Nvidia quietly unveiled a new artificial intelligence model on Tuesday that outperforms offerings from industry leaders OpenAI and Anthropic, marking a significant shift in the company’s AI strategy and potentially reshaping the competitive landscape of the field.

The model, named Llama-3.1-Nemotron-70B-Instruct, appeared on the popular AI platform Hugging Face without fanfare, quickly drawing attention for its exceptional performance across multiple benchmark tests.

Nvidia reports that their new offering achieves top scores in key evaluations, including 85.0 on the Arena Hard benchmark, 57.6 on AlpacaEval 2 LC, and 8.98 on the GPT-4-Turbo MT-Bench.

These scores surpass those of highly regarded models like OpenAI’s GPT-4o and Anthropic’s Claude 3.5 Sonnet, catapulting Nvidia to the forefront of AI language understanding and generation.

Nvidia’s AI gambit: From GPU powerhouse to language model pioneer

This release represents a pivotal moment for Nvidia. Known primarily as the dominant force in graphics processing units (GPUs) that power AI systems, the company now demonstrates its capability to develop sophisticated AI software. This move signals a strategic expansion that could alter the dynamics of the AI industry, challenging the traditional dominance of software-focused companies in large language model development.

Nvidia’s approach to creating Llama-3.1-Nemotron-70B-Instruct involved refining Meta’s open-source Llama 3.1 model using advanced training techniques, including Reinforcement Learning from Human Feedback (RLHF). This method allows the AI to learn from human preferences, potentially leading to more natural and contextually appropriate responses.

With its superior performance, the model has the potential to offer businesses a more capable and cost-efficient alternative to some of the most advanced models on the market.

The model’s ability to handle complex queries without additional prompting or specialized tokens is what sets it apart. In a demonstration, it correctly answered the question “How many r’s are in strawberry?” with a detailed and accurate response, showcasing a nuanced understanding of language and an ability to provide clear explanations.

What makes these results particularly significant is the emphasis on “alignment,” a term in AI research that refers to how well a model’s output matches the needs and preferences of its users. For enterprises, this translates into fewer errors, more helpful responses, and ultimately, better customer satisfaction.

How Nvidia’s new model could reshape business and research

For businesses and organizations exploring AI solutions, Nvidia’s model presents a compelling new option. The company offers free hosted inference through its build.nvidia.com platform, complete with an OpenAI-compatible API interface.

This accessibility makes advanced AI technology more readily available, allowing a broader range of companies to experiment with and implement advanced language models.

The release also highlights a growing shift in the AI landscape toward models that are not only powerful but also customizable. Enterprises today need AI that can be tailored to their specific needs, whether that’s handling customer service inquiries or generating complex reports. Nvidia’s model offers that flexibility, along with top-tier performance, making it a compelling option for businesses across industries.

However, with this power comes responsibility. Like any AI system, Llama-3.1-Nemotron-70B-Instruct is not immune to risks. Nvidia has cautioned that the model has not been tuned for specialized domains like math or legal reasoning, where accuracy is critical. Enterprises will need to ensure they are using the model appropriately and implementing safeguards to prevent errors or misuse.

The AI arms race heats up: Nvidia’s bold move challenges tech giants

Nvidia’s latest model release signals just how fast the AI landscape is shifting. While the long-term impact of Llama-3.1-Nemotron-70B-Instruct remains uncertain, its release marks a clear inflection point in the competition to build the most advanced AI systems.

By moving from hardware into high-performance AI software, Nvidia is forcing other players to reconsider their strategies and accelerate their own R&D. This comes on the heels of the company’s introduction of the NVLM 1.0 family of multimodal models, including the 72-billion-parameter NVLM-D-72B.

These recent releases, particularly the open-source NVLM project, have shown that Nvidia’s AI ambitions go beyond just competing—they are challenging the dominance of proprietary systems like GPT-4o in areas ranging from image interpretation to solving complex problems.

The rapid succession of these releases underscores Nvidia’s ambitious push into AI software development. By offering both multimodal and text-only models that compete with industry leaders, Nvidia is positioning itself as a comprehensive AI solutions provider, leveraging its hardware expertise to create powerful, accessible software tools.

Nvidia’s strategy seems clear: it’s positioning itself as a full-service AI provider, combining its hardware expertise with accessible, high-performance software. This move could reshape the industry, pushing rivals to innovate faster and potentially sparking more open-source collaboration across the field.

As developers test Llama-3.1-Nemotron-70B-Instruct, we’re likely to see new applications emerge across sectors like healthcare, finance, education, and beyond. Its success will ultimately depend on whether it can turn impressive benchmark scores into real-world solutions.

In the coming months, the AI community will closely watch how Llama-3.1-Nemotron-70B-Instruct performs in real-world applications beyond benchmark tests. Its ability to translate high scores into practical, valuable solutions will ultimately determine its long-term impact on the industry and society at large.

Nvidia’s deeper dive into AI model development has intensified the competition. If this is the beginning of a new era in artificial intelligence, it’s one where fully integrated solutions may set the pace for future breakthroughs.

Elias Torres has achieved a lot for somebody who immigrated to the US from Nicaragua at 17 without knowing any English. He served as a VP of engineering at HubSpot before co-founding Drift, a company that sold to Vista Equity for about $1.2 billion in 2021.

