Using NVIDIA MPS to increase Folding@Home PPD on high-end GPUs (Linux guide)
This article will contain only technical details intended for advanced F@H folders and participants.
Using NVIDIA MPS to increase Folding@Home PPD on high-end GPUs (Linux guide)
This article will contain only technical details intended for advanced F@H folders and participants.
If you’re not familiar with this topic, it will probably be completely useless or boring for you. If you’re into this kind of technical stuff, you’ll probably enjoy it.
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Overview
An RTX 3090 on Linux earns around 9M PPD. By utilizing a trick called MPS (Multi-Process Service), this can be increased to 14M PPD. Other big GPUs, like the 4080/4090 and 5080/5090, may see even greater gains. Remember that smaller GPUs do not benefit from this guide.
If you already know a bit about GPU architecture, feel free to skip to the section “A guide to using MPS with FAH”.
Why are small projects not suitable for high-end GPUs?
Nvidia GPUs contain hundreds to tens of thousands of units called CUDA cores that perform 32-bit floating point calculations (the type that dominates the work done in FAH simulations). When running a compute application with CUDA on a GPU, small programs called kernels are sent to the GPU sequentially. Once a kernel has finished its task, which usually takes mere microseconds, the CPU sends the next kernel to the GPU.
Each kernel performs some computational work like determining which atoms are interacting, calculating the force of chemical bonds, or even something as mundane as erasing a buffer in memory. Dozens of kernel launches in a row, each one doing a different job, make up a single simulation step and advance the simulation by 2–4 femtoseconds. Tens of thousands of simulation steps make up 1% of a work unit.
When a kernel executes, it launches hundreds to thousands of threads. Each thread performs the same core computation defined by the kernel, but on a different piece of data (such as a different set of atoms or forces). The massive number of CUDA cores in the GPU work together to execute the arithmetic instructions in these threads concurrently and are what makes GPUs so efficient at performing parallel computation tasks.
If the kernel has too few threads to occupy all the GPU’s CUDA cores, then some cores are left idle and do nothing but draw power. Small work units that simulate systems with a small number of atoms may not be able to make use of enough threads to fully-utilize high-end GPUs because those GPUs simply run more threads at once, but each thread is not necessarily faster than on lower-end GPUs. This is why projects with few atoms earn you less PPD on high-end GPUs than projects with many atoms. FAH servers will prioritize assigning larger projects to these GPUs, but even those might not be large enough to fully saturate modern high-end cards. As GPUs get bigger and bigger, projects are having a harder time fully utilizing them. This is called Amdahl’s law and that is what this guide helps overcome.
For nerds like me who want to know what a kernel’s code looks like, here is one for reduceEnergy, used by FAH to sum up the energy of the system. It shows the CUDA C++ code, the PTX code (an assembly-like intermediate representation that can run on all Nvidia GPUs) that the CUDA C++ compiles to, and the SASS code (the largely-undocumented raw assembly that is unique to each GPU generation, in this case Ampere) that the PTX compiles to at launch.
What if we could run multiple projects on a single GPU simultaneously?
There is no speedup if you simply start two projects on the same GPU together. Even if they appear to be running simultaneously, they are being interleaved and the GPU is only running one kernel from one project at any given instant. But there is a way to run two (or more) projects on the same GPU truly simultaneously, using the Linux-specific CUDA MPS server.
The MPS server attaches directly to the GPU. CUDA applications then connect to MPS instead of the GPU. MPS combines the requests and enables true concurrent execution which increases thread density and GPU utilization. This requires launching the MPS server and setting the GPU into exclusive mode.
A guide to using MPS with FAH:
While MPS is well-documented and technical guides exist for using MPS with molecular simulations, so far no one has written one that integrates seamlessly with Folding@home. So, here it is.
First, you need to install the MPS program and a program for managing the GPU called nvidia-smi. On Ubuntu, install the packages as shown here, replacing “570” in the MPS package with whatever your Nvidia driver version is. On Debian, the MPS package may be named “nvidia-cuda-mps” instead.
