Load models 10x faster. Serve 10 models with 1 GPU.

Docs • Quick Start • OSDI'24 Paper

ServerlessLLM loads models 6-10x faster than SafeTensors, enabling true serverless deployment where multiple models efficiently share GPU resources.

Results obtained on NVIDIA H100 GPUs with NVMe SSD. "Random" simulates serverless multi-model serving; "Cached" shows repeated loading of the same model.

ServerlessLLM is a fast, low-cost system for deploying multiple AI models on shared GPUs, with three core innovations:

1. ⚡ Ultra-Fast Checkpoint Loading: Custom storage format with O_DIRECT I/O loads models 6-10x faster than state-of-the-art checkpoint loaders
2. 🔄 GPU Multiplexing: Multiple models share GPUs with fast switching and intelligent scheduling
3. 🎯 Unified Inference + Fine-Tuning: Seamlessly integrates LLM serving with LoRA fine-tuning on shared resources

Result: Serve 10 models on 1 GPU, fine-tune on-demand, and serve a base model + 100s of LoRA adapters.

Don't have Docker? Jump to Use the Fast Loader in Your Code for a Docker-free example.

# Download the docker-compose.yml file
curl -O https://raw.githubusercontent.com/ServerlessLLM/ServerlessLLM/main/examples/docker/docker-compose.yml

# Set model storage location
export MODEL_FOLDER=/path/to/models

# Launch cluster (head node + worker with GPU)
docker compose up -d

# Wait for the cluster to be ready
docker logs -f sllm_head

docker exec sllm_head /opt/conda/envs/head/bin/sllm deploy --model Qwen/Qwen3-0.6B --backend transformers

curl http://127.0.0.1:8343/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen3-0.6B",
    "messages": [{"role": "user", "content": "What is ServerlessLLM?"}],
    "temperature": 0.7
  }'

That's it! Your model is now serving requests with an OpenAI-compatible API.

Use ServerlessLLM Store standalone to speed up torch-based model loading.

pip install serverless-llm-store

sllm-store save --model Qwen/Qwen3-0.6B --backend transformers

# Start the store server first
sllm-store start --storage-path ~/models --mem-pool-size 4GB

from sllm_store.transformers import load_model

# Load model (6-10x faster than from_pretrained!)
model = load_model(
    "Qwen/Qwen3-0.6B",
    device_map="auto",
    torch_dtype="float16"
)

# Use as a normal PyTorch/Transformers model
output = model.generate(**inputs)

How it works:

• Custom binary format optimized for sequential reads
• O_DIRECT I/O bypassing OS page cache
• Pinned memory pool for DMA-accelerated GPU transfers
• Parallel multi-threaded loading

• 6-10x faster than the SafeTensors checkpoint loader
• Supports both NVIDIA and AMD GPUs
• Works with vLLM, Transformers, and custom models

📖 Docs: Fast Loading Guide | ROCm Guide

• Run 10+ models on 1 GPU with fast switching
• Storage-aware scheduling minimizes loading time
• Auto-scale instances per model (scale to zero when idle)
• Live migration for zero-downtime resource optimization

📖 Docs: Deployment Guide

• Integrates LLM serving with serverless LoRA fine-tuning
• Deploys fine-tuned adapters for inference on-demand
• Serves a base model + 100s of LoRA adapters efficiently

📖 Docs: Fine-Tuning Guide

• Deploy embedding models alongside LLMs
• Provides an OpenAI-compatible /v1/embeddings endpoint

💡 Example: RAG Example

• OpenAI-compatible API (drop-in replacement)
• Docker and Kubernetes deployment
• Multi-node clusters with distributed scheduling

📖 Docs: Deployment Guide | API Reference

• NVIDIA GPUs: Compute capability 7.0+ (V100, A100, H100, RTX 3060+)
• AMD GPUs: ROCm 6.2+ (MI100, MI200 series) - Experimental

More Examples: ./examples/

• Discord: Join our community - Get help, share ideas
• GitHub Issues: Report bugs
• WeChat: QR Code - 中文支持
• Contributing: See CONTRIBUTING.md

Maintained by 10+ contributors worldwide. Community contributions are welcome!

If you use ServerlessLLM in your research, please cite our OSDI'24 paper:

@inproceedings{fu2024serverlessllm,
  title={ServerlessLLM: Low-Latency Serverless Inference for Large Language Models},
  author={Fu, Yao and Xue, Leyang and Huang, Yeqi and Brabete, Andrei-Octavian and Ustiugov, Dmitrii and Patel, Yuvraj and Mai, Luo},
  booktitle={OSDI'24},
  year={2024}
}

Apache 2.0 - See LICENSE

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