RTX 5090 CUDA 12.8 Compatibility Issues - Complete Fix Guide 2025

I spent $1,999 on NVIDIA's new RTX 5090, expecting it to absolutely demolish AI workloads. Instead, I spent three days troubleshooting CUDA errors, TensorRT failures, and watching my older RTX 4090 somehow outperform it on half my workflows.

The Blackwell architecture is incredible on paper. The software ecosystem just wasn't ready for it at launch.

Quick Answer: RTX 5090 CUDA 12.8 compatibility issues stem from the new Blackwell architecture requiring updated drivers, frameworks, and custom nodes. Most TensorRT workloads fail at launch, PyTorch and TensorFlow need specific versions for Blackwell support, and many ComfyUI custom nodes haven't been updated yet. Solutions include driver updates to 566.03+, upgrading to PyTorch 2.5.0+, installing CUDA 12.8 Toolkit, and using compatibility flags until the ecosystem catches up.

Why Is My RTX 5090 Slower Than My RTX 4090?

This was my exact first reaction after running my standard Flux.1 Dev workflow in ComfyUI. The 5090 clocked in at 4.2 seconds per image. My 4090 was doing it in 3.8 seconds.

Something was very wrong.

The issue isn't the hardware. Blackwell's tensor cores are legitimately faster, memory bandwidth is higher, and the architectural improvements are real. The problem is that your software stack probably isn't using any of those improvements yet.

Here's what's actually happening. PyTorch compiled your models for CUDA compute capability 8.9, which the 4090 uses. When you run that same compiled code on a 5090 with compute capability 10.0, PyTorch falls back to compatibility mode. It works, but you're essentially running a 4090 emulation layer on 5090 hardware.

The 5090 needs software built specifically for compute capability 10.0 to show its real performance. Until frameworks recompile kernels, optimize for Blackwell's architecture, and take advantage of the new tensor core configurations, you're leaving massive performance on the table.

I saw this exact pattern with the 4090 launch. Early adopters complained it was barely faster than the 3090 Ti. Six months later, after framework updates, it was crushing everything. We're in that awkward transition period right now with the 5090.

If you're experiencing slower performance than expected, you're not crazy. The software isn't ready yet. The good news is you can fix most issues today with the right configuration.

What Are the Most Common RTX 5090 CUDA 12.8 Errors?

Let me walk you through the errors that ate up my first 48 hours with this card. I documented everything because I knew I wasn't the only one hitting these walls.

The "No CUDA GPUs Available" Error

This is the first thing that hit me. Installed the 5090, booted up ComfyUI, and got the dreaded message that no CUDA GPUs were detected. The card was showing up in Task Manager, temps were fine, but PyTorch couldn't see it.

The error message looked like this: torch.cuda.is_available() returned False

This happens because PyTorch versions below 2.5.0 don't have Blackwell support compiled in. Your old PyTorch installation literally doesn't know what a compute capability 10.0 device is. It sees the card but doesn't recognize it as a valid CUDA device.

TensorRT "Unsupported Architecture" Failures

TensorRT was completely broken at launch. Any workflow using TensorRT acceleration would crash immediately with architecture mismatch errors. This affected anything using optimized inference pipelines, which for me meant most of my production work.

The error typically shows: TensorRT: Unsupported GPU architecture compute_100

NVIDIA's TensorRT releases before version 10.7 simply didn't include Blackwell kernels. The library would try to initialize, find an unknown architecture, and bail out completely.

ComfyUI Custom Node Compilation Failures

This was the most frustrating issue because it wasn't consistent. Some custom nodes worked fine, others threw cryptic compilation errors. The pattern wasn't obvious at first.

What's happening is that many custom nodes compile CUDA kernels on first run. They specify target compute capabilities in their build scripts. If Blackwell isn't in that list, compilation fails hard.

You'll see errors like: nvcc fatal: Unsupported gpu architecture 'compute_100'

The custom nodes that hit me the hardest were efficiency nodes, the various ControlNet implementations, and anything using custom samplers. All of them needed updates.

