You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
fuwa 89b7c02bba
rx-wow-fix-3: Revert "Increase the frequency of CBRANCH (#118)"
2 years ago
audits Added info about audits 2 years ago
doc Fixed an incorrect URL the the documentation 2 years ago
src rx-wow-fix-3: Revert "Increase the frequency of CBRANCH (#118)" 11 months ago
vcxproj Update dll project 1 year ago
.gitattributes Add .gitattributes 2 years ago
.gitignore Use cmake for building (#90) 2 years ago
CMakeLists.txt Merge pull request #160 from tevador/pr-install1 2 years ago
LICENSE Relicensed under the 3-clause BSD license 2 years ago Add a note about building portable binaries 2 years ago
randomx.sln Regression tests (#73) 2 years ago


RandomX is a proof-of-work (PoW) algorithm that is optimized for general-purpose CPUs. RandomX uses random code execution (hence the name) together with several memory-hard techniques to minimize the efficiency advantage of specialized hardware.


RandomX utilizes a virtual machine that executes programs in a special instruction set that consists of integer math, floating point math and branches. These programs can be translated into the CPU's native machine code on the fly (example: program.asm). At the end, the outputs of the executed programs are consolidated into a 256-bit result using a cryptographic hashing function (Blake2b).

RandomX can operate in two main modes with different memory requirements:

  • Fast mode - requires 2080 MiB of shared memory.
  • Light mode - requires only 256 MiB of shared memory, but runs significantly slower

Both modes are interchangeable as they give the same results. The fast mode is suitable for "mining", while the light mode is expected to be used only for proof verification.


Full specification is available in

Design description and analysis is available in


Between May and August 2019, RandomX was audited by 4 independent security research teams:

The first audit was generously funded by Arweave, one of the early adopters of RandomX. The remaining three audits were funded by donations from the Monero community. All four audits were coordinated by OSTIF.

Final reports from all four audits are available in the audits directory. None of the audits found any critical vulnerabilities, but several changes in the algorithm and the code were made as a direct result of the audits. More details can be found in the final report by OSTIF.


RandomX is written in C++11 and builds a static library with a C API provided by header file randomx.h. Minimal API usage example is provided in api-example1.c. The reference code includes a randomx-benchmark and randomx-tests executables for testing.


Build dependencies: cmake (minimum 2.8.7) and gcc (minimum version 4.8, but version 7+ is recommended).

To build optimized binaries for your machine, run:

git clone
cd RandomX
mkdir build && cd build
cmake -DARCH=native ..

To build portable binaries, omit the ARCH option when executing cmake.


On Windows, it is possible to build using MinGW (same procedure as on Linux) or using Visual Studio (solution file is provided).

Precompiled binaries

Precompiled randomx-benchmark binaries are available on the Releases page.

Proof of work

RandomX was primarily designed as a PoW algorithm for Monero. The recommended usage is following:

  • The key K is selected to be the hash of a block in the blockchain - this block is called the 'key block'. For optimal mining and verification performance, the key should change every 2048 blocks (~2.8 days) and there should be a delay of 64 blocks (~2 hours) between the key block and the change of the key K. This can be achieved by changing the key when blockHeight % 2048 == 64 and selecting key block such that keyBlockHeight % 2048 == 0.
  • The input H is the standard hashing blob with a selected nonce value.

RandomX was successfully activated on the Monero network on the 30th November 2019.

If you wish to use RandomX as a PoW algorithm for your cryptocurrency, please follow the configuration guidelines.

Note: To achieve ASIC resistance, the key K must change and must not be miner-selectable. We recommend to use blockchain data as the key in a similar way to the Monero example above. If blockchain data cannot be used for some reason, use a predefined sequence of keys.

CPU performance

The table below lists the performance of selected CPUs using the optimal number of threads (T) and large pages (if possible), in hashes per second (H/s). "CNv4" refers to the CryptoNight variant 4 (CN/R) hashrate measured using XMRig v2.14.1. "Fast mode" and "Light mode" are the two modes of RandomX.

