superfly · kcmartin · Aug 5, 2026 · Aug 4, 2026 · Aug 5, 2026 · Aug 5, 2026
diff --git a/about/free-trial.html.md b/about/free-trial.html.md
@@ -30,7 +30,6 @@ These are **not included** in the free trial:
 
 - Dedicated IPv4 addresses
 - Access to performance-optimized vCPUs
-- GPU machines (To be deprecated as of 08/01/26)
 
 <div class="callout">You can add a credit card from the dashboard at any time during the trial. This lifts the resource limits and keeps your apps running without interruption. **Note: adding a card ends the free trial** and your usage starts counting toward your bill from that point on.</div>
 

diff --git a/about/pricing.html.markerb b/about/pricing.html.markerb
@@ -21,7 +21,7 @@ All organizations (except for Linked Organizations) require a [credit card](/doc
 
 ## Compute
 
-We charge for started and stopped Machines differently. Attached GPUs are charged separately. For more details about how costs are calculated, see [Machine billing](/docs/about/billing/#machine-billing). To understand the difference between `performance` and `shared` CPU types in Machines, see [CPU performance](/docs/machines/cpu-performance).
+We charge for started and stopped Machines differently. For more details about how costs are calculated, see [Machine billing](/docs/about/billing/#machine-billing). To understand the difference between `performance` and `shared` CPU types in Machines, see [CPU performance](/docs/machines/cpu-performance).
 
 ### Started Fly Machines
 
@@ -236,30 +236,6 @@ You **will** be billed separately for data transfer to these external third-part
 
 The [Machine price](#compute) and [volume price](#persistent-storage-volumes) for Fly Postgres are the same as any other Machine and volume you'd run on Fly.io. Assuming the Machines are running all the time, the cost for the preset configurations is about $2/month for a single node cluster for dev projects and from about $82 to $164/month for a three-node production cluster. You don't need to keep the preset configurations, you can [scale your Fly Postgres Machines](/docs/postgres/managing/scaling/) to suit your workload at any time.
 
-### GPU-enabled Fly Machines (Deprecated)
-
-<div class="warning icon">
-**GPUs are deprecated and will be unavailable after August 1.**
-</div>
-
-Pricing for a GPU-enabled Fly Machine is the price of a standard Fly Machine (see above) plus the price of the attached GPU. Like Machines, GPUs are billed by the second when the attached Machine is running.
-
-On-demand GPU pricing:
-
-* A10: $0.75/hr per GPU
-* L40S: $0.70/hr per GPU
-* A100 40G PCIe: $1.25/hr per GPU
-* A100 80G SXM: $1.50/hr per GPU
-
-Usage terms:
-
-* No minimum usage requirements.
-* Customizable CPU, RAM, and storage options.
-
-Reserved and dedicated options:
-
-* Discounted rates for reserved GPU Machines and dedicated hosts.
-
 ## Discontinued Plans
 
 Fly.io no longer offers plans to new customers. If you purchased a Launch or Scale plan before October 7, 2024, you can remain on those plans unless you convert to Pay As You Go, delete your payment method, or otherwise stop using Fly.io.

diff --git a/about/support.html.md b/about/support.html.md
@@ -94,7 +94,6 @@ Here are some things to include in your ticket:
         <ul class="space-y-2">
           <li>**Networking**</li>
           <li>**Machines**</li>
-          <li>**GPU Machines**-GPU support to end 08/01/26</li>
           <li>**Managed Postgres** (MPG)</li>
           <li>**Apps**</li>
           <li>**Launch/Deploy** (UI & CLI)</li>

diff --git a/blueprints/shared-nothing.html.markerb b/blueprints/shared-nothing.html.markerb
@@ -206,8 +206,6 @@ and the code for that article can be found on [GitHub as fly-apps/pdf-appliance]
 
 This architectural pattern can be applied whenever there is a minority of requests that require an outsized amount
 of resources.  A second example that comes to mind: I've had requests for audio capture and transcription.
-Setting up a machine that [runs Whisper with Fly GPUs](https://news.ycombinator.com/item?id=39417197) is
-something I plan to explore.
 
