The NVIDIA RTX 5090 and 5090D were supposed to be the pinnacle of the GPU world: exceptional performance, futuristic features, and a major leap forward in AI and gaming. Yet, for many users, especially those working in high-performance computing and AI development, these high-end GPUs have become a source of immense frustration. RTX 5090 5090D Bricked Issues have surfaced in various use cases, causing catastrophic failures of powerful and expensive hardware, sometimes without warning.

In this in-depth analysis, we will explore the technical complexities of these failures, evaluate NVIDIA’s response, and define their specific implications for the data science and AI community, where GPU reliability is not a luxury, but a necessity.

Introducing the RTX 5090 and 5090D

NVIDIA’s next-generation flagship graphics cards

The RTX 5090 and 5090D mark a turning point for NVIDIA in next-generation GPU performance. Building on the success of the 40 series and advancing it with significant hardware improvements, these cards offer optimized AI processing and more powerful rendering engines, designed to meet the needs of gamers and professionals. The 5090D, often considered the “data” version, offers optimized memory bandwidth and cooling for enterprise workloads.

These GPUs incorporated cutting-edge technologies:

NVIDIA Blackwell architecture for next-generation graphics and increased AI efficiency

Up to 48GB of GDDR7 VRAM to handle massive datasets

More than 30,000 CUDA cores and 4th-generation Tensor Cores for accelerated machine learning and AI

PCIe Gen 5.0 and NVLink for rapid multi-GPU scalability

For professionals designing neural networks, training language models, or running large-scale simulations, this translated into faster results and greater modeling capabilities than previous models.

Why were these GPUs so highly anticipated?

The launch of the RTX 5090 generated considerable excitement, not only among gamers, but also among data engineers, machine learning researchers, and AI startups. The reason? Untapped computing power.

Tasks that previously took hours on a 3090, or even a 4090, were now completed in record time. For data scientists, this meant accelerated prototyping, shorter wait times for deep model training, and potential savings on cloud computing costs.

Performance improvements over previous generations

Comparison with the RTX 4090:

Training times for Transformer-based models are reduced by 40%.

Inference latency for real-time recommendation engines is significantly reduced.

Simulation workloads benefit from up to 2.5x acceleration.

This power revolutionized the market. However, it also introduced new risks: increased thermal constraints, greater firmware complexity, and stringent architectural requirements.

RTX 5090 5090D Bricked Issues

Definition of GPU Freezing Phenomenon

A frozen GPU becomes completely inoperative. It does not boot, is not displayed, and is not detected by the system. With the RTX 5090 5090D Bricked Issues, this freezing occurred suddenly and without warning. Users reported:

Black screens at startup
GPU not detected in the BIOS or nvidia-smi
Sudden crashes during demanding tasks
Unrecoverable firmware or driver errors

On technical forums, some engineers even reported power supply circuit failures or burnt circuit boards, signs of more serious hardware failures.

RTX 5090 5090D Bricked Issues Frequent User Complaints

On Reddit, NVIDIA forums, and GitHub issues, some trends began to emerge:

The crashes occurred after intensive and prolonged use.

They often happened after firmware or driver updates.
Some systems crashed a few weeks after installation.
The crashes were more than just an inconvenience. For professionals using these GPUs for machine learning or big data processing, they caused major disruptions to their workflows.

Issues Encountered in Consumer and Professional Configurations

While some failures appeared on high-end gaming PCs, the most concerning cases involved data centers and AI labs. Companies that had integrated multiple RTX 5090s into their deep learning platforms experienced cascading failures across multiple clusters.

One image recognition startup lost three GPUs in a single week. A university research group had to cancel a semester-long AI training project after their 5090D cards failed mid-experiment.

RTX 5090 5090D Bricked Issues Emerging trends

Tech communities began tracking failure logs, sharing common symptoms, and proposing temporary solutions. These included:

Driver version 551.32 was linked to firmware corruption.

GPU temperatures reached 100°C before the cards shut down.

BIOS vulnerabilities prevented firmware updates.

While not all issues with RTX 5090 5090D Bricked Issues, their frequency and severity generated significant concern and an immediate demand for solutions.

RTX 5090 5090D Bricked Issues Causes

Hardware Design and Manufacturing Defects

Faced with a growing number of reports of RTX 5090 5090D Bricked Issues, analysts and hardware teardown experts began identifying potential design flaws as the root cause. Several independent testers found that the PCB layout of early batches of 5090 cards featured very compact power components. This not only restricted airflow but also increased the risk of voltage fluctuations under heavy workloads.

Thermal imaging revealed hot spots around the VRM (voltage regulator module) and memory modules, particularly during AI training sessions. These hot spots often exceeded safe thresholds, even with factory-installed cooling solutions. Excessive heat, if not properly managed, likely contributed to solder fatigue and potential microcracks, preventing the GPU from booting.

Some users also observed inconsistencies in the application of thermal paste and thermal pads, suggesting quality control failures. In large-scale data science operations, where GPUs run 24/7, even minor heat dissipation issues can lead to significant long-term failures.

Software conflicts, drivers, and firmware bugs

Another major contributing factor to the wave of crashes was NVIDIA’s driver and firmware ecosystem. With each new generation of GPUs, NVIDIA releases updates to support new CUDA cores, TensorRT features, and compatibility with AI frameworks like PyTorch and TensorFlow. However, RTX 5090 users quickly discovered that some firmware updates were buggy, even extremely dangerous, as evidenced by crashing issues with the RTX 5090 and 5090D.