sudo apt install nvidia-smi nvidia-compute-utils-570Now create a systemd service override by running:
sudo systemctl edit fah-client.serviceThis will open up an editor where an override service file will be created. Put the following contents into it to launch MPS whenever FAH is launched, and to terminate MPS whenever FAH is terminated (the GPU is also brought into and taken out of exclusive compute mode as needed):
[Service]
ExecStartPre=+/usr/bin/nvidia-smi -c EXCLUSIVE_PROCESS
ExecStartPre=/usr/bin/nvidia-rrrcuda-mps-control -d
ExecStopPost=/bin/sh -c '/bin/echo quit | /usr/bin/nvidia-cuda-mps-control'
ExecStopPost=+/usr/bin/nvidia-smi -c DEFAULTRestart FAH for the changes to take effect:
sudo systemctl restart fah-client.serviceNow FAH should be running with MPS. But we still need to allow multiple work units to run on one GPU. To do this, open the web client and create a new resource group as described in the v8.4 client guide. Enable only the GPU in the new resource group. This will cause a WU to be downloaded and run on that GPU in addition to the one you have set in your default resource group. You can add more than one extra resource group if your GPU is big enough (especially if it’s a 5090), but usually one extra resource group is enough.
To determine if you are using MPS, use the nvidia-smi program to get a summary of the processes the GPU is servicing:
$ nvidia-smi
Sat Jul 19 12:01:40 2025
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 570.169 Driver Version: 570.169 CUDA Version: 12.8 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 3090 On | 00000000:03:00.0 Off | N/A |
|100% 83C P0 401W / 420W | 794MiB / 24576MiB | 99% E. Process |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| 0 N/A N/A 10239 C nvidia-cuda-mps-server 28MiB |
| 0 N/A N/A 40499 M+C ...4bit-release-8.1.4/FahCore_24 378MiB |
| 0 N/A N/A 40962 M+C ...4bit-release-8.1.4/FahCore_24 378MiB |
+-----------------------------------------------------------------------------------------+The processes with “M+C” listed are compute apps that communicating with the GPU through the MPS server.
You should be good to go now! Monitor your PPD to make sure that you’re seeing an increase. Give it a few hours for PPD to stabilize. Each work unit yields lower PPD individually, but the combined output increases significantly. A single WU alone gives me 9M PPD, but MPS allows me to run two WUs for 6.5M PPD each, totaling 14M PPD! All on a single 3090!
How can I tell if my GPU is being fully-utilized?
You can get basic statistics from your GPU that will let you determine if it’s big enough to benefit from MPS, and whether you could benefit from adding another resource group. This is what it looks like when your GPU is not being well-utilized. Each line represents one second. Notice how how power usage fluctuates rapidly and SM usage (the percentage of time that one or more kernels was executing on the GPU in the last sample period) is inconsistent:
$ nvidia-smi dmon -s pu
# gpu pwr gtemp mtemp sm mem enc dec jpg ofa
# Idx W C C % % % % % %
0 376 82 - 82 28 0 0 0 0
0 394 82 - 90 32 0 0 0 0
0 373 82 - 86 27 0 0 0 0
0 393 82 - 75 24 0 0 0 0
0 375 81 - 96 34 0 0 0 0
0 391 82 - 95 34 0 0 0 0
0 396 83 - 79 25 0 0 0 0
0 389 82 - 87 29 0 0 0 0
0 391 82 - 83 26 0 0 0 0
0 397 83 - 77 25 0 0 0 0Contrast this with stats that you get when MPS is enabled and two WUs are running. See how both power usage and SM utilization are higher and more consistent:
$ nvidia-smi dmon -s pu
# gpu pwr gtemp mtemp sm mem enc dec jpg ofa
# Idx W C C % % % % % %
0 399 83 - 100 41 0 0 0 0
0 396 82 - 100 45 0 0 0 0
0 400 84 - 100 43 0 0 0 0
0 414 82 - 99 44 0 0 0 0
0 386 82 - 97 45 0 0 0 0
0 397 83 - 100 43 0 0 0 0
0 401 83 - 100 46 0 0 0 0
0 390 81 - 100 43 0 0 0 0
0 391 82 - 92 40 0 0 0 0
0 402 82 - 100 45 0 0 0 0These are stats from an RTX 3090. Notice how a single WU does not make full use of the GPU’s resources. Almost a fifth of the GPU’s time is spent idling, doing nothing at all. When running two WUs at once with MPS, this idle period is reduced to nearly 1%. Average power usage only increases by about 10W, but total PPD increases by 5M PPD! Not all GPUs will benefit from MPS, however. The same project that only partially-utilizes an RTX 3090’s 10496 CUDA cores can fully utilize a GTX 970M’s mere 1280 CUDA cores without any tricks:
$ nvidia-smi dmon -s pu
# gpu pwr gtemp mtemp sm mem enc dec jpg ofa
# Idx W C C % % % % % %
0 85 58 - 99 56 0 0 - -
0 84 58 - 100 57 0 0 - -
0 82 58 - 100 59 0 0 - -
0 75 58 - 99 55 0 0 - -
0 84 58 - 94 50 0 0 - -
0 85 58 - 100 58 0 0 - -
0 87 58 - 95 52 0 0 - -
0 80 58 - 98 55 0 0 - -
0 76 58 - 99 55 0 0 - -
0 85 58 - 99 58 0 0 - -This makes sense. The 970M is more than a decade old and has just a bit more than a tenth as many cores as the 3090. This little GPU gets me about 0.5M PPD, but as even medium-sized projects can fully saturate it, there’s not much room for improvement.
