LLM Inference Service: A Complete Production-Grade Solution for Large Language Model Inference Services

This project provides a complete production-grade LLM inference service architecture, enabling high-throughput real-time inference based on FastAPI + vLLM, and integrating Redis caching, Prometheus monitoring, and Kubernetes deployment solutions.

• Original Author/Maintainer: satishpolireddy
• Source Platform: GitHub
• Original Title: llm-inference-service
• Original Link: https://github.com/satishpolireddy/llm-inference-service
• Publication Date: 2026-05-23

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The service-oriented deployment of Large Language Models (LLMs) is one of the core challenges in current AI engineering. Many teams face the following difficulties when migrating LLMs from experimental environments to production environments:

• Performance Bottleneck: Insufficient inference throughput on single nodes, making it difficult to support high-concurrency scenarios
• Latency Sensitivity: Real-time applications require low-latency responses, which traditional batch processing methods cannot meet
• Lack of Observability: Absence of comprehensive monitoring and alerting mechanisms
• Difficulty in Scaling: Manual scaling is complex and cannot handle traffic fluctuations

This project is designed to address these issues, providing a proven production-grade LLM inference service architecture.

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The project uses FastAPI as the web framework, combined with Server-Sent Events (SSE) to achieve streaming output:

• Low-Latency First Token: Users can see the first response without waiting for full generation
• Progressive Output: Simulates a typewriter effect to enhance user experience
• Standard Protocol: Based on HTTP/1.1, with good compatibility and easy debugging

Compared to WebSocket, SSE is more suitable for LLM inference scenarios because it is based on standard HTTP and natively supports load balancing and proxy servers.

vLLM is one of the most advanced open-source LLM inference engines currently available, and this project fully leverages its features:

• PagedAttention: Significantly improves GPU utilization through fine-grained memory management
• Continuous Batching: Dynamically merges requests to maximize throughput
• Multi-Model Support: Supports mainstream model architectures such as Llama, Mistral, and Qwen

The project configuration is optimized for common GPU models (A100, H100, RTX 4090), providing out-of-the-box performance.

To reduce repeated computation overhead, the project implements an intelligent caching strategy:

• Prompt Caching: Directly returns cached results for identical inputs
• Embedding Caching: Semantic similarity matching, supporting approximate caching
• TTL Management: Automatic expiration policy to balance hit rate and memory usage

In typical dialogue scenarios, the cache hit rate can reach 30-50%, significantly reducing inference costs.

The project has built-in comprehensive observability support:

• Core Metrics: TTFT (Time to First Token), TPOT (Time per Token), throughput
• Business Metrics: Request success rate, cache hit rate, queue length
• Resource Metrics: GPU utilization, VRAM usage, temperature monitoring

All metrics are exposed via Prometheus and can be seamlessly integrated into Grafana for visualization.

The project provides complete Kubernetes deployment configurations:

• HPA Auto-Scaling: Automatically adjusts the number of replicas based on GPU utilization and queue length
• Node Affinity: Ensures pods are scheduled to nodes with GPUs
• Resource Quotas: Prevents a single service from exhausting cluster resources
• Rolling Updates: Zero-downtime deployment of new versions

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