# Production-Grade LLM Inference Platform: A Complete Kubernetes-Based Deployment Solution

> This article details an open-source production-grade LLM inference platform built on Kubernetes, integrating FastAPI, Ollama, HPA auto-scaling, and Prometheus/Grafana monitoring systems, while comparing and testing the performance of three scaling strategies.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-01T22:14:00.000Z
- 最近活动: 2026-05-02T01:28:34.327Z
- 热度: 154.8
- 关键词: 大语言模型, Kubernetes, 自动扩缩容, Ollama, FastAPI, 生产部署, GPU推理
- 页面链接: https://www.zingnex.cn/en/forum/thread/kubernetes
- Canonical: https://www.zingnex.cn/forum/thread/kubernetes
- Markdown 来源: floors_fallback

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## [Overview] Production-Grade LLM Inference Platform: A Complete Kubernetes-Based Deployment Solution

This article introduces an open-source production-grade LLM inference platform built on Kubernetes, integrating FastAPI, Ollama, HPA auto-scaling, and Prometheus/Grafana monitoring systems, while comparing and testing the performance of three scaling strategies. This platform addresses the engineering challenges of LLM production deployment and provides a complete cloud-native solution.

## Background: Engineering Challenges of LLM Inference and Cloud-Native Solutions

As the scale of large language models grows, production deployment faces challenges such as model loading, request scheduling, resource management, and performance monitoring. Traditional monolithic deployment cannot meet the needs of high availability, elastic scaling, and observability. Kubernetes-based cloud-native deployment has become an industry consensus, and the open-source project in this article builds a production-grade LLM inference platform based on this technology stack.

## Methodology: Overall Platform Architecture Design

The platform adopts a modular microservice architecture with core components including:
- **API Gateway Layer**: Based on FastAPI, responsible for request reception, validation, routing, and result encapsulation. It handles high concurrency asynchronously and automatically generates OpenAPI documentation.
- **Model Inference Layer**: Uses Ollama as the inference engine, unifying the abstraction of multiple open-source models and supporting containerized independent scaling and updates.
- **Auto-Scaling Layer**: Leverages Kubernetes HPA to adjust the number of Pods based on CPU/memory/custom metrics.
- **Observability Layer**: Integrates Prometheus and Grafana to monitor key metrics such as request latency and GPU utilization in real time.

## Evidence: Comparative Test Results of Three Scaling Strategies

The project tested three scaling strategies:
1. **Classic HPA based on CPU utilization**: Simple and intuitive, but not sensitive to GPU-intensive tasks.
2. **Custom queue depth-based**: Focuses on request queue length, performs best in burst traffic scenarios, and responds quickly to load changes.
3. **Hybrid strategy based on inference latency**: Combines latency and throughput, stable in gradual growth scenarios, and avoids resource waste.
Locust was used to simulate burst, gradual growth, and periodic fluctuation traffic. The results show that the queue depth strategy is suitable for burst traffic, the hybrid strategy for gradual growth, and the CPU strategy is not suitable for pure inference loads.

## Adaptation: Infrastructure Optimization for NVIDIA AI Factory

The platform is optimized for NVIDIA AI Factory:
- **Hardware**: Utilizes NVIDIA GPU computing power, supports multi-GPU parallel inference and model sharding, and integrates TensorRT and Triton to improve performance.
- **Network**: Supports RoCE and GPUDirect technologies to reduce data transmission latency.
- **Software**: Deeply integrated with the NVIDIA container toolchain, dynamically allocates and isolates GPU resources, ensuring multi-tenant fairness and security.

## Practice: Deployment and Operation Guide

Deployment supports multiple modes: single-node Docker Compose for development and testing, and Kubernetes Helm Chart for production. Operations include built-in health checks, graceful shutdown, and rolling update mechanisms; centralized log collection facilitates troubleshooting; and Grafana log queries are used to quickly locate issues.

## Scenarios: Application Scenarios and Extensibility

The platform is suitable for scenarios such as intelligent customer service (high-concurrency conversations), content generation (batch text creation), and code assistance (real-time programming suggestions). The modular design supports component replacement (e.g., vLLM replacing Ollama), integration with vector databases (RAG applications), and also supports multi-model deployment and A/B testing.

## Conclusion: Project Value and Future Outlook

This open-source project provides a reference implementation of a production-grade LLM inference platform, covering key aspects such as architecture, performance optimization, monitoring, and operations. The comparative testing of three scaling strategies provides empirical data for the industry. For production teams, it is both a usable solution and a resource for learning best practices in cloud-native AI infrastructure. As LLM applications expand, such solutions will become more important.