Ray Serve vs AirLLM
A side-by-side comparison of features, pricing, and characteristics.
Ray Serve
Ray Serve is a scalable model serving library built on the Ray distributed computing framework.
Read the full reviewAirLLM
Open-source Python library that runs very large language models on low-memory GPUs by streaming model layers one at a time.
Read the full review| Attribute | Ray Serve | AirLLM |
|---|---|---|
| Pricing type | Free | Free |
| Korean support | No | No |
| Platforms | Web, API, CLI | Linux, macOS, CUDA-enabled NVIDIA GPUs, Apple Silicon |
| Open source | Yes | Yes |
| API available | Available | - |
| SDK | Available | - |
| LLM-based | - | - |
| Multimodal | Yes | - |
| AI model | - | Llama, Qwen, DeepSeek, Mistral, Mixtral, Phi, Gemma |
| GitHub Stars | 42.6K | 33.8K |
| Vendor | Anyscale | Anima AI LLC |
| Category | Developer Tools | Developer Tools |
| Details | View | View |
Ray Serve key features
- Framework Agnostic
- Python-first Configuration
- Dynamic Autoscaling
- Complex Pipeline Composition
- Distributed Resource Management
- HTTP & gRPC Support
AirLLM key features
- Reducing GPU memory usage through layer-wise model streaming
- Supporting inference of 70B-class models on a single 4GB GPU
- AutoModel interface based on Hugging Face model IDs
- 4-bit and 8-bit block-wise model compression
- Supporting CPU inference and Apple Silicon macOS
- Supporting various model families including Llama, Qwen, DeepSeek, and Mistral