AiStack: Modular AI Inference Service Stack

Core Overview: AiStack is a modular AI inference service stack developed by lioilsources (hosted on GitHub: AiStack repo, updated in June 2026). It integrates LLM inference, image generation, and OCR capabilities via a modern architecture (Go gateway + FastAPI microservices + Docker Compose), supporting models like vLLM, FLUX, Qwen2.5-VL. Key value: one-stop privatized deployment with plug-and-play modularity, solving complex AI stack setup issues for enterprises and developers.

With the explosion of open-source large models, many enterprises and developers want to deploy AI services on their own infrastructure. However, building a complete AI inference stack is challenging—needing to handle model loading, service orchestration, API gateway, load balancing, multi-model collaboration, etc. AiStack addresses these pain points with a modular design, enabling flexible combination of AI capabilities.

AiStack uses a three-layer architecture:

1. Gateway Layer (Go): High concurrency (goroutine), low latency, and low resource usage. Responsibilities: request routing, load balancing, authentication/authorization, protocol conversion, monitoring.
2. Service Layer (FastAPI): Python-based, async support, type safety, high dev efficiency. Ideal for AI service development.
3. Deployment Layer (Docker Compose): One-click startup, environment isolation, version control, easy scaling.

AiStack supports three core AI functions:

• LLM Inference (vLLM): Uses PagedAttention for high GPU utilization. Features: multi-model concurrency, dynamic batch processing, streaming output, quantization support (GPTQ/AWQ).
• Image Generation: Combines FLUX (high-quality art images) and Qwen (Alibaba's multi-modal model) for diverse scenarios.
• OCR (Qwen2.5-VL): Multi-language support, layout understanding, handwriting recognition, scene text detection.

AiStack applies to multiple scenarios:

1. Enterprise Internal AI Platform: Privatized deployment ensures data security, cost control, and customization.
2. AI App Development: Developers can focus on business logic without building AI infrastructure from scratch.
3. Model Effect Validation: Gateway routing enables easy A/B testing of different models.
4. Edge Computing: Modular design allows resource-efficient deployment for edge environments.

Highlights:

• Unified RESTful API across all AI services, reducing client development cost.
• Declarative configuration for model management (no code changes needed).
• Comprehensive运维 support (Makefile, health checks, Cloudflare Tunnel integration).

Comparison:

• vs BentoML: AiStack has simpler architecture and lower learning curve.
• vs Triton Inference Server: AiStack is easier to configure and use.
• vs TGI: AiStack supports multi-modal (not just text generation). AiStack's advantages: out-of-box experience, mainstream tech stack (Go+Python+Docker), multi-modal support.

AiStack is a well-engineered, modular open-source project that provides an excellent starting point for privatized AI deployment. It balances functionality and simplicity, suitable for both current needs and future expansion.

Future:

• Integrate more AI capabilities (voice recognition, video understanding, code generation).
• Support Kubernetes deployment for large-scale production environments.

For teams needing quick AI infrastructure setup, AiStack is a recommended choice.