Gigahorse 3.0 Compressed Plots

The chart has been updated for plot filter 256, effective starting on June 13th 2024 (max farm size is half than before).

Partial difficulty is important for maximum farm size, especially for C19 / C20 and C29 to C33 (higher difficulty is better).

Solo farming roughly corresponds to a partial difficulty of 500k (500000).

Join the Discord for support: https://discord.gg/BswFhNkMzY

Chia Gigahorse Node / Farmer / Harvester

In the release section you can find Chia Blockchain binaries to farm compressed plots created with the new plotters provided in this repository.

The compressed plot harvester and farmer are not compatible with the official Chia Node, it only works together with the Gigahorse Node. However it's possible to use a wallet from the official Chia repository, instead of the Gigahorse binary wallet.

Both NFT and OG plots are supported, as well as solo and pool farming (via the official pool protocol). Regular uncompressed plots and Bladebit plots are supported as well, so you can use Gigahorse while re-plotting your farm.

The dev fee is as follows:

• 3.125 % when using GPU(s) to farm compressed plots
• 1.562 % when using CPU(s) to farm compressed plots
• 0 % for regular uncompressed plots

When you find a block there's a chance the 0.125 XCH farmer reward is used as fee, this is a random process. In case of CPU farming it's 1 out of 8 blocks on average, and for GPU farming it's 1 out of 4 blocks on average.

When the fee is paid from a block, you will see a log entry like this:

full_node: WARNING  Used farmer reward of block 2187769 as dev fee (3.125 % on average)

It will show the block height as well as the average fee that applies, depending on if the proof was computed via CPU or GPU.

Pool Partial Difficulty

When farming NFT plots on a pool it is recommended to set the partial difficulty to 100 or more, otherwise your harvester will be overloaded with computing full proofs.

For GH 3.0 a much higher parital difficulty is required to achieve optimal performance, see the chart above for recommended settings.

Plot Reload Interval

It is recommended to increase your plot reload interval to at least 3600 seconds in config.yaml:

harvester:
  plots_refresh_parameter:
    interval_seconds: 3600

The default value of 120 sec will cause too much CPU load with large plot counts.

GUI Usage

Gigahorse now offers a full GUI package, by installing via ChiaSetup-X.X.X.gigaX.exe on Windows or installing chia-blockchain_X.X.X.gigaX_amd64.deb on Linux.

This will over-write any existing Chia installation, but keeps your databse and all your settings and wallets. It works just like the official GUI but with GH support.

You will see this icon to signal that you're running GH and not official Chia.

CLI Usage Linux

The new way with *.deb package install

Usage with the *.deb package install is the same as with official Chia.

For example:

chia stop all -d
chia start farmer

The old way with *.tar.gz install

Make sure to close any other instances first:

chia stop all -d

Or close the Chia GUI if you are running it. Otherwise you cannot start the Gigahorse version.

Using the Gigahorse binaries is pretty much the same as with a normal Chia installation:

cd chia-gigahorse-farmer
./chia.bin start farmer (full node + farmer + harvester)
./chia.bin start harvester (remote harvester)
./chia.bin show -s
./chia.bin farm summary
./chia.bin plotnft show
./chia.bin wallet show
./chia.bin stop all -d

Note the usage of ./chia.bin ... instead of just chia ..., this is the only difference in usage with Gigahorse.

Alternatively, you can . ./activate.sh in chia-gigahorse-farmer to be able to use chia ... commands instead of ./chia.bin ....

CLI Usage Windows

The new way with ChiaSetup-*.exe install

Usage with the ChiaSetup-*.exe install is the same as with official Chia.

For example:

chia stop all -d
chia start farmer

The old way with *.tar.gz install

Make sure to close any running Chia GUI first, otherwise you cannot start the Gigahorse version.

To start the farmer double click start_farmer.cmd in chia-gigahorse-farmer, this will open a terminal where you can continue to issue commands. To only open a terminal without starting anything you can use chia.cmd. To stop everything you can use stop_all.cmd.