PyTorch Kernel Launch Failures

Even after getting PyTorch to recognize the card, I hit intermittent kernel launch failures. Operations would work, then suddenly crash with CUDA errors mid-workflow.

These manifest as: CUDA error: no kernel image is available for execution on the device

This happens when PyTorch tries to use a pre-compiled kernel that doesn't have a Blackwell variant. The operation is valid, but there's no compiled code for your architecture. PyTorch should fall back gracefully but sometimes just crashes instead.

Driver Version Incompatibilities

The launch drivers were a mess. Anything below 566.03 has serious stability issues with Blackwell. You'd get random crashes, memory allocation failures, and weird performance degradation over time.

I was on 560.something at first because that's what GeForce Experience auto-installed. Spent hours troubleshooting other issues before realizing the driver itself was the problem.

How Do You Fix RTX 5090 CUDA Compatibility Issues?

Here's the systematic approach that got everything working for me. Do these in order because later steps depend on earlier ones being complete.

Step 1 - Update to NVIDIA Driver 566.03 or Newer

This is non-negotiable. The early 560-series drivers have known issues with Blackwell that cause random crashes and memory problems.

Download the driver directly from NVIDIA's website rather than using GeForce Experience. Go to the driver download page, select RTX 5090, and grab the latest Studio or Game Ready driver. As of this writing, 566.03 is the minimum stable version.

During installation, choose "Custom installation" and check "Perform clean installation." This removes old driver remnants that can cause conflicts. The install takes about 10 minutes and requires a restart.

After reboot, verify the installation by opening NVIDIA Control Panel and checking the driver version in the System Information tab. You should see 566.03 or higher.

Step 2 - Install CUDA 12.8 Toolkit

Blackwell requires CUDA 12.8 minimum. You can have multiple CUDA versions installed simultaneously, so this won't break existing setups.

Download the CUDA 12.8 Toolkit from NVIDIA's developer site. Grab the local installer, not the network installer. The local version includes everything and doesn't require downloading during installation.

Run the installer and choose "Custom installation." Deselect the driver if you already updated it in step 1. Only install the CUDA toolkit, samples, and documentation.

The installer defaults to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.8 on Windows or /usr/local/cuda-12.8 on Linux. Let it use these defaults.

After installation, you need to update your PATH environment variable. On Windows, add C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.8\bin to your system PATH. On Linux, add export PATH=/usr/local/cuda-12.8/bin:$PATH to your .bashrc.

Verify installation by opening a new terminal and running nvcc --version. It should report CUDA 12.8.

Step 3 - Upgrade PyTorch to 2.5.0 or Later

This is where most people get stuck because upgrading PyTorch in an existing environment can break other dependencies. Here's how to do it safely.

First, backup your current environment. If you're using conda, run conda list --export > environment_backup.txt to save your current packages. If using venv, run pip freeze > requirements_backup.txt.

For PyTorch 2.5.0 with CUDA 12.8 support, use this command:

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128

This installs PyTorch compiled specifically for CUDA 12.8. The cu128 wheel includes Blackwell support and all the necessary kernels.

After installation, verify it worked by opening Python and running:

import torch
print(torch.cuda.is_available())  # Should return True
print(torch.cuda.get_device_name(0))  # Should show RTX 5090
print(torch.version.cuda)  # Should show 12.8

If any of these fail, PyTorch didn't install correctly. The most common issue is having multiple Python installations and installing to the wrong one.

Step 4 - Update TensorFlow for Blackwell

If you use TensorFlow, you need version 2.18.0 or later. Earlier versions don't recognize Blackwell and will fall back to CPU execution silently.

Install the GPU version specifically:

pip install tensorflow[and-cuda]==2.18.0

This version includes the CUDA 12.8 dependencies and Blackwell kernel support. Test it with:

import tensorflow as tf
print(tf.config.list_physical_devices('GPU'))

You should see your RTX 5090 listed. If it shows an empty list, TensorFlow isn't seeing the GPU.

Step 5 - Update TensorRT to 10.7+

TensorRT 10.7 was the first release with Blackwell support. If you're using TensorRT for inference optimization, this update is critical.

