openlake README

The project's README from github.com/openlake-project/openlake. As one markdown file: README.md.

OpenLake OpenLake

Fast, easy and efficient storage for LLM Inference and Training

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šŸ”„ Distributed storage for GPU workloads. Built on Rust on io_uring, OpenLake is a state of the art storage engine delivering million+ iops within 1ms.

Updates

Older updates
  • [2026/07] Introducing OpenLake: open source storage that saturates the GPUs: 8Ɨ throughput and 600 Āµs reads (blog).

Why OpenLake?

OpenLake is a storage engine for AI infrastructure. With OpenLake you get high throughput for small I/O and cache like performance while being fully persistent and durable. Keep GPUs fed during training and inference reducing idle time and getting more from your accelerators.

OpenLake is fast with:

Learn more: Blogs ā†’ | Benchmarks ā†’ | KV Offload | Object Store


66Ɨ speedup on time to first token first token when cached. (128K context window)

GPU nodes contribute to create an OpenLake cluster. The inference engine writes KV once and reads it back in milliseconds (using the host RAM and disk), saving prefill for long and repeated prompts.

Quickstart:

1) Setup KV Pool on GPU nodes:

Drop OpenLake into your existing setup. No code changes:

a. Install the connector and start the store:

pip install openlake-vllm
openlaked

b. Run vLLM with OpenLake enabled:

export PYTHONHASHSEED=0
vllm serve <model_name> --kv-transfer-config '{"kv_connector":"OpenLakeConnector","kv_connector_module_path":"openlake_client.openlake_connector","kv_role":"kv_both","kv_connector_extra_config":{"openlake_nodes":["127.0.0.1:9400"],"openlake_device":"local"}}'

Note: By default OpenLake offloads to the same host. To enable OpenLake across your GPU fleet, please start openlaked with a --config.

For Kubernetes clusters, use the Helm KV deployment guide to place one OpenLake instance on each selected node and generate the ordered vLLM peer configuration.

OpenLake enabled vs disabled:

openlake-video

OpenLake and vLLM serving Gemma4-31B on H100 (256K context window)

Multi host GPU Cluster (IB)

Run OpenLake on existing GPU cluster. Run OpenLake with (kv_rdma.toml) and node's self_id: (0, 1, 2...):

openlaked --config kv_rdma_0.toml   # gpu 1: ids = 0
openlaked --config kv_rdma_1.toml   # gpu 2: ids = 1

Point your vLLM workers at the unified cluster, in id order:

cat > /tmp/openlake-kv.json <<'EOF'
{
  "kv_connector": "OpenLakeConnector",
  "kv_connector_module_path": "openlake_client.openlake_connector",
  "kv_role": "kv_both",
  "kv_connector_extra_config": {
    "openlake_nodes": ["10.0.0.1:9400", "10.0.0.2:9400"],
    "openlake_device": "mlx5_ib0"
  }
}
EOF

export PYTHONHASHSEED=0
vllm serve <model_name> --kv-transfer-config "$(cat /tmp/openlake-kv.json)"

A prefix computed on one GPU host is served to any other from the shared pool.

2) PB scale object store for GPU Fleet

Build from source and have an S3 compatible store running in four steps. Install the dependencies and build the OpenLake binary locally.

a. Clone and build:

sudo apt-get install -y build-essential pkg-config clang cmake libhwloc-dev libudev-dev curl git awscli
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y && . "$HOME/.cargo/env"

git clone https://github.com/openlake-project/openlake.git && cd openlake
cargo build --release --bin openlaked

b. Start the store (single node, default config):

mkdir -p data/d0 data/d1 data/d2 data/d3
./target/release/openlaked --config crates/openlake_server/configs/storage-tcp-local.toml
Talk to it with any S3 client:
export AWS_ACCESS_KEY_ID=openlakeadmin
export AWS_SECRET_ACCESS_KEY=openlakeadmin
export AWS_DEFAULT_REGION=us-east-1

aws --endpoint-url http://127.0.0.1:9000 s3 mb s3://demo
aws --endpoint-url http://127.0.0.1:9000 s3 cp ./checkpoint.safetensors s3://demo/
aws --endpoint-url http://127.0.0.1:9000 s3 ls s3://demo/

Both ListObjects versions support encoding-type=url for UTF-8 percent-encoded keys, prefixes, and delimiters in the XML response. Without this option, key names retain their existing XML representation. ListObjectsV2 now echoes StartAfter and returns XML-safe, versioned continuation tokens; treat tokens as opaque and pass them back unchanged, even when changing the encoding option. Legacy raw-key tokens remain accepted except for the reserved openlake-list-v1: token prefix (restart those listings after upgrading).

Build from source:

To build from the source, please follow the platform specific build guides:

Ubuntu / Debian

Produce binaries for your deployment or test code changes.

# Install system dependencies
sudo apt-get update
sudo apt-get install -y --no-install-recommends ca-certificates build-essential pkg-config clang cmake libhwloc-dev libudev-dev curl git

# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y && . "$HOME/.cargo/env"

# Clone and build OpenLake
git clone https://github.com/openlake-project/openlake.git
cd openlake && cargo build --release --locked -p openlake_server --bin openlaked

# Create the local storage directories
mkdir -p data/d0 data/d1 data/d2 data/d3

# Start OpenLake in TCP mode. Please switch the config path for RDMA.
RUST_LOG=info ./target/release/openlaked --config crates/openlake_server/configs/storage-tcp-local.toml
macOS (development)

Install Homebrew first if brew is unavailable.

# Install system dependencies
xcode-select --install
brew install cmake pkg-config

# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y && . "$HOME/.cargo/env"

# Clone and build OpenLake
git clone https://github.com/openlake-project/openlake.git
cd openlake && cargo build --release --locked -p openlake_server --bin openlaked

# Create local storage directories
mkdir -p data/d0 data/d1 data/d2 data/d3

# Start OpenLake in TCP mode. (macOS does not provide the Linux RDMA interfaces)
RUST_LOG=info ./target/release/openlaked --config crates/openlake_server/configs/storage-tcp-local.toml
Windows (WSL2)

Build OpenLake with WSL2. See the full Windows development environment guide.


# Run in an administrator PowerShell terminal:
wsl --install -d Ubuntu
wsl

# Then run inside the Ubuntu shell:
# Install system dependencies
sudo apt-get update
sudo apt-get install -y --no-install-recommends ca-certificates build-essential pkg-config clang cmake libhwloc-dev libudev-dev curl git

# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y && . "$HOME/.cargo/env"

# Clone and build OpenLake
git clone https://github.com/openlake-project/openlake.git
cd openlake && cargo build --release --locked -p openlake_server --bin openlaked

# Create local storage directories
mkdir -p data/d0 data/d1 data/d2 data/d3

# Start OpenLake in TCP mode
RUST_LOG=info ./target/release/openlaked --config crates/openlake_server/configs/storage-tcp-local.toml

If you would like support for a different build system, please open an issue on GitHub.

Architecture

OpenLake keeps the path from storage to GPU memory short, predictable, and low latency.

Explore the architecture and user docs, inspect the included configurations, or read the OpenLake engineering blog for deeper technical discussions.

Contributing

We welcome and value any contributions and collaborations. Please check out Contributing to OpenLake for how to get involved.

Get in touch

License

Apache 2.0.

openlake (openlake-project/openlake)

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Openlake

OpenLake is a high performance storage engine for efficient LLM inference and GPU Training

Creator
openlake-project
Size
4.6 MB
Stars
2.8K
Updated
CommitNov 04Releasev0.11.0Nov 04
Age
6mo
Commits Ā· 90 Days