What should I use for serving fine-tuned models at scale?

For serving fine-tuned models at scale, the best choice depends on how much traffic you have, latency targets, model size, and whether you need batching, streaming, or multi-GPU support.

Good default choices

• vLLM: Best general-purpose choice for high-throughput, low-latency LLM serving.
  • Strong continuous batching
  • Great GPU utilization
  • Easy to expose as an OpenAI-compatible API
• Hugging Face TGI (Text Generation Inference): Also a solid production option.
  • Stable and widely used
  • Good for standard inference deployments
  • Supports batching and quantization workflows

If you need maximum scale/performance

• NVIDIA TensorRT-LLM: Best when you want to squeeze out maximum performance on NVIDIA GPUs.
  • More setup complexity
  • Best for optimized, large-scale deployments

If you want a managed platform

• AWS Bedrock / SageMaker
• Google Vertex AI
• Azure AI Foundry / Azure ML
• Databricks Model Serving These reduce ops burden, but can be more expensive and less flexible.

If your workload is smaller or simpler

• FastAPI + transformers or Flask + transformers can work for prototypes or low traffic, but usually won’t scale well for heavy concurrent LLM traffic.

Practical recommendation

If you’re serving a fine-tuned LLM and want a strong balance of ease + scale:

1. Start with vLLM
2. Use quantization if needed
3. Put it behind a load balancer / autoscaling group
4. Add request batching and monitoring

Rule of thumb

• Best overall: vLLM
• Best enterprise-managed: SageMaker / Vertex / Bedrock
• Best raw NVIDIA performance: TensorRT-LLM
• Best for classic inference stacks: TGI

If you tell me:

• model type and size
• expected requests/sec
• GPU type
• latency target
• whether you need OpenAI-compatible APIs

…I can recommend the best serving stack more precisely.