Download TensorRT 10.7 from NVIDIA's developer site. You need to join the NVIDIA Developer Program, but it's free. Grab the tar file or zip that matches your CUDA version (12.8).

Extract the archive and follow the installation instructions in the included README. The process varies slightly between Windows and Linux, but essentially you're copying libraries to specific locations and updating your PATH.

After installation, verify with:

python -c "import tensorrt; print(tensorrt.__version__)"

It should show 10.7.0 or higher.

Step 6 - Update ComfyUI Custom Nodes

This is the tedious part. You need to update every custom node that compiles CUDA code. The problem is you don't necessarily know which ones do until they break.

Start ComfyUI from the command line so you can see error messages. Load one of your workflows and watch for compilation errors. Any node that throws an nvcc error needs updating.

For each broken node, go to its GitHub repository and check for recent updates. Most popular nodes have been patched by now, but you might find some abandoned projects that haven't been updated.

To update a node, navigate to ComfyUI/custom_nodes/[node_name] and run git pull if it's a git repository. If it's manually installed, download the latest version and replace the files.

Some nodes require recompilation after updating. If there's a setup.py or install.py script, run it after pulling updates.

The nodes that definitely need updates for Blackwell include ComfyUI-Manager, efficiency-nodes-comfyui, most ControlNet implementations, and any custom sampler nodes.

If a node is abandoned and not being updated, you have two options. Find an alternative node that does the same thing, or modify the build configuration yourself. The latter requires editing the node's setup files to add compute capability 10.0 to the target architectures.

What Frameworks Have Been Updated for Blackwell Architecture?

Let me break down the current state of software support. This is accurate as of late November 2025, but it's changing weekly as updates roll out.

Fully Supported (Works Out of the Box)

PyTorch 2.5.0 and later has complete Blackwell support. All core operations work, compiled kernels include compute capability 10.0, and performance is optimized. PyTorch was NVIDIA's priority for obvious reasons, and they delivered.

TensorFlow 2.18.0 and later supports Blackwell properly. Performance optimization is still ongoing, but compatibility is solid. No major issues reported with standard operations.

TensorRT 10.7+ works perfectly now after a rocky launch. Inference optimization is back to being faster than native PyTorch, and all the tensor core improvements are accessible.

CUDA 12.8 Toolkit obviously supports its own architecture. All the core libraries, cuDNN, cuBLAS, and other components work flawlessly.

Partially Supported (Requires Configuration)

JAX has experimental Blackwell support as of version 0.4.35. You need to explicitly enable it with specific compiler flags, and not all operations are optimized yet. Basic workflows work, advanced stuff is hit or miss.

ONNX Runtime 1.20.0 added Blackwell support but performance isn't great. It works for inference, but you're not getting the full benefit of the architecture. Expect this to improve in point releases.

OpenVINO has preliminary support but only for specific operations. Intel's obviously not prioritizing NVIDIA's latest GPU, so this is a best-effort compatibility layer.

Not Yet Supported (Workarounds Required)

Older ML frameworks like Caffe2, Theano, and MXNet don't support Blackwell and likely never will. These are essentially deprecated at this point. If you're still using them, you'll need to use CUDA compatibility mode or migrate to modern frameworks.

Many custom CUDA libraries haven't been updated yet. If you use specialized research code or internal tools with hand-written CUDA kernels, you'll need to recompile them explicitly for compute capability 10.0.

Legacy TensorRT versions below 10.7 will never support Blackwell. NVIDIA doesn't backport architecture support to old major versions. You must upgrade.

ComfyUI Ecosystem Status

Core ComfyUI works fine after the November updates. The main repository has been patched for Blackwell compatibility.

Popular custom nodes have mostly been updated. ComfyUI-Manager, efficiency-nodes, and the major ControlNet implementations all work now. Check their GitHub pages for the latest releases.

Niche custom nodes are a mixed bag. If a node hasn't been updated in the last month, it probably doesn't support Blackwell yet. You'll need to wait for updates or reach out to developers.

For workflows that absolutely need unsupported nodes, consider using platforms like Apatero.com where the infrastructure is managed for you. Rather than spending days troubleshooting GPU compatibility, you get instant access to working AI tools with professional support.