In particular, firmware updates intended to optimize Tensor Core performance caused cards to crash during the update process. In some cases, users lost access to their GPU during the update, ending up with a device that was no longer recognized by the system.

Data scientists who automate driver updates via DevOps pipelines or dependency managers were among those affected. A simple compatibility error between the NVIDIA driver and its machine learning framework led to sudden system crashes and corrupted the BIOS of RTX 5090 and 5090D GPUs.

Implications for Data Scientists and AI Engineers

GPU Reliability

In the world of data science, GPUs are indispensable: they form the backbone of any serious machine learning, deep learning, or big data pipeline. When RTX 5090 5090D Bricked Issues mid-training on a model with 200 million parameters, the entire process must be restarted. Checkpoints can be lost, data must be reorganized, and hours, or even days, of processing time can be wasted.

Not only is this frustrating, but it also hurts productivity. Especially in environments with tight deadlines, release targets, or customer deliveries, hardware failures can cause significant delays and damage reputation.

Many AI teams conduct experiments at night or on weekends, and if a GPU crashes during this time without an alert system, entire tasks silently fail, sometimes going undetected until the next business day.

The Cost of Downtime and Experiment Interruptions

Let’s talk numbers. The RTX 5090 retails for over $2,000 and the 5090D for around $3,000. But that’s just the cost of the hardware. The true cost of an RTX 5090/5090D GPU failure includes:

Lost time in the cloud (for tasks transferred to backup servers)

Work hours spent debugging or restarting pipelines

Delays in model validation cycles

For AI startups or independent data scientists, a single RTX 5090/5090D GPU failure can wipe out weeks of work. For enterprise AI teams, the problem is exponential. A cluster of 8 to 10 GPUs can paralyze an entire phase of the project.

Identify the warning signs of failure.

Most GPUs don’t fail suddenly. They typically exhibit subtle symptoms before completely failing. For data scientists managing their teams or HPC administrators operating large-scale GPU clusters, recognizing these signs early is crucial.

Pay attention to the following:

Increased fan noise or consistently high fan speeds
Unusual temperature spikes, even at idle
Inconsistent power consumption reported by tools like nvidia-smi
GPU crashes during undemanding tasks

Setting up GPU monitoring dashboards with tools like Prometheus, Grafana, and Telegraf can help detect anomalies before they become catastrophic.

Tools for GPU Health Monitoring

Proactive monitoring is your best defense. Here are some tools and strategies:

nvidia-smi: Run it regularly to check usage, temperature, and memory errors.

GPUtil: A Python tool that provides quick and useful statistics for machine learning notebooks.

nvtop: A command-line monitor similar to top for real-time GPU diagnostics. PyTorch Lightning + Callbacks: Automates the recording of training performance and GPU usage during machine learning runs.

In enterprise environments, GPU monitoring should be integrated into DevOps practices, with automatic alerts and protective measures against temperature or usage anomalies.

NVIDIA’s Response to the RTX 5090 Crash Crisis

Faced with mounting complaints, NVIDIA officially acknowledged the crashing issue in a developer announcement. Firmware patches were released, and their immediate installation was recommended for RTX 5090 owners. However, this solution was not universally accepted.

Firmware Updates, Support Tickets, and Refunds

Some users have found that the firmware update caused the crash, especially when performed without a secure boot or with third-party software managers. NVIDIA’s Return Merchandise Authorization (RMA) process has also been criticized for being slow and selective: some data science users have had their warranty denied because their use case “exceeded the intended thermal range.”

Despite this, the company is working on hardware revisions for newer batches, and some major AI labs have reported faster replacements thanks to NVIDIA’s enterprise program.

RTX 5090 5090D Bricked Issues FAQs

Why does my RTX 5090/5090D freeze and how can I prevent it?

Your RTX 5090 may freeze due to firmware bugs, overheating, or a hardware design flaw that occurred during early production. This freeze typically happens when the card fails to fully initialize and often displays no system information or detects any components. To prevent this:

Avoid overclocking unless the temperature is perfectly under control.

Regularly monitor temperatures using tools like nvidia-smi or nvtop.

Wait until firmware updates have been thoroughly tested before installing them.

Ensure you have a stable and uninterrupted power supply during driver or BIOS updates.

Proactive diagnostics can save you thousands of euros and prevent significant downtime, especially if you use your GPU for data science or AI applications.

Can blocking issues be resolved, or is replacement the only solution?

Once a GPU is completely blocked (i.e., it is neither published nor detected by the system), recovery is extremely difficult without specialized tools. While some tech-savvy users attempt to reinstall the BIOS using SPI programmers, this is strongly discouraged except for highly experienced users.

For most users, a Return Merchandise Authorization (RMA) is the only viable option. However, ensure that:

You have not voided the warranty by overclocking.

Your cooling system meets the manufacturer’s specifications.

You can provide diagnostic logs, if possible.

It is always recommended to back up the firmware before any update. Prevention is better than cure.

Is it safer to use cloud GPUs rather than the RTX 5090 for AI workloads?

Yes, cloud GPUs offer a more stable and scalable environment for AI workloads, especially when GPU hardware like the RTX 5090 has reliability issues. With platforms such as:

AWS EC2 P4d/P5 instances
Google Cloud TPU/GPU offerings
NVIDIA DGX Cloud

You benefit from guaranteed uptime, rapid deployment, and automated monitoring. While cloud solutions are more expensive in the long run, they offer undeniable peace of mind, especially for large-scale training projects where a service interruption can have disastrous consequences.

The cloud also eliminates the constraints of hardware management, making it ideal for teams focused exclusively on developing and deploying models.