Cast XMR - high speed CryptoNight miner for RX Vega GPUs (Beta)

Updated on March 29th, 2018 to version 0.9.2

This version is outdated, please check the latest version here

Here is ‘Cast XMR’ the high performance CryptoNight miner for CryptoNote based crypto currencies like Monero (XMR), Bytecoin (BCN), DigitalNote (XDN) and many more.

Cast XMR is specially optimized for the Radeon RX Vega series of GPUs, reaching hash rates of more then 2000 CryptoNight Hashes/s on an single RX Vega 56 or Vega 64.

cast xmr

Hash faster and try it for yourself:

Cast XMR 0.9.2 for Windows (64 bit)


  • Full support for CryptoNight/CryptoNote based currencies:
  • Fastest miner for AMD Radeon RX Vega GPU series
  • Radeon RX Vega 64
    • Crimson 17.10.2 driver and stock settings: 1180 Hash/s
    • Blockchain driver and 1025 MHz mem clock: 1990 Hash/s
    • Adrenalin driver 18.4.3 and 1025 MHz mem clock: 1980 Hash/s
  • Radeon RX Vega 56
    • Crimson driver 17.10.2 and stock setting: 1150 Hash/s
    • Blockchain driver and 945 MHz mem clock: 1970! Hash/s
    • Adrenalin 18.4.3 driver and 945 MHz mem clock: 1950 Hash/s
  • Radeon RX 480/RX 580
    • Crimson 17.10.2 driver and stock settings: 640 Hash/s
    • Blockchain driver and 2315 MHz mem clock: 810 Hash/s
  • Radeon RX 470/RX 570
    • Crimson 17.11.4 and stock settings: 640 Hash/s
    • Blockchain driver and 2250 MHz mem clock: 830 Hash/s
    • Adrenalin 18.4.3 driver in compute mode and 2250 MHz mem clock: 820 Hash/s
  • Multiple GPU support
  • Monitor temperature and fan speed of each GPU
  • Full pool support
  • Nicehasher support
  • Remote http access for statistics in JSON format
  • only 1.5% dev fee


How To

cast_xmr has a command line interface. For a minimal configuration run:

cast_xmr -S [pool server] -u [username or wallet address]

To select which GPU to use the -G switch, e.g. for using the 2nd card use:

cast_xmr -S [pool server] -u [username or wallet address] -G 1

To select multiple GPU to use the -G switch and list comma separated the GPUs which should be used, e.g. for using the 1st and 3rd card use:

cast_xmr -S [pool server] -u [username or wallet address] -G 0,2

In case you have multiple OpenCL implementation installed or mixed GPUs (Nvidia, Intel, AMD), the correct OpenCL implemenation can be selected with the “–opencl” switch:

cast_xmr -S [pool server] -u [username or wallet address] --opencl 1 -G 0

For a complete list of configuration options run:

cast_xmr --help

Read here how to get the best mining performance with a Radeon RX Vega 56/64 GPU

Change History

Cast XMR Version 0.9.2 (2018/3/29)

  • Improvements for RX Vegas that achieve 99% the performance of the Blockchain Compute Beta driver with the latest standard Radeon Driver 18.3.4

Cast XMR Version 0.9.1 (2018/3/22)

  • 0.5% overall performance improvement for Vega based GPUs
  • --ratewatchdog experimental option to monitor the hash rate, in case an occasional happening drop of the hash rate is detected the kernel will be resetted to restore the optimal hash rate
  • fully tested CryptoNight variant (CryptoNightV7) for the Monero V7 Network Upgrade PoW Change which is now scheduled for the 6th April.
  • In case you want to mine the Graft coin use the --algo=0 option, an automatic detection of the used CryptoNight variant is not possible for this coin. For all other known CryptoNight coins the hash variant seems to be detected correct
  • In case you are mining with or any other automatic coin switching pool that has the Graft coin in the mix the --algo=0 option is advised to prevent invalid shares on the Graft coin. Be aware to remove the option when the Monero V7 network upgrade is happpening otherwise it then will clash with mining Monero!

