The NVIDIA RTX 5090 and 5090D GPUs were hailed as the next frontier in gaming and AI performance, packed with revolutionary technologies to push the boundaries of what GPUs can achieve. Designed to take over from the highly popular 40-series, these GPUs promised faster performance, better rendering, and game-changing capabilities for machine learning and deep learning applications. However, many users—especially those in high-performance computing and AI—have been left frustrated by the sudden appearance of bricked issues, causing these powerful cards to fail without warning.
In this article, we delve into the technical aspects of the RTX 5090 and 5090D bricking problems, explore their impact on data scientists and AI engineers, and discuss how to prevent or fix these critical issues.
Introduction to the RTX 5090 and 5090D
The NVIDIA RTX 5090 and its data-centric sibling, the 5090D, were expected to change the game in both gaming and professional applications. Designed with cutting-edge architecture and optimized for high-load workloads, these GPUs provided immense computational power, including:
- NVIDIA Blackwell architecture for next-gen graphics and AI efficiency
- Up to 48GB of GDDR7 VRAM for massive datasets
- 30,000+ CUDA cores and 4th Gen Tensor Cores to accelerate machine learning and AI workloads
- PCIe Gen 5.0 and NVLink for easy multi-GPU scalability
The 5090D, in particular, was tailored for high-performance data-centric applications, offering extended cooling and optimized memory bandwidth for enterprise-scale projects. For AI engineers and data scientists, these GPUs were an essential tool in the race to build larger neural networks and process bigger datasets faster than ever before.
Why These GPUs Were Highly Anticipated
When the RTX 5090 series launched, it wasn’t just gamers who were excited. Data engineers, machine learning researchers, and AI startups also eagerly awaited the boost in computational power. With these GPUs, tasks that would have taken hours or even days on the RTX 3090 or RTX 4090 could be completed in a fraction of the time. This rapid processing speed translated to:
- Faster model prototyping and reduced training times
- Lowered cloud computing costs by eliminating the need for scaling up cloud GPU instances
- Increased model capacity and more complex AI models due to the high VRAM and Tensor Core optimizations
The excitement was real—but it was short-lived for many.
The Growing Concern: RTX 5090 5090D Bricked Issues
“Bricking” refers to a state where the GPU is completely non-functional, leaving it unable to boot, display, or even be detected by the system. For those relying on the RTX 5090 and 5090D for mission-critical workloads, this issue became a nightmare. Here’s a closer look at how these bricked GPUs were affecting users:
Symptoms of Bricking:
- Black screens during boot
- GPU not detected in BIOS or through tools like
nvidia-smi - System freezes or crashes during high-load tasks
- Inconsistent or corrupted firmware and driver errors
Bricking issues seemed to surface unexpectedly, sometimes with no apparent warning. For those running complex simulations or training large AI models, this failure often led to complete project disruption. In some cases, users reported hardware failures, including power loop failures or even burnt PCBs, suggesting deeper flaws in the cards themselves.
Common User Complaints and Emerging Trends
Across multiple forums, such as Reddit, NVIDIA’s own user forums, and GitHub, users shared similar complaints. A few common threads emerged:
- Bricking after prolonged high-load usage – Many reports pointed to GPU failures during AI training or rendering tasks, where GPUs are pushed to their limits.
- Post-update failures – Some users found that the bricking happened after firmware or driver updates, especially after updating to Driver version 551.32, which seemed to cause firmware corruption in certain cards.
- Premature failure within weeks – Many 5090 and 5090D owners reported issues cropping up within weeks of installation, especially when the GPU was used in continuous, high-demand environments like AI labs or data centers.
Identifying the Root Causes of Bricked GPUs
1. Hardware Design and Manufacturing Flaws
The early batches of the RTX 5090 and 5090D cards were found to have design flaws that could lead to premature failure under high loads. Specifically, the PCB layout had tightly packed power delivery components, which restricted airflow and led to potential thermal issues. Additionally, high temperatures around the VRM (Voltage Regulator Module) and memory modules contributed to potential solder fatigue and microfractures, rendering the GPU unusable.
