NVIDIA

56 Articles

Laptop GPU Upgrade With Just A Little Reballing

October 2, 2024 by 5 Comments

Modern gaming laptops are in an uncomfortable spot – often too underpowered for newest titles, but too bulky to be genuinely portable. It doesn’t help they’re not often upgradeable, so you’re stuck with what you’ve bought – unless, say, you’re a hacker equipped some tools for PCB reflow? If that’s the case, welcome to [TechModLab]’s video showing you the process of upgrading a laptop’s soldered-on NVIDIA GPU, replacing the 3070 chip with a 3080.

You don’t need much – the most exotic tool is a BGA rework station, holding the mainboard steady&stiff and heating a specific large chip on the board with an infrared lamp from above. This one is definitely a specialty tool, but we’ve seen hackers build their own. From there, some general soldering tools like flux and solder wick, a stencil for your chip, BGA balls, and a $20 USB-C hotplate are instrumental for reballing chips – tools you ought to have.

Reballing was perhaps the hardest step of the journey – instrumental for preparing the GPU before the transplant. Afterwards, only a few steps were needed – poking a BGA ball that didn’t connect, changing board straps to adjust for the new VRAM our enterprising hacker added alongside the upgrade, and playing with the driver process install a little. Use this method to upgrade from a lower-end binned GPU you’re stuck with, or perhaps to repair your laptop if artifacts start appearing – it’s a worthwhile reminder about methods that laptop repair shops use on the daily.

Itching to learn more about BGAs? You absolutely should read this article series by our own [Robin Kearey]. We’ve mostly seen reballing used for upgrading RAM on laptop and Raspberry Pi boards, but seeing it being used for an entire laptop is nice – it’s the same technique, just scaled up, and you always can start by practicing at a smaller scale. Now, it might feel like we’ve left the era of upgradable GPUs on laptops, and today’s project might not necessarily help your worries – but the Framework 16 definitely bucks the trend.

Continue reading “Laptop GPU Upgrade With Just A Little Reballing” →

Hacking An NVIDIA CMP 170HX Crypto GPU For EM Sim Work

September 11, 2024 by 7 Comments

A few years back NVIDIA created a dedicated cryptocurrency mining GPU, the CMP 170HX. This was a heavily restricted version of its flagship A100 datacenter accelerator, using the same GA100 chip. It was intended for accelerating Ethash, the Etherium proof-of-work algorithm, and nothing else. [niconiconi] bought one to use for accelerating PCB electromagnetic simulations and put a lot of effort into repairing the card, converting it to water-cooling, and figuring out how best to use this nobbled GPU.

Typically, the GA100 silicon sits in the center of the mighty A100 GPU card and would be found in a server rack, cooled by forced air. This was not an option at home, so an off-the-shelf water-cooling block was wedged in. During this process, [niconconi] found that the board wouldn’t power on, so they went on a deep dive into the power supply tree with the help of a leaked A100 schematic. The repair and modifications can be found in the appendix, right down to the end of the article. It is a long read to get there.

Continue reading “Hacking An NVIDIA CMP 170HX Crypto GPU For EM Sim Work” →

A standard-compliant MXM card installed into a laptop, without heatsink

MXM: Powerful, Misused, Hackable

April 18, 2024 by 26 Comments

Today, we’ll look into yet another standard in the embedded space: MXM. It stands for “Mobile PCI Express Module”, and is basically intended as a GPU interface for laptops with PCIe, but there’s way more to it – it can work for any high-power high-throughput PCIe device, with a fair few DisplayPort links if you need them!

You will see MXM sockets in older generations of laptops, barebones desktop PCs, servers, and even automotive computers – certain generations of Tesla cars used to ship with MXM-socketed Nvidia GPUs! Given that GPUs are in vogue today, it pays to know how you can get one in low-profile form-factor and avoid putting a giant desktop GPU inside your device.

I only had a passing knowledge of the MXM standard until a bit ago, but my friend, [WifiCable], has been playing with it for a fair bit now. On a long Discord call, she guided me through all the cool things we should know about the MXM standard, its history, compatibility woes, and hackability potential. I’ve summed all of it up into this article – let’s take a look!

This article has been written based on info that [WifiCable] has given me, and, it’s also certainly not the last one where I interview a hacker and condense their knowledge into a writeup. If you are interested, let’s chat!

Continue reading “MXM: Powerful, Misused, Hackable” →

NVIDIA Trains Custom AI To Assist Chip Designers

November 11, 2023 by 16 Comments

AI is big news lately, but as with all new technology moves, it’s important to pierce through the hype. Recent news about NVIDIA creating a custom large language model (LLM) called ChipNeMo to assist in chip design is tailor-made for breathless hyperbole, so it’s refreshing to read exactly how such a thing is genuinely useful.

