How do I run real-time inference on GPUs without managing everything myself?

If you want real-time GPU inference without running the infrastructure yourself, the easiest path is usually to use a managed inference service. These platforms handle deployment, autoscaling, GPU provisioning, health checks, and often rolling updates for you.

Common options

1) Managed model inference APIs

Best if you want to call a model over HTTP and not worry about servers.

• OpenAI / Anthropic / Google Gemini APIs for hosted models
• AWS SageMaker Real-Time Endpoints
• Azure Machine Learning Managed Online Endpoints
• Google Vertex AI Endpoints
• Hugging Face Inference Endpoints
• Replicate, Baseten, Fireworks.ai, Together.ai, Modal, RunPod Serverless

These are good when you need:

• low-latency responses
• GPU-backed serving
• autoscaling
• minimal DevOps

2) Managed GPU app platforms

Best if you need custom code around the model.

• Modal
• RunPod
• Baseten
• Fly.io Machines with GPUs in some setups
• Hugging Face Spaces for lighter use cases

These let you package your model in a container or function and deploy it with GPU access.

3) Serverless inference

Best for spiky traffic or bursty workloads.

• automatically scale to zero when idle
• pay only when used
• can add cold-start latency, so check if that’s acceptable for “real-time”

What to look for

For real-time inference, compare these features:

• Latency
• Cold starts
• Autoscaling speed
• GPU type and memory
• Concurrency support
• Streaming responses
• Private networking / VPC
• Container support
• Model size limits
• Pricing per second / per token / per request

Typical architecture

1. Train or fine-tune your model
2. Package it in a container or upload model weights
3. Deploy to a managed inference endpoint
4. Send requests from your app over HTTPS
5. Monitor logs, metrics, and autoscaling behavior

If you want the simplest setup

• For LLMs: use a hosted API from OpenAI, Anthropic, Gemini, or Together/Fireworks.
• For your own model: use Hugging Face Inference Endpoints, Baseten, or Modal.
• For enterprise cloud: use SageMaker, Vertex AI, or Azure Managed Online Endpoints.

Rule of thumb

• Need zero ops? Use a hosted model API.
• Need custom model + low maintenance? Use managed inference endpoints.
• Need maximum control and cheapest steady-state at scale? Self-hosting may eventually be better.

If you want, I can recommend the best platform based on:

• your model type
• expected traffic
• latency target
• budget
• whether you need open-source or proprietary models