Likewise, not all projects benefit from MPS. Some projects are large enough to fully saturate even the widest GPUs, although they are more rare. The ultimate way to determine how much benefit you are getting is by checking to see if the total ns/day has increased. Look for the growing science.log files in your work directory. Every checkpoint, it will log the most recent ns/day. When MPS is in use, you’ll have multiple science.log files for the same GPU. Add together the ns/day values and see if the sum is greater than the ns/day value of a single WU when MPS is not active. Here are some examples for a particularly small project running on an RTX 4090 Mobile:
Total ns/day without MPS: 1x WU: 1080 2x WU: 813 (~407 ea) 3x WU: 814 (~271 ea) 4x WU: 810 (~203 ea) 5x WU: 807 (~161 ea)
Total ns/day with MPS: 1x WU: 1016 2x WU: 1687 (~844 ea) 3x WU: 2191 (~730 ea) 4x WU: 2514 (~629 ea) 5x WU: 2823 (~565 ea)
For that particular project, running 5 simultaneous copies increases total ns/day by almost 3 times! Most projects will not scale this well however, and will only benefit from one, or sometimes two, extra WUs. On occasion, you’ll increase total ns/day but lose PPD due to a quirk in FAH’s point awarding algorithm. In that case, you have to decide whether more science or more points are worth more to you.
GPUs work best when they are oversubscribed (running more threads than they can simultaneously execute) because it ensures they always have work to do and the scheduler will always be able to find a pending thread to utilize an available hardware resource (such as a single CUDA core that has no work to do), but adding too many WUs may decrease PPD due to increasingly diminishing returns and higher memory cache pressure.
Is this allowed? Could this taint the science?
MPS is completely safe and does not make any changes to FAH’s behavior or tamper with its code (which would violate the EULA). It is not cheating either: The increase in PPD is a result of more efficient GPU usage and does not game the point algorithm. It is officially-supported by Nvidia on Linux and is designed to allow multiple compute apps to run at once without interfering with each other. Both MPS and OpenMM (the simulation software that FAH uses for GPU folding) are compatible and are often used together.
What if I have multiple GPUs?
I don’t have a multi-GPU system to test it out, but launching MPS with CUDA_VISIBLE_DEVICES variable set to a comma-delimited list of the GPU indexes or UUIDs on your system should enable MPS for multiple GPUs. You can get a comma-delimited list this way:
nvidia-smi --query-gpu=uuid --format=csv,noheader | paste -sd ','In the fah-client.service override file, add CUDA_VISIBLE_DEVICES as an environmental variable. It should look something like this:
Environment=CUDA_VISIBLE_DEVICES=GPU-c7bf74dd-ec57-41ae-a1dc-c8f8ee96053e,GPU-b4c9e155-45c9-4906-9090-6bd6fa9e0b37What if I’m using AMD and not Nvidia?
As far as I know, AMD does not require any workarounds at all and can natively run multiple simultaneous simulations without needing something like MPS. You can create a new resource group to add a second WU to the same GPU right away if you turn on AMD hardware scheduling. There aren’t many AMD GPUs that are big enough to benefit from this, but perhaps someone with a 7900XTX could test this out.
What if I’m using Windows and not Linux?
Unfortunately, Nvidia has not released a version of MPS for Windows. Maybe it could work by running FAH and MPS under WSL2, but I have no idea if that’s feasible due to WSL2’s use of paravirtualization. The GPU will have to be set to EXCLUSIVE_PROCESS mode from Windows and not from WSL2.
PPD on Windows is typically lower than that of Linux anyway even for the same GPU. If you’re chasing PPD, you really should be using Linux.
A DrDoge folder tested it.
Thank your for testing big dog! These are his notes:
PPD/Watt probably got little worse The not-so-accurate Nvidia utilization and power (target?) numbers go from: about 92%, 472W (Without MPS), to 99%, 575W (With MPS).
As a rough estimate A 5090 typically gets 43M PPD on a lot of WUs (18245 for this example), and doing MPS with 2 WUs: 2 x 26M = 52M PPD.
