serverless GPU model inference

If you mean running GPU-backed model inference without managing servers, the common term is:

Serverless GPU inference

What it means

You deploy a model endpoint, and the platform:

• automatically provisions GPU instances
• scales up/down with traffic
• may scale to zero when idle
• handles most infrastructure ops for you

Typical use cases

• LLM/chatbot inference
• image generation
• speech models
• batch inference jobs
• event-driven ML workloads

Popular options

• AWS SageMaker Serverless Inference
  Note: serverless inference is typically CPU-oriented; GPU support is more limited and often uses real-time endpoints instead.
• Modal
• Runpod Serverless
• Replicate
• Beam
• Hugging Face Inference Endpoints
• Baseten
• Google Cloud Run with GPU in some configurations
• Azure Container Apps / Azure ML depending on setup

Important caveats

“Serverless” on GPU often still has tradeoffs:

• cold starts can be noticeable
• cost may be higher than reserved GPU instances at steady load
• memory/model size limits apply
• concurrency and autoscaling behavior vary by platform

If you’re choosing a platform, consider

• latency requirements
• model size
• traffic pattern
• need for scale-to-zero
• supported frameworks (PyTorch, TensorRT, vLLM, TGI, etc.)
• pricing per second/request/token
• whether you need batching or streaming

If you want, I can also give you:

1. a list of best serverless GPU platforms,
2. a decision guide, or
3. an example deployment for PyTorch/Transformers/vLLM.