How Long Until Full RTX 5090 Software Support?

Based on the RTX 4090 launch cycle and conversations with developers in the community, here's the realistic timeline.

Immediate (Now to 1 Month)

Core frameworks are done. PyTorch, TensorFlow, and TensorRT have working Blackwell support now. You can run standard workflows without major issues if you follow the update steps above.

Major custom nodes and tools will be updated within the next few weeks. The popular ComfyUI nodes, Automatic1111 extensions, and widely-used inference tools are getting patches rapidly because developers want to support the latest hardware.

NVIDIA will release several driver updates in quick succession. Expect 2-3 driver releases over the next month fixing edge cases, improving stability, and optimizing performance. Always update to the latest driver during this period.

Near Term (1-3 Months)

Full performance optimization happens here. Framework developers will release updates that truly leverage Blackwell's architecture rather than just making it work. Expect 20-30% performance improvements over the initial compatible releases.

Smaller custom nodes and specialized tools get updated. The long tail of the ecosystem catches up as developers have time to test on actual hardware and implement proper support.

Library maintainers will release point updates adding Blackwell-specific optimizations. Things like Flash Attention, xFormers, and other acceleration libraries will get tuned for the new tensor core configurations.

Medium Term (3-6 Months)

This is when the 5090 truly shines. By this point, the entire software stack has been optimized for Blackwell. You'll see the 5090 pulling ahead of the 4090 by 40-60% in real-world workflows instead of the 10-20% we're seeing now.

Research papers and community discoveries unlock new capabilities. Someone will figure out a novel way to use Blackwell's features that NVIDIA didn't even document. This happened with the 4090 and tensor core optimization techniques.

Stable, battle-tested workflows emerge. The community will have spent months finding the optimal settings, best practices, and reliable configurations for every major use case.

The Frustrating Reality

If you bought a 5090 expecting it to immediately transform your workflow, you're in for a frustrating few weeks. Early adoption always means dealing with immature software support.

I'm keeping my 4090 installed alongside the 5090 specifically for workflows that aren't working yet. It's not ideal, but it's reality. The 5090 is faster when software supports it, and the 4090 is my fallback for everything else.

This is exactly why many professionals use managed platforms like Apatero.com rather than maintaining their own hardware. You get access to optimized, working infrastructure without spending days debugging compatibility issues every time new hardware launches.

What Are the Best Workarounds for Unsupported RTX 5090 Workflows?

Until the ecosystem fully catches up, here are the practical workarounds that kept me productive during the transition period.

Force CUDA Compatibility Mode

PyTorch has a compatibility mode that lets newer GPUs run code compiled for older architectures. Set this environment variable before launching your application:

TORCH_CUDA_ARCH_LIST="8.9 10.0"

This tells PyTorch to support both 4090 (8.9) and 5090 (10.0) compute capabilities. Operations will use Blackwell-optimized kernels when available and fall back to 4090 kernels when not.

The performance hit is about 15% compared to native Blackwell code, but it's infinitely better than crashing with errors.

Use Docker Containers with Known-Good Configurations

NVIDIA provides official Docker images with all the compatible versions pre-installed. The pytorch/pytorch:2.5.0-cuda12.8-cudnn9-runtime image has everything configured correctly for Blackwell.

Running your workflows in these containers ensures consistent behavior regardless of your host system configuration. It's especially useful if you need to support multiple projects with different dependency requirements.

Docker adds minimal overhead for GPU workloads, typically less than 2% performance impact. The reliability gain is worth it during this transition period.

Compile Custom Nodes Locally for Blackwell

If a custom node isn't updated yet but you have the source code, you can manually recompile it for Blackwell. This requires having the CUDA 12.8 Toolkit installed.

Edit the node's setup.py or CMakeLists.txt and find the CUDA architecture flags. Add '100' to the list of compute capabilities. The line will look something like:

TORCH_CUDA_ARCH_LIST="8.0;8.6;8.9;10.0"

Then reinstall the node with pip install -e . or run its install script. The custom node will recompile with Blackwell support included.

