sgl-project--sglang/docs/advanced_features/object_storage.md

Loading Models from Object Storage

SGLang supports direct loading of models from object storage (S3 and Google Cloud Storage) without requiring a full local download. This feature uses the runai_streamer load format to stream model weights directly from cloud storage, significantly reducing startup time and local storage requirements.

Overview

When loading models from object storage, SGLang uses a two-phase approach:

1. Metadata Download (once, before process launch): Configuration files and tokenizer files are downloaded to a local cache
2. Weight Streaming (lazy, during model loading): Model weights are streamed directly from object storage as needed

Supported Storage Backends

1. Amazon S3: s3://bucket-name/path/to/model/
2. Google Cloud Storage: gs://bucket-name/path/to/model/
3. Azure Blob: az://some-azure-container/path/
4. S3 compatible: s3://bucket-name/path/to/model/

Quick Start

Basic Usage

Simply provide an object storage URI as the model path:

# S3
python -m sglang.launch_server \
  --model-path s3://my-bucket/models/llama-3-8b/ \
  --load-format runai_streamer

# Google Cloud Storage
python -m sglang.launch_server \
  --model-path gs://my-bucket/models/llama-3-8b/ \
  --load-format runai_streamer

Note: The --load-format runai_streamer is automatically detected when using object storage URIs, so you can omit it:

python -m sglang.launch_server \
  --model-path s3://my-bucket/models/llama-3-8b/

With Tensor Parallelism

python -m sglang.launch_server \
  --model-path gs://my-bucket/models/llama-70b/ \
  --tp 4 \
  --model-loader-extra-config '{"distributed": true}'

Configuration

Load Format

The runai_streamer load format is specifically designed for object storage, ssd and shared file systems

python -m sglang.launch_server \
  --model-path s3://bucket/model/ \
  --load-format runai_streamer

Extended Configuration Parameters

Use --model-loader-extra-config to pass additional configuration as a JSON string:

python -m sglang.launch_server \
  --model-path s3://bucket/model/ \
  --model-loader-extra-config '{
    "distributed": true,
    "concurrency": 8,
    "memory_limit": 2147483648
  }'

Available Parameters

Parameter	Type	Description	Default
distributed	bool	Enable distributed streaming for multi-GPU setups. Automatically set to true for object storage paths and cuda alike devices.	Auto-detected
concurrency	int	Number of concurrent download streams. Higher values can improve throughput for large models.	4
memory_limit	int	Memory limit (in bytes) for the streaming buffer.	System-dependent

Performance Considerations

Distributed Streaming

For multi-GPU setups, enable distributed streaming to parallelize weight loading between the processes:

python -m sglang.launch_server \
  --model-path s3://bucket/model/ \
  --tp 8 \
  --model-loader-extra-config '{"distributed": true}'

Limitations

• Supported Formats: Currently only supports .safetensors weight format (recommended format)
• Supported Device: Distributed streaming is supported on cuda alike devices. Otherwise fallback to non distributed streaming

See Also

• Runai model streamer documentation