As a landmark product in the AI field, the NVIDIA Blackwell series (B200/B300) has worn a halo since its launch, and the spectacle of selling out immediately after its release bears witness to its appeal in the industry.

However, as the hype fades, numerous hidden problems gradually surface: out-of-control overheating, liquid cooling leakage, scheduled downtime... A series of unexpected issues have put the heavy investments of many enterprises in an awkward position.

Whether you are planning to purchase or have already deployed it, this in-depth guide to avoiding pitfalls is worth keeping to help you mitigate risks and significantly reduce the cost of operation and maintenance pitfalls.

✅ B200/B300 (HGX/DGX): An 8-GPU small cluster, equivalent to a "Pro version of an e-sports desktop". It features air cooling, is easy to operate, and has almost no bugs. Small and medium-sized enterprises can go for it without any extra hassle;

❌ GB200/GB300 NVL72: A rack-level behemoth, packing 72 GPUs and 36 CPUs, it requires liquid cooling for temperature control. It looks insanely powerful on the surface, but in reality, it’s riddled with countless pitfalls, so beginners should steer clear!

1️⃣ Liquid Cooling Leakage 💧: A million-dollar device gets "soaked" directly, a huge loss warning!

Right after the GB200 was released, a huge scandal broke out: the liquid cooling system actually leaked! The coolant secretly seeped into the chip cores, rendering millions of devices useless in an instant, which was heartbreaking 😭. After thorough investigation, it turned out that the cooling pipeline was too complex and the sealing was not done properly,and this fatal bug was only barely fixed in 2025.

What's even worse is that this device has a standalone power consumption of up to 120kW, equivalent to 10 household air conditioners running at full blast at the same time, ordinary data centers simply can't handle it! To use it, you first have to spend millions to upgrade the infrastructure, if you don't have strong enough resources, anyone who buys it will regret it!

2️⃣ Unbalanced Computing Power 🤡: It’s Powerful, but It Cuts Out Mid-Run

To boost AI computing power, the B200 focuses all its efforts on upgrading the matrix operation module, yet it forgets to update the "special function unit" (the key component for processing AI attention mechanisms), which is a classic case of losing sight of one thing and neglecting another, with a severe lack of balance!

Put simply: Your legs can run 100 yards, but your lungs can't keep up—you're gasping for breath after just two steps! When dealing with complex AI models, the powerful modules are idling away, while the weak ones are overworked to a standstill, cutting the overall computing power in half. It's like spending a million on a half-broken product😅 Luckily, the B300 fills this gap, otherwise it would have been a total loss.

3️⃣ Scheduled Crashes⏰: CrashesEvery 66 Days, Wasting AI Training Efforts

The most ridiculous bug has struck! The B200 server with open-source drivers froze up completely after running continuously for 66 days and 12 hours, crashed and stopped working, all AI training tasks were lost, and all the late nights and money spent earlier were completely wasted!

After checking for a long time, I found out that it was actually the internal counter "maxing out" (overflow), just like an alarm going off and no one turning it off, which directly paralyzed the system! What's even more infuriating is that as of the first quarter of 2026, NVIDIA still hasn't fixed this bug, so we have to restart it every two months to keep it going. 🤯

4️⃣ Chip Warpage 🙄: Half of the Mass-Produced Chips Are Defective, and Supply Is Delayed Miserably

The B200 uses TSMC's advanced packaging technology, but the thermal expansion and contraction were not properly calculated during the design phase. The chip will deform slightly (warp) during operation, resulting in half of the chips being defective during mass production. Shipping was directly delayed by 3 months, much to the frustration of the enterprises that placed orders.

Jensen Huang even came out personally to admit fault, saying this was NVIDIA's fault and had nothing to do with Taiwan Semiconductor Manufacturing Company (TSMC). It wasn't until the chip architecture was re-optimized that supply resumed in early 2025. The enterprises that placed orders in advance had to wait for a full six months, missing out on plenty of opportunities!

5️⃣ Security Vulnerability 🔴: Hackers Can Tamper with AI Results at Will, Leaving Enterprises Terrified

A major vulnerability was exposed as early as 2025: Hackers rely on "repeated reading and writing of memory" (Rowhammer attack), which can reduce the AI inference accuracy of B200 from 80% to 0.1%; in 2026, it became even more vicious, directly upgraded to "GPUBreach", allowing hackers to directly take control of the entire server. Just thinking about it is terrifying!

Fortunately, the B200 has "Memory Protection Lock" (ECC) enabled by default, which can prevent such attacks, but at the cost of a 10% drop in computing power. You can't have your cake and eat it too: either go slower or risk being hacked. It's really tough for enterprises😭

✔ Hardware-level hidden dangers (liquid cooling leakage, overheating, chip warping) have been basically fixed, so there's no need to worry about device damage caused by hardware issues;

✔ The 66-day scheduled downtime bug remains unresolved. Key reminder: Do not use open-source drivers for servers equipped with B200.

✔ The B300 software ecosystem has been gradually improved, and mainstream frameworks such as PyTorch and TensorFlow have all been adapted and can be put into normal use;

AI inference scenarios for small and medium-sized enterprises (such as intelligent customer service, image recognition): Prioritize HGX B200 (8-card air-cooled version), which offers outstanding cost performance, minimal risks, stable operation without excessive investment, and is convenient and efficient.

Large-scale AI training scenarios for large enterprises: DGX B300 (8 GPUs) is recommended. Please avoid GB300 NVL72, as its software ecosystem is not yet mature. Blind investment will only bring more troubles and outweigh the benefits.

Supercomputing Centers and Top-Level Laboratories: If you insist on choosing GB300 NVL72, you need to invest millions in advance to renovate the data center and improve the liquid cooling and power supply systems. Otherwise, even if you purchase the equipment, it will be difficult to operate normally, resulting in unnecessary waste.

For long-term AI training scenarios, the H100 is still a more reliable choice, while the B series is more suitable for short-term inference tasks, and the selection must be accurately adapted to the needs.

It is undeniable that the computing power performance of B200/B300 is stunning, injecting strong impetus into the development of the AI field. However, before procurement, in addition to the additional investment required for infrastructure renovation, you must also stay alert to the risk of downtime.