More WU The number of WUs increased by about 15 WUs per card per day.
Using NVIDIA MPS to dramatically increase PPD on big GPUs (Linux guide)
https://foldingforum.org/viewtopic.php?t=43130
Update GPU driver to latest, if it's been awhile to avoid minor rev mismatches.
For Linux Mint, with v595 drivers:
---
sudo apt install nvidia-compute-utils-595
// This is only needed for 2-card setup below:
nvidia-smi --query-gpu=uuid --format=csv,noheader | paste -sd ','
---
sudo systemctl edit fah-client.service
1 GPU:
[Service]
ExecStartPre=+/usr/bin/nvidia-smi -c EXCLUSIVE_PROCESS
ExecStartPre=/usr/bin/nvidia-cuda-mps-control -d
ExecStopPost=/bin/sh -c '/bin/echo quit | /usr/bin/nvidia-cuda-mps-control'
ExecStopPost=+/usr/bin/nvidia-smi -c DEFAULT
-- OR --
For 2 GPUs, run this command and use the output in the Env... line below: nvidia-smi --query-gpu=uuid --format=csv,noheader | paste -sd ','
[Service]
Environment=CUDA_VISIBLE_DEVICES=GPU-aae157dd-fb3a-3b3a-eccd-0f5771aea880,GPU-50926525-be8e-2b98-7841-2f875b8ec48a
ExecStartPre=+/usr/bin/nvidia-smi -c EXCLUSIVE_PROCESS
ExecStartPre=/usr/bin/nvidia-cuda-mps-control -d
ExecStopPost=/bin/sh -c '/bin/echo quit | /usr/bin/nvidia-cuda-mps-control'
ExecStopPost=+/usr/bin/nvidia-smi -c DEFAULT
sudo systemctl restart fah-client.service---------
Before:
User@Z890-19:~$ nvidia-smi
Mon May 18 19:50:39 2026
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 580.95.05 Driver Version: 580.95.05 CUDA Version: 13.0 |
+-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 5090 Off | 00000000:01:00.0 On | N/A |
| 0% 40C P1 78W / 600W | 970MiB / 32607MiB | 4% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 1 NVIDIA GeForce RTX 5090 Off | 00000000:02:00.0 Off | N/A |
| 66% 68C P1 471W / 575W | 864MiB / 32607MiB | 93% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| 0 N/A N/A 1171 G /usr/lib/xorg/Xorg 196MiB |
| 0 N/A N/A 1539 G xfwm4 7MiB |
| 0 N/A N/A 991623 C /usr/NX/bin/nxnode.bin 537MiB |
| 0 N/A N/A 1205380 G /usr/lib/firefox/firefox 161MiB |
| 1 N/A N/A 1171 G /usr/lib/xorg/Xorg 4MiB |
| 1 N/A N/A 1204977 C ...4bit-release-8.2.1/FahCore_27 840MiB |
+-----------------------------------------------------------------------------------------+---
After:
Environment=CUDA_VISIBLE_DEVICES=GPU-aae157dd-fb3a-3b3a-eccd-0f5771aea880,GPU-50926525-be8e-2b98-7841-2f875b8ec48a
3 out of 4 RGs:
User@Z890-19:~$ nvidia-smi
Mon May 18 21:43:01 2026
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 595.58.03 Driver Version: 595.58.03 CUDA Version: 13.2 |
+-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 5090 Off | 00000000:01:00.0 On | N/A |
| 57% 73C P1 598W / 600W | 1937MiB / 32607MiB | 98% E. Process |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 1 NVIDIA GeForce RTX 5090 Off | 00000000:02:00.0 Off | N/A |
| 64% 70C P1 472W / 575W | 912MiB / 32607MiB | 92% E. Process |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| 0 N/A N/A 1189 G /usr/lib/xorg/Xorg 166MiB |
| 0 N/A N/A 1531 G xfwm4 7MiB |
| 0 N/A N/A 3690 C nvidia-cuda-mps-server 52MiB |
| 0 N/A N/A 4186 M+C ...4bit-release-8.2.1/FahCore_27 836MiB |
| 0 N/A N/A 4253 M+C ...4bit-release-8.2.1/FahCore_27 836MiB |
| 1 N/A N/A 1189 G /usr/lib/xorg/Xorg 4MiB |
| 1 N/A N/A 3690 C nvidia-cuda-mps-server 52MiB |
| 1 N/A N/A 4244 M+C ...4bit-release-8.2.1/FahCore_27 836MiB |
+-----------------------------------------------------------------------------------------+
--
User@Z890-19:~$ nvidia-smi
Mon May 18 22:37:25 2026
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 595.58.03 Driver Version: 595.58.03 CUDA Version: 13.2 |