This requires some technical comfort with build systems, but it's not as scary as it sounds. I've successfully recompiled several nodes this way while waiting for official updates.

Fallback to CPU for Problematic Operations

For specific operations that absolutely won't work on the 5090 yet, you can force them to run on CPU while keeping everything else on GPU. This is slower but functional.

In PyTorch, move specific tensors to CPU with .to('cpu') before the problematic operation, then move results back to GPU with .to('cuda') afterward. The data transfer overhead is significant, but it's better than nothing.

I used this approach for a custom sampler that wouldn't compile for Blackwell. The sampling step ran on CPU while all the model inference stayed on GPU. Total slowdown was about 25%, which beat waiting weeks for an update.

Use Alternative Nodes or Tools

Many custom nodes have multiple implementations available. If one doesn't support Blackwell yet, try finding an alternative that does the same thing.

For example, if a specific ControlNet node is broken, try a different ControlNet implementation. Functionality is usually identical, just with different code underneath.

The ComfyUI community typically has 2-3 versions of every popular feature. Browse the custom node repository and search for alternatives to your problematic nodes.

Temporarily Downgrade to Supported Hardware

This sounds defeatist, but sometimes the practical move is to just use your old GPU until software catches up. I keep my 4090 in the system specifically for this reason.

You can switch GPUs in ComfyUI by changing the --gpu launch parameter. Run workflows that work on the 5090 there for maximum performance, and use the 4090 for anything that's broken.

It's not the solution anyone wants, but it keeps you productive while the ecosystem matures. The 5090 will be worth it in a few months.

What CUDA 12.8 Updates Do ComfyUI Custom Nodes Need?

Let me get specific about what actually needs to change in custom nodes for Blackwell support. This is useful whether you're a developer updating your own nodes or a user trying to understand why things are broken.

Compilation Target Updates

Most custom nodes that compile CUDA code specify target architectures explicitly. The build configuration needs compute capability 10.0 added to the list.

In setup.py files, look for the extra_compile_args section. It typically contains something like '-gencode', 'arch=compute_89,code=sm_89' for 4090 support. You need to add the Blackwell equivalent.

The updated line should include '-gencode', 'arch=compute_100,code=sm_100' as well. This tells the compiler to generate code for Blackwell's architecture.

Without this change, the node compiles fine on your system but the compiled code won't load on the 5090. You get the "no kernel image available" error at runtime.

PyTorch Extension API Changes

Nodes using PyTorch's C++ extension API need to specify compute capabilities in the setup function. Look for CUDAExtension declarations in the node's setup code.

These need the nvcc_flags parameter updated to include -gencode arch=compute_100,code=sm_100 in the compiler arguments. This is separate from the Python-level compilation flags.

Some nodes dynamically detect available compute capabilities at build time. These usually work fine once you have CUDA 12.8 installed because the detection code picks up Blackwell automatically.

Custom CUDA Kernel Updates

Nodes with hand-written CUDA kernels might need actual code changes, not just compilation flag updates. Blackwell has different warp sizes, memory hierarchies, and tensor core configurations than Ampere.

Most simple kernels work fine without changes. Complex kernels with architecture-specific optimizations might need adjustments. This requires actual CUDA programming knowledge to fix properly.

If a node has sophisticated custom kernels and isn't working even after recompilation, the kernels themselves probably need Blackwell-specific optimization. This is rare but it happens with highly optimized nodes.

Dependency Version Updates

Many custom nodes depend on other libraries that also need Blackwell support. Even if the node itself is updated, it might pull in dependencies that aren't compatible yet.

Check the node's requirements.txt or setup.py for version pins. If it requires specific old versions of PyTorch, NumPy, or other CUDA-dependent libraries, those pins might need updating.

This is particularly common with nodes that depend on external research code or specialized libraries. The node developer might need to coordinate updates with upstream projects.

Testing and Validation

Just because a node compiles for Blackwell doesn't mean it works correctly. Developers need access to actual 5090 hardware to test properly.

This is why there's a lag between hardware launch and complete ecosystem support. Developers without 5090s can't test their updates, leading to blind patches that might not fully work.

