# Design of Production-Grade Large Model Inference System: A Complete Guide from Architecture to Implementation

> Fastino Labs' open-source LLM inference system design document details how to build a production-grade inference platform supporting 5000 RPS with P95 latency below 2 seconds, covering core technologies such as multi-tenant isolation, cache-aware routing, and paged attention.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-08T15:09:34.000Z
- 最近活动: 2026-06-08T15:19:34.923Z
- 热度: 148.8
- 关键词: LLM推理, vLLM, PagedAttention, 生产系统设计, 多租户, GPU优化, 大模型部署
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-github-udaymanhas9-llm-inference-system-design
- Canonical: https://www.zingnex.cn/forum/thread/llm-github-udaymanhas9-llm-inference-system-design
- Markdown 来源: floors_fallback

---

## Introduction to the Production-Grade Large Model Inference System Design Guide

Fastino Labs' open-source LLM inference system design document details how to build a production-grade inference platform supporting 5000 RPS with P95 latency below 2 seconds. It covers core technologies like multi-tenant isolation, cache-aware routing, and paged attention, and provides a complete architecture design and runnable code skeleton to serve as a reference for building LLM inference services.

## Background: Engineering Challenges of Production-Grade LLM Inference

As LLMs move from labs to production, inference services need to meet requirements such as high RPS (thousands of requests per second), low latency, multi-tenant isolation, and maximum GPU resource utilization. Fastino Labs' document provides a complete engineering solution, including architecture design and Python skeleton code, to help build production-grade inference services from scratch.

## Core Assumptions and System Scale Estimation

The system's target scale is clear: the model is a 13B-parameter FP16 model (occupying 26GB of VRAM), using NVIDIA H100 80GB hardware; performance targets are 5000 RPS and P95 latency <2 seconds. Capacity estimation: KV cache per request is about 1.1GB, each H100 supports 45 concurrent requests, and 5000 RPS requires approximately 12 H100s.

## Layered Architecture: Entry, Inference, and Streaming Layers

The system adopts a three-layer architecture:
1. Entry layer: Includes L7 load balancing, tenant-aware routing (hash to reuse KV cache), token cost rate limiting (token-based leaky bucket), and admission queue (backpressure mechanism);
2. Inference layer: Based on vLLM, using paged attention (improves memory utilization to 80-90%), continuous batching (dynamically add/remove sequences), and chunked prefill scheduling (reduces tail latency for long prompts);
3. Streaming layer: Uses SSE protocol to push tokens in real time, reducing TTFT.

## Key Engineering Tradeoff Decisions

Several tradeoffs were made in the design:
- Cache affinity vs load balancing: Gain KV reuse benefits via hash routing;
- Pessimistic reservation vs precise billing: Simplify implementation with maximum token count reservation;
- Chunked prefill vs throughput: Sacrifice a small amount of throughput to improve tail latency.

## Implementation and Deployment Recommendations

The project provides a complete implementation skeleton (gateway, inference worker, scheduler, etc.), supports local demos on CPU/Apple Silicon, and allows switching to the vLLM engine. Deployment recommendations: Build a cluster with 12 H100s, orchestrate with Kubernetes or Docker Compose, and refer to docs/sizing.md for capacity calculation.

## Industry Insights and Summary

The document demonstrates a systematic engineering design methodology (goal → estimation → architecture → tradeoff → implementation), providing a reference architecture for LLM inference teams. Designs like multi-tenant isolation and cache routing can be directly applied. Large model inference requires a combination of engineering and algorithms, and such open-source documents are of great value to the industry.