CPU RAM OS AES CNv4 Fast mode Light mode
Intel Core i9-9900K 32G DDR4-3200 Windows 10 hw 660 (8T) 5770 (8T) 1160 (16T)
AMD Ryzen 7 1700 16G DDR4-2666 Ubuntu 16.04 hw 520 (8T) 4100 (8T) 620 (16T)
Intel Core i7-8550U 16G DDR4-2400 Windows 10 hw 200 (4T) 1700 (4T) 350 (8T)
Intel Core i3-3220 4G DDR3-1333 Ubuntu 16.04 soft 42 (4T) 510 (4T) 150 (4T)
Raspberry Pi 3 1G LPDDR2 Ubuntu 16.04 soft 3.5 (4T) - 20 (4T)

Note that RandomX currently includes a JIT compiler for x86-64 and ARM64. Other architectures have to use the portable interpreter, which is much slower.

GPU performance

SChernykh is developing GPU mining code for RandomX. Benchmarks are included in the following repositories:

The code from the above repositories is included in the open source miner XMRig.

Note that GPUs are at a disadvantage when running RandomX since the algorithm was designed to be efficient on CPUs.


Which CPU is best for mining RandomX?

Most Intel and AMD CPUs made since 2011 should be fairly efficient at RandomX. More specifically, efficient mining requires:

  • 64-bit architecture
  • IEEE 754 compliant floating point unit
  • Hardware AES support (AES-NI extension for x86, Cryptography extensions for ARMv8)
  • 16 KiB of L1 cache, 256 KiB of L2 cache and 2 MiB of L3 cache per mining thread
  • Support for large memory pages
  • At least 2.5 GiB of free RAM per NUMA node
  • Multiple memory channels may be required:
    • DDR3 memory is limited to about 1500-2000 H/s per channel (depending on frequency and timings)
    • DDR4 memory is limited to about 4000-6000 H/s per channel (depending on frequency and timings)

Does RandomX facilitate botnets/malware mining or web mining?

Due to the way the algorithm works, mining malware is much easier to detect. RandomX Sniffer is a proof of concept tool that can detect illicit mining activity on Windows.

Efficient mining requires more than 2 GiB of memory, which also disqualifies many low-end machines such as IoT devices, which are often parts of large botnets.

Web mining is infeasible due to the large memory requirement and the lack of directed rounding support for floating point operations in both Javascript and WebAssembly.

Since RandomX uses floating point math, does it give reproducible results on different platforms?

RandomX uses only operations that are guaranteed to give correctly rounded results by the IEEE 754 standard: addition, subtraction, multiplication, division and square root. Special care is taken to avoid corner cases such as NaN values or denormals.

The reference implementation has been validated on the following platforms:

  • x86 (32-bit, little-endian)
  • x86-64 (64-bit, little-endian)
  • ARMv7+VFPv3 (32-bit, little-endian)
  • ARMv8 (64-bit, little-endian)
  • PPC64 (64-bit, big-endian)

Can FPGAs mine RandomX?

RandomX generates multiple unique programs for every hash, so FPGAs cannot dynamically reconfigure their circuitry because typical FPGA takes tens of seconds to load a bitstream. It is also not possible to generate bitstreams for RandomX programs in advance due to the sheer number of combinations (there are 2512 unique programs).

Sufficiently large FPGAs can mine RandomX in a soft microprocessor configuration by emulating a CPU. Under these circumstances, an FPGA will be much less efficient than a CPU or a specialized chip (ASIC).


RandomX uses some source code from the following 3rd party repositories:

The author of RandomX declares no competing financial interest.


If you'd like to use RandomX, please consider donating to help cover the development cost of the algorithm.

Author's XMR address:


Total donations received: ~3.86 XMR (as of 30th August 2019). Thanks to all contributors.