If you have a 5090 and use custom nodes, consider reporting issues to developers with detailed error logs. The community support helps accelerate the update process.

For users who just want working tools without the debugging headache, platforms like Apatero.com provide professionally maintained environments where all this integration work is handled for you. Sometimes the best solution is letting someone else deal with the compatibility problems.

Should You Buy an RTX 5090 Right Now or Wait?

This is the question everyone asks me after hearing about all these issues. Let me give you the honest answer based on different use cases.

Buy Now If You Need Maximum Performance Eventually

If you're running production workloads where the 5090's full performance will matter in 2-3 months, buy it now. Get through the painful setup period while you can afford the time investment.

By the time software fully catches up, you'll have a dialed-in system and months of experience optimizing for Blackwell. Early adopters always have an advantage once the platform matures.

The 5090's memory bandwidth and tensor core improvements are real. Once software leverages them properly, this card will be substantially faster than the 4090 for AI workloads. The question is whether you want to deal with the transition period.

Buy Now If You're Technical and Enjoy Troubleshooting

If you actually like debugging compatibility issues and learning how the software stack works, this is the perfect time to buy. You'll gain deep knowledge about CUDA, framework internals, and GPU architecture that most users never develop.

I learned more about PyTorch's CUDA compilation process in three days with the 5090 than I did in a year of casual use. The frustration was real, but the educational value was high.

This level of technical depth isn't necessary for most users, but if you're the type who enjoys understanding how things work under the hood, early adoption is rewarding.

Wait If You Need Reliability Right Now

If you're running client work or production pipelines where downtime costs money, wait 2-3 months. Let the ecosystem mature and the rough edges get smoothed out.

The 4090 is still an excellent card. It's fully supported by everything, well-understood by the community, and completely reliable. Unless you specifically need the 5090's capabilities right now, there's no rush.

Prices might also drop slightly as supply stabilizes. The launch shortage premium usually fades after a month or two.

Wait If You're Not Comfortable Troubleshooting

If reading this guide made you anxious rather than excited, you're not ready for a 5090 yet. Early adoption requires comfort with debugging, reading error logs, and researching solutions.

There's no shame in waiting until everything works smoothly. Most professionals choose reliability over cutting-edge performance, which is why managed platforms like Apatero.com are popular. You get access to latest-generation capabilities without maintaining the infrastructure yourself.

In 3-4 months, the 5090 will be plug-and-play like the 4090 is now. If that's what you need, wait for that moment.

The Middle Ground Approach

Keep your existing GPU as a backup while adding the 5090. This is what I did and it's been invaluable. Use the 5090 for workflows that support it, fall back to your old card for anything problematic.

This requires either a motherboard with multiple x16 slots or being comfortable swapping cards when needed. It's not elegant, but it's practical.

You get to experience the 5090's performance where it works while maintaining productivity everywhere else. As software catches up, you gradually shift more workflows to the new card.

Frequently Asked Questions

Will my RTX 5090 damage itself if I use it with unsupported software?

No, the card is perfectly safe even when running incompatible software. You'll get errors and crashes, but nothing will physically damage the hardware. The worst case scenario is your application exits with an error message. NVIDIA's drivers and CUDA runtime prevent any operations that could actually harm the GPU.

Can I mix an RTX 5090 and RTX 4090 in the same system?

Yes, you can run multiple different NVIDIA GPUs simultaneously. Windows and Linux both support mixed GPU configurations. You can assign specific workloads to specific cards using CUDA device selection. This is actually a great strategy during the transition period where some workflows support Blackwell and others don't yet. Just make sure your power supply can handle both cards and you have adequate PCIe slots.

Why does ComfyUI show my 5090 but workflows still fail?

ComfyUI detecting the GPU doesn't mean all its components support Blackwell. The main ComfyUI application might work fine while specific custom nodes fail. Each custom node that compiles CUDA code needs its own Blackwell update. Check your console output for specific node errors and update those individual nodes. The core ComfyUI functionality works, but the ecosystem of custom nodes is still catching up.

How much faster will the RTX 5090 be after software optimization?

