How do I serve an open-source LLM in my cloud account?

Serving an open-source LLM in your cloud account usually means one of these setups:

1. Managed endpoint on a cloud AI service
2. Self-hosted inference server on a VM or Kubernetes
3. Serverless/container deployment for lighter usage

Here’s the practical path.

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1) Choose a model

Pick based on:

• Quality vs cost
• License
• Hardware needs

Common options:

• Llama 3.x
• Mistral / Mixtral
• Qwen2.5
• Phi
• Gemma

Check:

• Context length
• GPU memory requirements
• Commercial-use license terms

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2) Choose how you’ll serve it

Option A: Managed hosting

Best if you want minimal ops.

Providers:

• AWS Bedrock / SageMaker
• Azure AI Foundry / Azure ML
• Google Vertex AI
• Hugging Face Inference Endpoints
• Together / Fireworks / Replicate

Pros:

• Easy scaling
• Less infrastructure work
• Monitoring and autoscaling often included

Cons:

• More expensive
• Less control

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Option B: Self-host on a GPU VM

Best balance for many teams.

Typical stack:

• Cloud GPU instance: AWS g5/g6, Azure ND, GCP A2/A3, etc.
• Inference server:
  • vLLM
  • TGI (Text Generation Inference)
  • llama.cpp
  • TensorRT-LLM
• Container: Docker
• API layer: FastAPI / nginx / cloud load balancer

Pros:

• Full control
• Often cheaper at steady usage
• Easy to customize

Cons:

• You manage scaling, updates, reliability

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Option C: Kubernetes

Best if you already run Kubernetes.

Use:

• GPU node pool
• Model serving deployment
• Horizontal autoscaling
• Ingress + auth

Pros:

• Good for multi-service platforms
• Standard deployment patterns

Cons:

• More complex

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3) Typical deployment steps

A. Provision compute

Choose GPU size based on model:

• 7B model: often fits on 1× 16–24 GB GPU with quantization
• 13B–14B: usually 24–48 GB
• 70B: multiple GPUs or high-memory GPUs

If unsure, start with:

• 1 GPU VM
• Quantized model
• vLLM or TGI

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B. Download the model

Usually from Hugging Face or vendor registry.

You may need:

• Hugging Face token
• Access approval for gated models
• Storage volume for weights

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C. Run an inference server

Examples:

vLLM

python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --host 0.0.0.0 \
  --port 8000

This gives you an OpenAI-compatible API.

TGI

docker run --gpus all -p 8080:80 \
  -e HF_TOKEN=your_token \
  ghcr.io/huggingface/text-generation-inference:latest \
  --model-id meta-llama/Llama-3.1-8B-Instruct

llama.cpp

Good for CPU or small GPU deployments, especially quantized GGUF models.

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D. Put it behind an API gateway or load balancer

Add:

• Authentication
• Rate limiting
• TLS/HTTPS
• Logging
• Request limits
• Retry behavior

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E. Add monitoring

Track:

• GPU utilization
• Memory usage
• Request latency
• Tokens/sec
• Error rate
• Queue depth

Tools:

• Prometheus + Grafana
• Cloud-native monitoring
• OpenTelemetry

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4) Security considerations

Important if your model is exposed publicly or to internal users.

• Keep model server in a private subnet if possible
• Use IAM roles, not hardcoded secrets
• Restrict network access
• Validate prompt sizes to avoid abuse
• Add rate limiting and auth
• Log carefully; prompts may contain sensitive data
• Encrypt storage and traffic

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5) Cost-control tips

• Start with a smaller model
• Use quantization
• Use spot/preemptible GPUs if your workload allows it
• Scale down when idle
• Cache responses if applicable
• Use batching-enabled servers like vLLM/TGI
• Set max context and max output tokens

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6) A simple production architecture

A common setup:

Client → API Gateway → Load Balancer → GPU VM(s) running vLLM → Model weights on attached disk

For higher scale:

• Client → API Gateway → Kubernetes service → GPU pods
• Add autoscaling based on queue/latency/GPU utilization

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7) If you want the fastest path

If you want something working quickly:

1. Pick a model like Llama 3.1 8B Instruct or Mistral 7B
2. Provision a single GPU VM
3. Install Docker + NVIDIA runtime
4. Run vLLM or TGI
5. Put it behind a reverse proxy with HTTPS
6. Add auth and monitoring

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8) If you want, I can help with a concrete setup

I can give you a step-by-step deployment for any of these:

• AWS
• Azure
• GCP
• Kubernetes
• Docker on one GPU VM

If you tell me:

• your cloud provider
• model size
• expected traffic
• budget I can suggest the exact instance type and deployment commands.