- CPU Recommendations
- We recommend a CPU with the highest number of cores as possible. AMD Threadripper or Intel Server (Xeon) CPUs are fine.
- We recommend AMD Threadripper as you get a larger number of cores for parallelization compared to Intel.
- Threadripper also has a cost-per-core advantage and a greater number of PCIe lanes compared to the equivalent Intel part. PoH (Proof of History) is based on sha256 and Threadripper also supports sha256 hardware instructions.
- SSD size and I/O style (SATA vs NVMe/M.2) for a validator
- Minimum example - Samsung 860 Evo 2TB
- Mid-range example - Samsung 860 Evo 4TB
- High-end example - Samsung 860 Evo 4TB
- While a CPU-only node may be able to keep up with the initial idling network, once transaction throughput increases, GPUs will be necessary
- What kind of GPU?
- We recommend Nvidia Turing and volta family GPUs 1660ti to 2080ti series consumer GPU or Tesla series server GPUs.
- We do not currently support OpenCL and therefore do not support AMD GPUs. We have a bounty out for someone to port us to OpenCL. Interested? Check out our GitHub.
- Power Consumption
- Approximate power consumption for a validator node running an AMD Threadripper 3950x and 2x 2080Ti GPUs is 800-1000W.
Here are our recommendations for low, medium, and high end machine specifications:
|Low end||Medium end||High end||Notes|
|CPU||AMD Ryzen 3950x||AMD Threadripper 3960x||AMD Threadripper 3990x||Consider a 10Gb-capable motherboard with as many PCIe lanes and m.2 slots as possible.|
|Ledger Drive||Samsung 860 Evo 2TB||Samsung 860 Evo 4TB||Samsung 860 Evo 4TB||Or equivalent SSD|
|Accounts Drive(s)||None||Samsung 970 Pro 1TB||2x Samsung 970 Pro 1TB|
|GPU||Nvidia 1660ti||Nvidia 2080 Ti||2x Nvidia 2080 Ti||Any number of cuda-capable GPUs are supported on Linux platforms.|
Virtual machines on Cloud Platforms
While you can run a validator on a cloud computing platform, it may not be cost-efficient over the long term.
However, it may be convenient to run non-voting api nodes on VM instances for your own internal usage. This use case includes exchanges and services built on Solana.
In fact, the offical mainnet-beta API nodes are currently (Oct. 2020) run on GCE
n1-standard-32 (32 vCPUs, 120 GB memory) instances with 2048 GB SSD for
For other cloud platforms, select instance types with similar specs.
Also note that egress internet traffic usage may turn out to be high, especially for the case of running staked validators.
Running validator for live clusters (including mainnet-beta) inside Docker is not recommended and generally not supported. This is due to concerns of general docker's containerzation overhead and resultant performance degradation unless specially configured.
We use docker only for development purpose.
- We build and run on Ubuntu 18.04. Some users have had trouble when running on Ubuntu 16.04
- See Installing Solana for the current Solana software release.
Be sure to ensure that the machine used is not behind a residential NAT to avoid NAT traversal issues. A cloud-hosted machine works best. Ensure that IP ports 8000 through 10000 are not blocked for Internet inbound and outbound traffic. For more information on port forwarding with regards to residential networks, see this document.
Prebuilt binaries are available for Linux x86_64 (Ubuntu 18.04 recommended). MacOS or WSL users may build from source.
CUDA is required to make use of the GPU on your system. The provided Solana release binaries are built on Ubuntu 18.04 with CUDA Toolkit 10.1 update 1. If your machine is using a different CUDA version then you will need to rebuild from source.