Cast XMR Version 0.9.0 (2018/3/8)

  • support for the Monero V7 network upgrade. Read more about it here
  • the Monero PoW switch will be automatically detected, no restart of cast XMR is required. Only make sure to update cast XMR before the network upgrade is happening and that the used Monero pool supports the network upgrade
  • no decrase in hash rate for other currencies was introduced by the change
  • CryptoNightV7 will run at the same hash rate as CryptoNight
  • for any case there is the new --algo option to force which CryptoNight variant to use:
    • -1 = autodetect (default)
    • 0 = CryptoNight
    • 1 = CryptoNightV7 (Monero V7 network upgrade)

Cast XMR Version 0.8.5 (2018/2/1)

  • 1% performance gain for Vega Frontier Edition with Blockchain driver (fastest GPU around)
  • 1% performance improvement with current stock driver (18.1.1) for RX Vega 56/64 (40% to go to reach Blockchain driver)
  • fix for random blocking console output
  • GPU now always logs with its device id not the order they are listed in the -G argument
  • --log option to log output to a file
  • remote access includes CORS support in HTTP header

Cast XMR Version 0.8.1 (2017/12/15)

  • Fix for OpenCL automatic platform detection for Crimson driver 17.12.1 (Adrenalin). Blockchain driver is still way more performant on Vegas though

Cast XMR Version 0.8 (2017/12/14)

  • kernel optimizations result in subtle performance improvements
  • --fastjobswitch option (experimental) to force fast switching to new jobs. When enabled the GPUs will switch to the new job within less then 100ms. This improves the effective hash rate by 1 to 5% as nearly no GPU performance will be wasted on calculating outdated shares anymore. The drawback is a slightly lower displayed hashrate as while switching no hashes are calculated.
  • improved support for more then 8 GPUs
  • support for Radeon RX 470/RX 570 (Polaris) added
  • --forcecompute option to force kernel for compute mode on Polaris (RX470/570/480/580) GPUs. In the Radeon Settings GPU Workload has to be switched to ‘Compute’. This option is available in Crimson 17.10.2 (or later) driver and reaches nearly the same performance on Polaris based GPUs as with the Blockchain driver.
  • openCL platform is auto detected if --opencl is not specified
  • --nonicehash option to disable nicehash support
  • nicehash support will automatically disabled if more then 8 GPUs are used. Otherwise search space could be exhausted to early and the GPUs search in overlapping nonce ranges.

Cast XMR Version 0.7 (2017/11/29)

  • network and job status added to current statistics
  • remote access to get current status at in JSON format (enable with --remoteaccess)
  • command line option to specify number of reconnects to pool (default 100) before giving up (set with --reconnects n). To realise a simple failover to another pool set it to a lower value and add a second configuration to batch file
  • fix for nicehash to support upto 8 GPUs (before only 4 were possible)

Cast XMR Version 0.6 (2017/10/27)

  • improved queue sync which results in an 0.1% higher average hash rate
  • experimental support for Radeon RX 480/580 GPUs added
  • pressing ‘s’ key prints the current statistics
  • count outdated and invalid shares separate
  • fixed random crash when quitting app
  • bugfix: invalid command line arguments did hang app

Cast XMR Version 0.5 (2017/10/19)

  • support for multiple Vega GPUs. Specify multiple GPUs comma separated with the -G switch like “-G 0,1,2,3”
  • monitor temperature and fan speed for each GPU
  • subtle performance improvements in core

Cast XMR Version 0.4 (2017/10/13)

  • performance optimzations result in 1 - 2% higher hash rates
  • reduced dev fee to 1.5%

Cast XMR Version 0.3 (2017/10/10)

  • performance optimzations result in 5 - 10% higher hash rates
  • breaking the 2 KHashes/s barrier with working overclocking settings for RX Vega 64 and RX Vega 56

Cast XMR Version 0.2 (2017/09/27)

  • performance improvement result in 1% higher hashrates
  • Improved support for RX Vega 56 (now goes faster then a Vega 64 !?)
  • Detection if Blockchain driver is installed
  • Fix for Sikka and Intense pools

Cast XMR Version 0.1 (2017/09/22)

  • Initial Release

A Linux version is nearly finished, but does not reach the same performance as the Windows version, still investigating.


Casting software for high performance, massive parallel computing and machine learning.