tools like DataDog

Infrastructure Optimization:

CI/CD: Set up automated deployments with GitHub Actions
Multi-Region Deployment: Deploy across multiple regions with geo-routing
Advanced Workload Placement: Create node groups for different model types (small, medium, large)
Cost Monitoring: Set up detailed cost tracking and alerts

Advanced AI Features:

Persistent Storage: Add PostgreSQL and S3 for training data and model storage
Fine-tuning Pipeline: Implement model training and fine-tuning capabilities on dedicated training nodes
Data Collection: Store user interactions for continuous model improvement
A/B Testing: Deploy multiple model variants simultaneously using different node groups

For Advanced AI Platforms:

Training Job Orchestration: Use Convox to run distributed training jobs on specialized training node groups
Model Experiment Tracking: Implement MLOps pipelines for model versioning
Data Pipeline Management: Set up ETL processes for training data
Multi-Model Serving: Deploy different models on appropriately-sized GPU node groups

Want to deploy more advanced AI workloads? Explore our complete collection of deployment guides

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