Need GPU model hosting with autoscaling and batching

If you want GPU model hosting with autoscaling and batching, the best fit depends on whether you want managed inference, Kubernetes control, or simple API deployment.

Good options

1. NVIDIA Triton Inference Server

Best for:

• high-throughput GPU inference
• dynamic batching
• multiple model frameworks
• production serving

Pros:

• built-in batching and concurrency
• excellent GPU utilization
• supports TensorRT, PyTorch, ONNX, TensorFlow
• integrates well with Kubernetes autoscaling

Cons:

• more operational overhead
• you manage infra or use it with a platform

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2. Ray Serve

Best for:

• Python-native model serving
• flexible autoscaling
• request routing and batching patterns

Pros:

• easy to build custom serving logic
• autoscaling support
• works well for LLMs and multi-stage pipelines

Cons:

• batching is not as “plug-and-play” as Triton
• you still need to operate Ray cluster or use managed Ray

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3. KServe on Kubernetes

Best for:

• Kubernetes-based production deployments
• autoscaling GPU workloads
• standardized model serving

Pros:

• can autoscale based on traffic
• supports inference graphs, canary deploys
• works with Triton, vLLM, and custom containers

Cons:

• Kubernetes complexity
• batching usually depends on the underlying server

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4. vLLM

Best for:

• LLM serving specifically
• high-throughput token generation
• continuous batching

Pros:

• excellent for LLMs
• very efficient GPU utilization
• popular for chat/completion APIs

Cons:

• mostly focused on LLMs
• autoscaling handled externally

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5. Managed platforms

If you want less ops:

• AWS SageMaker
• Google Vertex AI
• Azure ML
• Modal
• Replicate
• Baseten
• Fireworks
• Together AI

Pros:

• easier setup
• autoscaling often built in
• some support batching or efficient LLM serving

Cons:

• less control
• cost can be higher
• batching capabilities vary

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Best combinations by use case

If you want maximum performance

• Triton + Kubernetes + KEDA/HPA

If you’re serving LLMs

• vLLM + KServe or autoscaled container platform
• or managed LLM hosting like Together, Fireworks, Baseten

If you want fastest production setup with low ops

• Managed inference platform
• e.g. Baseten, Vertex AI, SageMaker, Modal

If you want custom Python logic

• Ray Serve or KServe custom container

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Key features to look for

Make sure the platform supports:

• GPU autoscaling
• dynamic batching / continuous batching
• cold start handling
• concurrency control
• multi-GPU support if needed
• observability: latency, queue time, GPU utilization
• model versioning and rollout

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My recommendation

• For LLMs: vLLM or Triton, with autoscaling via Kubernetes/KServe or a managed platform.
• For general ML inference: Triton is often the strongest choice.
• For minimum ops: use a managed GPU inference platform.

If you tell me:

1. model type (LLM, CV, embedding, etc.)
2. traffic pattern
3. cloud preference
4. latency vs cost priority

…I can recommend the best exact stack.