ChipNeMo is trained on the highly specific domain of semiconductor design via internal code repositories, documentation, and more. The result is a vast 43-billion parameter LLM running on a single A100 GPU that actually plays no direct role in designing chips, but focuses instead on making designers’ jobs easier.

For example, it turns out that senior designers spend a lot of time answering questions from junior designers. If a junior designer can ask ChipNeMo a question like “what does signal x from memory unit y do?” and that saves a senior designer’s time, then NVIDIA says the tool is already worth it. In addition, it turns out another big time sink for designers is dealing with bugs. Bugs are extensively documented in a variety of ways, and designers spend a lot of time reading documentation just to grasp the basics of a particular bug. Acting as a smart interface to such narrowly-focused repositories is something a tool like ChipNeMo excels at, because it can provide not just summaries but also concrete references and sources. Saving developer time in this way is a clear and easy win.

It’s an internal tool and part research project, but it’s easy to see the benefits ChipNeMo can bring. Using LLMs trained on internal information for internal use is something organizations have experimented with (for example, Mozilla did so, while explaining how to do it for yourself) but it’s interesting to see a clear roadmap to assisting developers in concrete ways.

Here’s Why GPUs Are Deep Learning’s Best Friend

September 3, 2023 by 20 Comments

If you have a curiosity about how fancy graphics cards actually work, and why they are so well-suited to AI-type applications, then take a few minutes to read [Tim Dettmers] explain why this is so. It’s not a terribly long read, but while it does get technical there are also car analogies, so there’s something for everyone!

He starts off by saying that most people know that GPUs are scarily efficient at matrix multiplication and convolution, but what really makes them most useful is their ability to work with large amounts of memory very efficiently.

Essentially, a CPU is a latency-optimized device while GPUs are bandwidth-optimized devices. If a CPU is a race car, a GPU is a cargo truck. The main job in deep learning is to fetch and move cargo (memory, actually) around. Both devices can do this job, but in different ways. A race car moves quickly, but can’t carry much. A truck is slower, but far better at moving a lot at once. Continue reading “Here’s Why GPUs Are Deep Learning’s Best Friend” →

A Dedicated GPU For Your Favorite SBC

May 5, 2023 by 35 Comments

The Raspberry Pi is famous for its low cost, versatile and open Linux environment, and plentiful I/O, making it a perfect device not only for its originally-intended educational purposes but for basically every hobbyist from gardeners to roboticists to amateur radio operators. Most builds tend to make use of the GPIO pins which allow easy connections to various peripherals and sensors, but the Pi also supports PCI devices which means that, in theory, it could use a GPU in much the same way that a modern computer would. After plenty of testing and development, [Jeff Geerling] brings us this custom graphics card interface for the Raspberry Pi.

The testing for all of these graphics cards has been done with a Pi Compute Module 4 and the end result is an interface device which looks much like a graphics card itself. It splits the PCI bus out onto a more familiar x16 slot connector and adds physical connections for power, USB, and Ethernet. When plugged into the carrier board, the Compute Module can be attached to any of a number of graphics cards, including the latest and highest-end of Nvidia and AMD offerings.

Perhaps unsurprisingly, though, the 4090 and 7900 cards don’t work with the Raspberry Pi. This is partially due to the 32-bit limitations of the Pi and other memory mapping issues, but even after attempting some workarounds Nvidia’s cards aren’t open-source enough to test properly (although the card is recognized by the Pi) and AMD’s drivers crash the system even after compiling a custom kernel. [Jeff] did find an Nvidia card that worked, although it requires using the USB interface and second-hand cards are selling for around $3000 USD. For a more economical choice there are some other graphics cards that he was eventually able to get working, albeit not with perfect performance, including some of the ones we’ve seen him test already.

Continue reading “A Dedicated GPU For Your Favorite SBC” →

The Tale Of The Final EVGA GPU Overclocking Record

January 23, 2023 by 12 Comments

It’s not news that EVGA is getting out of the GPU card game, after a ‘little falling out’ with Nvidia. It’s sad news nonetheless, as this enthusiastic band of hardware hackers has a solid following in certain overclocking and custom PC circles. The Games Nexus gang decided to fly over to meet up with the EVGA team in Zhonghe, Taiwan, and follow them around a bit as they tried for one last overclocking record on the latest (unreleased, GTX4090-based) GPU card. As you will note early on in the video, things didn’t go smoothly, with their hand-lapped GPU burning out the PCB after a small setup error. Continue reading “The Tale Of The Final EVGA GPU Overclocking Record” →