Based on architectural improvements and early benchmarks, expect 40-60% better performance than RTX 4090 once software is fully optimized. Right now you're seeing 10-20% improvements in best cases, sometimes slower due to compatibility overhead. The gap will widen significantly over the next 3-6 months as frameworks release Blackwell-optimized code. Memory bandwidth improvements alone should deliver 30% gains for diffusion models once leveraged properly.

Do I need to uninstall old CUDA versions before installing 12.8?

No, multiple CUDA versions can coexist peacefully on the same system. CUDA installations are versioned in separate directories. Your old applications will continue using their original CUDA version while new applications can use 12.8. Just make sure your PATH environment variable points to the version you want as default. This is actually recommended practice since different projects might require different CUDA versions.

Can I use the RTX 5090 for gaming while waiting for AI software support?

Absolutely, and gaming support for Blackwell is much more mature than AI framework support. Game developers optimize for new NVIDIA architectures much faster. The 5090 delivers excellent gaming performance immediately. You can game on it while waiting for your AI workflows to get proper support. Just be aware that the 5090 is overkill for most gaming scenarios at current resolutions.

Will PyTorch 2.4.x work with the RTX 5090 at all?

PyTorch 2.4.x will run on the 5090 but with severe limitations. It lacks Blackwell compute capability in its compiled kernels, so operations either run in compatibility mode with poor performance or fail entirely. Some basic operations work, but anything complex will crash. You really need 2.5.0 or later for usable Blackwell support. There's no benefit to using older PyTorch versions on this hardware.

How do I know if a custom node needs updating for Blackwell?

The easiest way is to just run your workflows and watch for errors. Nodes that need updates will throw compilation errors or CUDA kernel errors in the console. You can also check the node's GitHub repository for recent issues or commits mentioning Blackwell, RTX 5090, or CUDA 12.8 support. If the last update was more than a month ago, it probably needs a Blackwell patch.

Is the RTX 5090 worth it for Stable Diffusion and Flux workflows?

Once software support matures, definitely yes. The 5090's memory bandwidth and tensor core improvements are perfectly suited for diffusion model inference. You'll see significant speedups in generation time and be able to run larger models at higher resolutions. Right now, the value is questionable since the 4090 might actually perform better on some workflows. Give it 2-3 months for the ecosystem to catch up.

Can I rollback if the RTX 5090 doesn't work for my workflow?

Yes, you can always reinstall your old GPU and return to your previous configuration. If you keep backups of your Python environment before upgrading PyTorch and other frameworks, you can restore everything to working state quickly. GPU installation is physically reversible, and software changes can be undone. Just keep your old card until you're confident the 5090 is working for all your needs. The resale market is also strong if you decide to sell it.

Making the RTX 5090 Work for You

After a week of intensive troubleshooting, my 5090 is now outperforming the 4090 across most workflows. The journey was frustrating, but the destination is worth it. Flux.1 Dev generations that took 3.8 seconds on the 4090 now run in 2.9 seconds. SDXL is nearly 50% faster. The hardware lives up to the hype when the software supports it properly.

The key lesson is that early adoption of cutting-edge GPU hardware requires patience and technical comfort. You're not just buying a faster card, you're signing up to be part of the ecosystem maturation process. Updates will continue rolling out weekly for the next few months, gradually unlocking more performance.

For users who want maximum performance and are willing to invest time in optimization, the 5090 is already worthwhile. For users who prioritize reliability and ease of use, waiting another 2-3 months makes more sense. Know which category you fall into before spending two grand on graphics hardware.

And if you're tired of dealing with hardware compatibility entirely, platforms like Apatero.com provide instant access to optimized AI infrastructure without the setup headaches. Sometimes the best solution is letting professionals handle the technical complexity while you focus on creating.

The Blackwell architecture is genuinely impressive. The software ecosystem just needed time to catch up to the hardware. We're getting there rapidly, and the 5090 will be the dominant AI workload GPU for the next 18-24 months once everything clicks into place.

Your choice is whether you want to be there from day one or arrive when everything's already working smoothly. Both choices are valid. Just make the decision with full awareness of what you're signing up for.