+-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 5090 Off | 00000000:01:00.0 On | N/A |
| 56% 74C P1 600W / 600W | 1947MiB / 32607MiB | 100% E. Process |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 1 NVIDIA GeForce RTX 5090 Off | 00000000:02:00.0 Off | N/A |
| 75% 76C P1 571W / 575W | 2344MiB / 32607MiB | 99% E. Process |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| 0 N/A N/A 1189 G /usr/lib/xorg/Xorg 176MiB |
| 0 N/A N/A 1531 G xfwm4 7MiB |
| 0 N/A N/A 6067 C nvidia-cuda-mps-server 52MiB |
| 0 N/A N/A 6138 M+C ...4bit-release-8.2.1/FahCore_27 836MiB |
| 0 N/A N/A 6201 M+C ...4bit-release-8.2.1/FahCore_27 836MiB |
| 1 N/A N/A 1189 G /usr/lib/xorg/Xorg 4MiB |
| 1 N/A N/A 6067 C nvidia-cuda-mps-server 52MiB |
| 1 N/A N/A 6078 M+C ...4bit-release-8.2.1/FahCore_27 836MiB |
| 1 N/A N/A 6261 M+C ...4bit-release-8.2.1/FahCore_27 1432MiB |
+-----------------------------------------------------------------------------------------+===========
---
After:
Environment=CUDA_VISIBLE_DEVICES=GPU-7536dc93-bf65-24b3-0aa7-83748eb99220,GPU-69fbc467-1580-c675-02e0-f0935908a8d2
User@Z890-17:~$ nvidia-smi
Mon May 18 19:39:11 2026
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 595.58.03 Driver Version: 595.58.03 CUDA Version: 13.2 |
+-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 5090 Off | 00000000:01:00.0 On | N/A |
| 71% 74C P1 601W / 600W | 2144MiB / 32607MiB | 97% E. Process |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 1 NVIDIA GeForce RTX 5090 Off | 00000000:AE:00.0 Off | N/A |
| 57% 68C P1 560W / 600W | 1738MiB / 32607MiB | 99% E. Process |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| 0 N/A N/A 1156 G /usr/lib/xorg/Xorg 202MiB |
| 0 N/A N/A 1564 G xfwm4 7MiB |
| 0 N/A N/A 4577 C nvidia-cuda-mps-server 52MiB |
| 0 N/A N/A 4590 G /usr/lib/firefox/firefox 143MiB |
| 0 N/A N/A 5179 M+C ...4bit-release-8.2.1/FahCore_27 836MiB |
| 0 N/A N/A 5255 M+C ...4bit-release-8.2.1/FahCore_27 836MiB |
| 1 N/A N/A 1156 G /usr/lib/xorg/Xorg 4MiB |
| 1 N/A N/A 4577 C nvidia-cuda-mps-server 52MiB |
| 1 N/A N/A 5078 M+C ...4bit-release-8.2.1/FahCore_27 826MiB |
| 1 N/A N/A 5254 M+C ...4bit-release-8.2.1/FahCore_27 836MiB |
+-----------------------------------------------------------------------------------------+
Disclaimer
This guide was created by a Folding@Home forum member, not by the DrDoge project. We simply wrote this article to help the information reach more people.
One of our DrDoge folders tested it, confirmed that it works, and found it to be very interesting technology.
Many thanks to the Folding forum member **arisu **for creating the guide. Source: https://foldingforum.org/viewtopic.php?t=43130
Useful links & resources:
DrDoge Website: https://drdoge.net
How to earn DrDoge with Folding@Home: https://drdoge.net/smart-pups-do-science
DrDoge profit calculator: https://drdoge.net/things/scientific-bark-ulator
DrDoge Audit: https://hashlock.com/audits/drdoge
DrDoge smart contract address: 0x9d95DFBB1aBb8F57abc20FDE4F786BB1Db52ad71
DrDoge forever locked pool address for DEX trading: 0x8f5e13be76cd74b9c7330d00a5598616536b7047 https://dexscreener.com/pulsechain/0x8f5e13be76cd74b9c7330d00a5598616536b7047
DrDoge twitter: https://x.com/DrDoge_PLS
DrDoge Discord: https://discord.com/invite/etQckZgu74
Important: The DrDoge project exists only on PulseChain. If you find another token with the same name on a different chain, it’s a different project.
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