 ## Backups
 

diff --git a/deep-dive/index.html.markerb b/deep-dive/index.html.markerb
@@ -9,7 +9,7 @@ toc: true
   <img src="/static/images/deep-dive.png" alt="Illustration by Annie Ruygt of a bird in a diver costume, swimming under water" class="w-full max-w-lg mx-auto">
 </figure>
 
-Welcome to our deep dive demo, where you can explore Fly.io more thoroughly, but in a time-boxed way. In one hour or less: get a fully-functioning app running in the first few minutes, and then have enough time left over to understand what you just did, explore how the pieces fit together, and even integrate AI functionality that makes use of GPUs.
+Welcome to our deep dive demo, where you can explore Fly.io more thoroughly, but in a time-boxed way. In one hour or less: get a fully-functioning app running in the first few minutes, and then have enough time left over to understand what you just did and explore how the pieces fit together.
 
 ## Goals of the deep dive
 

diff --git a/elixir/advanced-guides/clustering-from-home-to-your-app-in-fly.html.md b/elixir/advanced-guides/clustering-from-home-to-your-app-in-fly.html.md
@@ -20,14 +20,6 @@ Here we cover _why_ we might want to do this, _what_ is required to make it work
 
 Besides being really cool that we can do this, there are some practical reasons as well.
 
-### AI/ML development
-
-With a local Elixir application clustered to an application on Fly.io with a GPU attached, we can keep our local development workflow without having a large GPU in our development machine.
-
-When we leverage [Nx](https://github.com/elixir-nx/nx) and [Bumblebee](https://github.com/elixir-nx/bumblebee), we can easily have the clustered application do all the GPU accelerated AI work and return the processing results seamlessly to our local application.
-
-It really does feel like the GPU is local when we work this way.
-
 ### Develop and debug a distributed application
 
 Building a globally distributed application can be challenging to model locally. With [Fly.io Regions](https://fly.io/docs/reference/regions/), we can deploy our cluster-aware application where it makes sense. Then, our local application joins the global cluster, giving us a close-up view of how the application behaves in a truly globally distributed environment.
@@ -169,6 +161,6 @@ Now you're _really_ doing distributed Elixir!
 
 ## Summary
 
-When we couple Elixir's clustering ability with Fly.io's networking, VPN, and API discoverability, we can easily cluster a locally running Elixir application with a deployed Elixir application. This makes it easy to leverage hosted GPUs for developing AI/ML applications or working on distributed applications.
+When we couple Elixir's clustering ability with Fly.io's networking, VPN, and API discoverability, we can easily cluster a locally running Elixir application with a deployed Elixir application. This makes it easy to develop and debug distributed applications.
 
 The ready-to-use script automates much of the process.
diff --git a/getting-started/launch-demo.html.markerb b/getting-started/launch-demo.html.markerb
@@ -173,4 +173,3 @@ Read about some of the ways you can increase availability, capacity, and perform
 * [Autoscale Machines based on load or custom metrics](/docs/reference/autoscaling/)
 * [Scale Machine CPU and RAM](/docs/apps/scale-machine/) 
 * [Scale Machine count](/docs/apps/scale-count/)
-* Try out [Fly GPUs](/docs/gpus/)
diff --git a/getting-started/troubleshooting.html.md b/getting-started/troubleshooting.html.md
@@ -104,7 +104,7 @@ fly auth login
 
 1. **A Fly registry incident.** Check [status.flyio.net](https://status.flyio.net). If there's an active incident, wait it out or subscribe for updates.
 
-Note the image size limits: **8GB** for standard Machines, **50GB** for GPU Machines. If your image exceeds these limits, the push fails.
+Note the image size limit of **8GB**. If your image exceeds this limit, the push fails.
 
 ### 403 Forbidden
 
@@ -199,7 +199,7 @@ Secrets set with `fly secrets set` are available as environment variables at run
 
 ### Image size limit
 
-Standard (non-GPU) Machines have an **8GB rootfs limit**. GPU Machines allow up to **50GB**.
+Machines have an **8GB rootfs limit**.
 
 If your image is too large:
 

diff --git a/gpus/getting-started-gpus.html.md b/gpus/getting-started-gpus.html.md
diff --git a/gpus/gpu-quickstart.html.markerb b/gpus/gpu-quickstart.html.markerb