How does this help with streaming responses?

It provides standardized Server-Sent Events (SSE) implementation patterns for both FastAPI backends and React frontends to enable real-time token streaming.

Which serving engine should I use for self-hosted LLMs?

vLLM is the recommended primary choice for most production deployments due to its PagedAttention memory management, which significantly improves throughput.

Does this skill support local development without a GPU?

Yes, it includes patterns for Ollama, which is designed for local development and prototyping on standard laptop hardware.

Can I use this for traditional machine learning models?

Absolutely. The skill covers BentoML and Triton Inference Server for deploying scikit-learn, PyTorch, and XGBoost models.

Does it provide guidance on GPU hardware requirements?

Yes, it includes formulas for GPU memory estimation and strategies for quantization (like AWQ or INT8) to help fit models on available hardware.

Model Serving & LLM Deployment

Name: Model Serving & LLM Deployment
Author: ancoleman

byancoleman

•

158

데이터 과학 및 ML

Deploys and optimizes LLM and machine learning models for production-grade inference and API integration.

소개

Model Serving provides a comprehensive framework for deploying Large Language Models (LLMs) and traditional ML models using industry-standard engines like vLLM, TensorRT-LLM, and BentoML. It enables developers to implement high-throughput inference, optimized GPU utilization, and streaming response patterns while integrating with orchestration tools like LangChain and LlamaIndex for RAG pipelines. Whether you are hosting Llama models locally or scaling production APIs on Kubernetes, this skill offers the implementation patterns and best practices required for efficient, low-latency AI delivery.

주요 기능

Pre-configured patterns for SSE streaming response APIs
158 GitHub stars
Traditional ML deployment with BentoML and Triton
GPU memory optimization and quantization strategies
Support for high-throughput engines like vLLM and TensorRT-LLM
RAG orchestration with LangChain and LlamaIndex

사용 사례

Building real-time AI chat interfaces with streaming backend support
Self-hosting Llama, Mistral, or Qwen for private production applications
Optimizing GPU infrastructure costs through PagedAttention and quantization

소개

주요 기능

Pre-configured patterns for SSE streaming response APIs
158 GitHub stars
Traditional ML deployment with BentoML and Triton
GPU memory optimization and quantization strategies
Support for high-throughput engines like vLLM and TensorRT-LLM
RAG orchestration with LangChain and LlamaIndex

사용 사례

Building real-time AI chat interfaces with streaming backend support
Self-hosting Llama, Mistral, or Qwen for private production applications
Optimizing GPU infrastructure costs through PagedAttention and quantization