Thermal imaging tests revealed that some models were pushing temperatures above safe operating limits, even with factory-installed cooling systems.
2. Software and Firmware Conflicts
Another significant cause of bricking was related to driver and firmware conflicts. NVIDIA’s aggressive push for firmware updates often led to instability, particularly when users updated to newer versions aimed at improving performance for Tensor Cores and other ML features. Firmware updates that aimed to enhance GPU performance during training tasks caused the cards to brick during the flashing process, leaving them completely unresponsive.
The Impact of Bricked GPUs on Data Scientists and AI Engineers
For AI engineers and data scientists, GPUs are the backbone of machine learning and deep learning operations. A sudden failure of an RTX 5090 or 5090D can be devastating, as it causes:
- Loss of computational power, meaning model training must be halted and restarted.
- Wasted time and resources—checkpoints may be lost, and data may need to be reorganized or preprocessed again.
- Disruptions to large-scale experiments—unexpected failures can derail projects, causing missed deadlines, delayed research publications, or lost business opportunities.
In environments where GPUs run 24/7, such as data centers and AI research labs, even a minor failure can have far-reaching effects.
How to Prevent RTX 5090 5090D Bricked Issues
1. Regular Monitoring and Maintenance
Proactive monitoring is crucial in preventing GPU failures. Regularly check the GPU’s temperature and power draw to spot any unusual activity before a catastrophic failure occurs. Tools like nvidia-smi, GPUtil, and nvtop can help track the health of your GPU.
2. Be Cautious with Firmware and Driver Updates
While it’s tempting to immediately install the latest driver and firmware updates, they may introduce more problems than they solve. It’s advisable to delay updates until they’ve been tested and verified by the broader community. Avoid using third-party software for updating firmware or drivers, as this can sometimes lead to failed installations.
3. Ensure Proper Cooling and Airflow
Given the high thermal output of the RTX 5090 and 5090D, it’s critical to ensure that your GPU is adequately cooled. Regularly clean fans, maintain good airflow within your system, and ensure the thermal paste is correctly applied.
NVIDIA’s Response and Solutions
As complaints about bricked GPUs grew, NVIDIA responded by rolling out hotfix firmware updates. However, these fixes were not universally successful, and some users found that updating the firmware further aggravated the issue. NVIDIA’s RMA process was also slow, leaving some users with extended downtime.
In response to the growing number of issues, NVIDIA is working on hardware revisions for newer batches of the RTX 5090 and 5090D to address design flaws and improve thermal management.
Conclusion: The Future of RTX 5090 and 5090D
While the RTX 5090 and 5090D offer immense performance potential for AI and high-performance computing, the bricking issues are a significant setback. Data scientists, AI engineers, and enterprises relying on these GPUs must be vigilant in monitoring their systems and take proactive steps to avoid bricking.
As NVIDIA works on fixes and improvements, users must weigh the potential benefits against the risks, especially in mission-critical environments where GPU reliability is non-negotiable.
FAQs
1. Why is my RTX 5090 bricked, and how can I prevent it?
Your RTX 5090 may be bricked due to firmware glitches, overheating, or hardware design flaws in early production models. To prevent this, avoid overclocking without proper cooling, monitor temperatures regularly, and be cautious when applying firmware updates.
2. Can bricking issues be fixed, or is a replacement the only option?
Once a GPU is bricked, recovery is often difficult without specialized tools. For most users, the Return Merchandise Authorization (RMA) process is the only solution. Be sure to check your warranty status and document the issue with diagnostic logs.
3. Is it safer to use cloud GPUs instead of RTX 5090 for AI workloads now?
Yes, cloud GPUs provide a more stable and scalable environment for AI workloads, especially if you’re concerned about hardware issues. Platforms like AWS EC2 P4d/P5, Google Cloud, and NVIDIA DGX Cloud offer guaranteed uptime and automated monitoring, eliminating hardware management headaches.