The usage in general is the same as normal chia:

chia.exe start farmer (not: chia start farmer)
chia.exe start harvester (not: chia start harvester)
chia show -s
chia farm summary
chia plotnft show
chia wallet show
chia stop all -d

Official GUI + Gigahorse

Note: Gigahorse now offers the GUI as well, but if you still want to use official GUI, see below.

You can start the official Chia GUI after starting Gigahorse in a terminal, however it needs to be the same version. It will still complain about version mismatch but when the base version (like 1.6.2) is the same then it works.

When you close the GUI everything will be stopped, so you need to restart Gigahorse in the terminal again if so desired. Newer GUI versions allow to keep services running at exit though, if you select it.

Installation

Linux

Install dependency first:

sudo apt install ocl-icd-libopencl1

The new way of using GH on Debian systems:

sudo dpkg -i ~/Downloads/chia-blockchain_X.X.X.gigaX_amd64.deb

Or if you don't need the GUI and use CLI only:

sudo dpkg -i ~/Downloads/chia-blockchain-cli_X.X.X.gigaX_amd64.deb

Alternatively, the old way of using GH:

tar xf chia-gigahorse-farmer-*.tar.gz

Windows

The new way of using GH: Install ChiaSetup-X.X.X.gigaX.exe and use the GUI or command line.

The old way of using GH: Unzip the chia-gigahorse-farmer-*.zip somewhere.

You might also have to install latest Microsoft Visual C++ Redistributable: https://aka.ms/vs/17/release/vc_redist.x64.exe

Note: There is no need to re-sync the blockchain, Gigahorse will re-use your existing database and config.

Limit GPU / RAM usage

Please take a look at:

• How to limit GPU usage
• How to limit RAM usage

Note: When changing environment variables you need to restart the Chia daemon for it to take effect: ./chia.bin stop all -d or chia.exe stop all -d

When mixing different K size and C levels, only the higest RAM / VRAM requirement applies.

Remote Compute

It's possible to move the compute task to another machine or machines, in order to avoid having to install a GPU or powerful CPU in every harvester:

To use the remote compute feature:

• Start chia_recompute_server on the machine that is doing the compute (included in release).
• export CHIAPOS_RECOMPUTE_HOST=... on the harvester (replace ... with the IP address or host name of the compute machine, and make sure to restart via chia stop all -d or stop_all.cmd on windows)
• On Windows you need to set CHIAPOS_RECOMPUTE_HOST variable via system settings.
• CHIAPOS_RECOMPUTE_HOST can be a list of recompute servers, such as CHIAPOS_RECOMPUTE_HOST=192.168.0.11,192.168.0.12. A non-standard port can be specified via HOST:PORT syntax, such as localhost:12345. Multiple servers are load balanced in a fault tolerant way.
• CHIAPOS_RECOMPUTE_PORT can be set to specify a custom default port for chia_recompute_server (default = 11989).
• See chia_recompute_server --help for available options.

To use the remote compute proxy (optional):

• Start chia_recompute_proxy -n B -n C ... on a machine A. (B, C, etc are running chia_recompute_server)
• Set CHIAPOS_RECOMPUTE_HOST on your harvester(s) to machine A.
• chia_recompute_proxy can be run on a central machine, or on each harvester itself, in which case A = localhost.
• See chia_recompute_proxy --help for available options.

When using CHIAPOS_RECOMPUTE_HOST, the local CPU and GPUs are not used, unless you run a local chia_recompute_server and CHIAPOS_RECOMPUTE_HOST includes the local machine.

CPU based Compute Servers

For CPU based compute it's important to increase CHIAPOS_MAX_CORES on the harvesters to achieve full CPU utilization on compute servers. Because CHIAPOS_MAX_CORES is the maximum parallel requests made from a harvester to recompute servers, and a request is processed on a single CPU core only. By default CHIAPOS_MAX_CORES is the number of phsical CPU cores on the harvester.

For example if you have a single compute server with 32 CPU cores, you should set CHIAPOS_MAX_CORES on the harvesters to 32. The sum of CHIAPOS_MAX_CORES accross all harvesters should be greater or equal to the sum of CPU cores on all compute servers. In case of low number of harvesters (ie. 1-3) you should set CHIAPOS_MAX_CORES to the number of CPU cores on your compute server.

