How does this skill help reduce AI deployment costs?

It provides patterns for quantization (reducing memory footprint) and PagedAttention (maximizing throughput), allowing you to serve more users on less expensive hardware.

What frameworks are supported by the LLM Serving Patterns skill?

This skill provides guidance on a wide range of frameworks including vLLM, Text Generation Inference (TGI), TensorRT-LLM, Triton Inference Server, Ollama, and llama.cpp.

Can this skill help with latency issues?

Absolutely. It covers low-latency optimization techniques such as speculative decoding, continuous batching, and hardware-specific optimizations for NVIDIA GPUs.

Does it cover streaming for real-time chat?

Yes, it includes detailed patterns for implementing both Server-Sent Events (SSE) and WebSocket streaming for interactive LLM applications.

LLM Serving Patterns

Name: LLM Serving Patterns
Author: melodic-software

bymelodic-software

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Data Science & ML

Optimizes LLM inference infrastructure and deployment strategies using industry-standard frameworks and architectural patterns.

About

The LLM Serving Patterns skill provides comprehensive guidance on designing, deploying, and scaling Large Language Model (LLM) serving infrastructure. It helps developers navigate critical technical decisions including framework selection among vLLM, TGI, and TensorRT-LLM, while offering deep dives into quantization techniques like AWQ and GPTQ. By implementing advanced patterns such as continuous batching, PagedAttention for KV cache management, and speculative decoding, this skill enables the creation of high-throughput, low-latency AI services capable of handling production-grade workloads efficiently.

Key Features

Framework benchmarking and selection (vLLM, TGI, TensorRT-LLM)
Advanced quantization strategies including INT4, INT8, AWQ, and GPTQ
Continuous batching and throughput optimization patterns
Streaming response implementation using SSE and WebSockets
12 GitHub stars
Memory management techniques like PagedAttention and KV caching

Use Cases

Reducing GPU memory requirements for model deployment via quantization
Optimizing real-time chat applications for minimal time-to-first-token (TTFT)
Architecting a high-concurrency inference backend for enterprise AI agents

About

Key Features

Framework benchmarking and selection (vLLM, TGI, TensorRT-LLM)
Advanced quantization strategies including INT4, INT8, AWQ, and GPTQ
Continuous batching and throughput optimization patterns
Streaming response implementation using SSE and WebSockets
12 GitHub stars
Memory management techniques like PagedAttention and KV caching

Use Cases

Reducing GPU memory requirements for model deployment via quantization
Optimizing real-time chat applications for minimal time-to-first-token (TTFT)
Architecting a high-concurrency inference backend for enterprise AI agents