AI Inference Guide
Libraries & Frameworks
Inference Libraries & Frameworks
Essential tools and libraries for implementing AI inference in your applications. From low-level optimization libraries to high-level serving frameworks.
Key Features
CPU optimized
Multiple quantization
Cross-platform
Memory efficient
Language: C++
Key Features
Simple API
Model library
Docker support
REST API
Language: Go
Key Features
PagedAttention
Continuous batching
GPU acceleration
OpenAI compatible
Language: Python
Key Features
Continuous batching
Structured outputs
Compatible API option
Distributed serving
Language: Python/CUDA
Key Features
Compiled engines
In-flight batching
Quantization support
NVIDIA-specific
Language: C++/Python
Choosing the Right Library
For Local Development
- • Ollama - CLI-first local model workflow
- • llama.cpp - Low-level runtime and quantization control
- • LM Studio - GUI for beginners
For Production Serving
- • vLLM - High throughput, GPU optimization
- • SGLang - Structured generation and distributed serving
- • Provider APIs - Managed solutions
For Web Applications
- • WebLLM - Browser-based inference
- • BrowserAI - TypeScript support
- • Transformers.js - Hugging Face models