K33+

K33+ performance for C11 to C20 is considerably less than K32. In addition higher K size benefits more from a higher partial difficulty. GH 3.0 only supports K32.

Known Issues

• AMD GPU getting stuck in Linux, workaround is: watch -n 0.1 sudo cat /sys/kernel/debug/dri/0/amdgpu_pm_info

Gigahorse GPU Plotter

You can find the GPU plotter binaries here: cuda-plotter.

CPU Plotter (GH 1.0 and 2.0 only)

You can find the CPU plotter binaries here: cpu-plotter.

Farming Benchmark

To test how many plots you can farm on a given system you can use the ProofOfSpace tool in chiapos.

Plot Sink

Plot Sink is a tool to receive plots over the network and copy them to multiple HDDs in parallel.

You can find binaries in plot-sink

See also the open source repository: https://github.com/madMAx43v3r/chia-plot-sink

Docker Usage

The Dockerfile file uses multiple build stages to support 4 different applications CPU-Only, NVIDIA-GPU, Intel-GPU, and AMD-GPU.

It is highly recommended to run the container with the /root/.chia/mainnet directory mapped to a local volume for persistant storage of the database and config files

CPU-Only

Docker Run Example:

docker run --rm -it -v /path/to/.chia:/root/.chia/mainnet -p 8444:8444 ghcr.io/madmax43v3r/chia-gigahorse:latest

Docker Compose Example:

version: '3'
services:
  chia:
    image: ghcr.io/madmax43v3r/chia-gigahorse:latest
    restart: unless-stopped
    volumes:
      - /path/to/.chia:/root/.chia/mainnet
#      - /path/to/plots:/plots
#      - /path/to/ssl/ca:/path/in/container
    ports:
      - "8444:8444"
    environment:
#      TZ: 'UTC'
      CHIA_SERVICES: 'farmer'
#      CHIA_UPNP: 'true'
#      CHIA_LOG_LEVEL: 'WARNING'
#      CHIA_HOSTNAME: 127.0.0.1
#      CHIA_PLOTS: /plots
### Remote harvester settings
#      CHIA_FARMER_ADDRESS: 127.0.0.1
#      CHIA_FARMER_PORT: 8447
#      CHIA_CA: /path/in/container
### Remote compute server
#      CHIAPOS_RECOMPUTE_HOST: 192.168.1.12   

NVIDIA-GPU

Docker Run Example:

docker run --rm -it --runtime=nvidia -v /path/to/.chia:/root/.chia/mainnet -p 8444:8444 ghcr.io/madmax43v3r/chia-gigahorse:latest-nvidia

Docker Compose Example:

version: '3'
services:
  chia:
    image: ghcr.io/madmax43v3r/chia-gigahorse:latest-nvidia
    restart: unless-stopped
    runtime: nvidia
    volumes:
      - /path/to/.chia:/root/.chia/mainnet
#      - /path/to/plots:/plots
#      - /path/to/ssl/ca:/path/in/container
    ports:
      - "8444:8444"
    environment:
#      TZ: 'UTC'
      CHIA_SERVICES: 'farmer'
#      CHIA_UPNP: 'true'
#      CHIA_LOG_LEVEL: 'WARNING'
#      CHIA_HOSTNAME: 127.0.0.1
#      CHIA_PLOTS: /plots
### Remote harvester settings
#      CHIA_FARMER_ADDRESS: 127.0.0.1
#      CHIA_FARMER_PORT: 8447
#      CHIA_CA: /path/in/container
### Remote compute server
#      CHIAPOS_RECOMPUTE_HOST: 192.168.1.12
### GPU Specific Options ###
#      NVIDIA_VISIBLE_DEVICES: 0,3

Note: for nvidia you also need the NVIDIA Container Toolkit installed on the host, for more info please see: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker

Intel-GPU

Docker Run Example:

docker run --rm -it --device=/dev/dri -v /path/to/.chia:/root/.chia/mainnet -p 8444:8444 ghcr.io/madmax43v3r/chia-gigahorse:latest-intel

