# Hardware requirements The hardware requirements for Boost are tied to the Lotus stack in a Filecoin SP deployment. Depending on how much data you need to onboard, and how many deals you need to make with clients, hardware requirements in terms of *CPU* and *Disk* will vary. ## General hardware requirements ### CPU A miner will need an **8+ core CPU**. We strongly recommend a CPU model with support for *Intel SHA Extensions*: AMD since Zen microarchitecture, or Intel since Ice Lake. Lack of SHA Extensions results in a very significant slow down. The most significant computation that Boost has to do is the *Piece CID calculation (also known as Piece Commitment or CommP)*. When Boost receives data from a client, it calculates the *Merkle root* out of the hashes of the *Piece* (padded .car file). The resulting root of the clean binary Merkle tree is the *Piece CID.* ### RAM **128 GiB** of RAM is needed at the very least. ### Disk Boost stores all data received from clients before Piece CID is calculated and compared against deal parameters received from clients. Next, deals are published on-chain, and Boost waits for a number of epoch confirmations before proceeding to pass data to the Lotus sealing subsystem. This means that depending on the throughput of your operation, you must have disk space for at least a few staged sectors. For small deployments **100 GiB of disk are needed at the very least** if we assume that Boost is to keep three 32 GiB sectors before passing them to the sealing subsystem. We recommend using **NVME disk** for Boost. As Dagstore grows in size, the overall performance might slow down due to slow disk. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://boost.filecoin.io/hardware-requirements.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.