Docker Compose Example:

version: '3'
services:
  chia:
    image: ghcr.io/madmax43v3r/chia-gigahorse:latest-intel
    restart: unless-stopped
    devices:
      - /dev/dri:/dev/dri
    volumes:
      - /path/to/.chia:/root/.chia/mainnet
#      - /path/to/plots:/plots
#      - /path/to/ssl/ca:/path/in/container
    ports:
      - "8444:8444"
    environment:
#      TZ: 'UTC'
      CHIA_SERVICES: 'farmer'
#      CHIA_UPNP: 'true'
#      CHIA_LOG_LEVEL: 'WARNING'
#      CHIA_HOSTNAME: 127.0.0.1
#      CHIA_PLOTS: /plots
### Remote harvester settings
#      CHIA_FARMER_ADDRESS: 127.0.0.1
#      CHIA_FARMER_PORT: 8447
#      CHIA_CA: /path/in/container
### Remote compute server
#      CHIAPOS_RECOMPUTE_HOST: 192.168.1.12
### GPU Specific Options ###
#      CHIAPOS_MAX_OPENCL_DEVICES: 0

Note: for ARC GPU's you will need to be running kernel 6.2+ on your docker host

AMD-GPU

Docker Run Example:

docker run --rm -it --device=/dev/kfd --device=/dev/dri -v /path/to/.chia:/root/.chia/mainnet -p 8444:8444 ghcr.io/madmax43v3r/chia-gigahorse:latest-amd

Docker Compose Example:

version: '3'
services:
  chia:
    image: ghcr.io/madmax43v3r/chia-gigahorse:latest-amd
    restart: unless-stopped
    devices:
      - /dev/dri:/dev/dri
      - /dev/kfd:/dev/kfd
    volumes:
      - /path/to/.chia:/root/.chia/mainnet
#      - /path/to/plots:/plots
#      - /path/to/ssl/ca:/path/in/container
    ports:
      - "8444:8444"
    environment:
#      TZ: 'UTC'
      CHIA_SERVICES: 'farmer'
#      CHIA_UPNP: 'true'
#      CHIA_LOG_LEVEL: 'WARNING'
#      CHIA_HOSTNAME: 127.0.0.1
#      CHIA_PLOTS: /plots
### Remote harvester settings
#      CHIA_FARMER_ADDRESS: 127.0.0.1
#      CHIA_FARMER_PORT: 8447
#      CHIA_CA: /path/in/container
### Remote compute server
#      CHIAPOS_RECOMPUTE_HOST: 192.168.1.12
### GPU Specific Options ###
#      CHIAPOS_MAX_OPENCL_DEVICES: 0

Further Customization

You can modify the container options by uncommenting the relevent settings in the docker-compose example and changing them from the defaults.

Services

You can set which services to run with the CHIA_SERVICES environment variable.

Docker Run Examples:

-e CHIA_SERVICES="harvester"
-e CHIA_SERVICES="node farmer-only"
-e CHIA_SERVICES="node farmer-only wallet"

Docker Compose Examples:

environment:
  - CHIA_SERVICES="harvester"

environment:
  - CHIA_SERVICES="node farmer-only"
  
environment:
  - CHIA_SERVICES="node farmer-only wallet"

When setting CHIA_SERVICES="harvester" you will also need to specify the following environment variables CHIA_CA CHIA_FARMER_ADDRESS CHIA_FARMER_PORT

Docker Compose - uncomment the relevant lines in the example above and adjust the settings accordingly.

Docker Run - add the following to your command:

-e CHIA_FARMER_ADDRESS="farmer.ip.address" -e CHIA_FARMER_PORT="8447" -v /path/to/ssl/ca:/path/in/container -e CHIA_CA="/path/in/container"

Plots

Add plot directories by uncommenting the CHIA_PLOTS environment variable and set the /path/to/plots volume to a local plot directory

Keys

Add keys by entering the container shell and using the chia keys command:

docker exec -it container-name bash ./chia.bin keys add