“It’s very rare to get this far, but I’m not done,” Torres told TechCrunch.

About a year ago, Torres (pictured above) founded Agency, an AI-powered startup designed to automate tasks traditionally handled by customer success managers (CSMs). These professionals provide personalized support to users of complex B2B software, ranging from onboarding and training to upselling new features.

On Wednesday, Agency is coming out of stealth and announcing that it raised a $12 million seed round led by Sequoia and HubSpot Ventures.

The idea for Agency was born when Torres started consulting for OpenAI in early 2023. The ChatGPT maker asked Torres for help developing AI solutions for some of their enterprise customers, including NBA and LiveNation. In the course of doing that, it occurred to Torres that companies could benefit from AI customer success managers.

He was encouraged to build a startup around this concept when he met with Brian Halligan, co-founder and executive chairman of HubSpot. “We worked together on the CRM at HubSpot, and he told me, ‘Let’s build something great together again,’” Torres said about his conversation with Halligan. (Halligan joined Agency’s board.)

Shortly after meeting with Halligan, Torres reached out to Sequoia partner Pat Grady, who had previously invested in Drift. Grady was instantly sold on the idea.

“It’s hard to hire great CSMs. It’s hard to scale great CSMs,” Grady told TechCrunch. “If you have a product that can do a lot of the work on their behalf, and you can scale your company without having to hire an army of CSMs. That’s pretty useful.”

Agency can free up time in the customer success manager’s workday by handling tasks such as scheduling, follow-ups, note-taking, customer onboarding, and meeting preparation.

Torres explained that Agency’s AI gains a deep understanding of each customer from emails, CRM data, chat messages, and phone conversations, which allows it to anticipate customer needs at any point.

“This is something that we’ve been dreaming about for a long time,” he said, adding that this was the goal of Salesforce, the CRM he helped build at HubSpot and Drift, which was building personalized conversations for salespeople. “We didn’t have the technology to do this until now.”

The company’s product is currently being tested with companies, including HeyGen, and is available in an invite-only beta for customer success professionals.

While Agency seems to have no direct competitors at present, another function within the sales and marketing organization, sales development representative, is facing disruption from dozens of AI-powered solutions.

“I don’t know anybody else going after this market,” Sequoia’s Grady said. “Hopefully people won’t discover that for a while, and they’ll have a little bit of room to run.”

The ruling, which Google has appealed, would force Google to distribute third-party app stores within Google Play, no longer require Google Play Billing for apps distributed via Google Play, and more, with many of those changes ordered to begin on November 1st — just over two weeks from today.

But echoing many of Google’s arguments during the district court case, which Judge Donato rejected as insufficient, the company now argues that the order “threatens Google Play’s ability to provide a safe and trusted user experience.”

“This wouldn’t just hurt Google – this would have negative consequences for Android users, developers and device manufacturers who have built thriving businesses on Android, writes Google’s Lee-Anne Mulholland, VP of regulatory affairs, in a fact sheet distributed to journalists.

The fact sheet is bulleted into five different sections, and the section headers give you an idea of Google’s objections:

To get a sense of Google’s actual filing with the court, here’s how it begins:

At the request of a single competitor, Epic Games, the District Court ordered extensive redesigns to Play that will expose 100-million-plus U.S. users of Android devices to substantial new security risks and force fundamental changes to Google’s contractual and business relationships with hundreds of thousands of Google partners. The court gave Google just three weeks to make many of these sweeping changes—a Herculean task creating an unacceptable risk of safety and security failures within the Android ecosystem.

You can read the whole fact sheet, and Google’s whole emergency motion, below.

ChatGPT became much more collaborative when OpenAI released Canvas mode for the AI chatbot earlier in October. Switching to Canvas mode provides a more flexible way to create and edit text. Through AI’s code-writing ability, it enables more complex, long-term planning with visualization, spot-editing, and even automation.

Despite OpenAI’s bragging about how practical this approach to ChatGPT can be, you might stare at that prompt and ask how to use ChatGPT’s Canvas mode to enhance your daily life.

SpaceX achieved a spectacular first on Sunday when it used a pair of giant mechanical arms to catch the 70-meter-tall Super Heavy booster just minutes after it deployed the Starship spacecraft to orbit in the vehicle’s fifth test flight.

But SpaceX isn’t stopping there. As part of its efforts to create a fully reusable spaceflight system for the Starship — comprising the first-stage Super Heavy booster and the upper-stage Starship spacecraft — SpaceX will attempt to catch not only the booster, but also the spacecraft.

SpaceX CEO Elon Musk confirmed the plan for the world’s most powerful rocket in a post on X ( formally Twitter) on Wednesday, saying, “Hopefully early next year, we will catch the ship too.”

Before then, SpaceX will want to carry out more test flights of the Starship in which it will continue to catch the Super Heavy, while the Starship will continue to come down in the ocean, as it did in Sunday’s test flight.

Catching the Starship back at the launch base will allow for a faster turnaround time between launches, with the spacecraft only needing to be checked, refurbished, and refueled before being lifted atop a Super Heavy for another flight.

SpaceX also has to perfect a landing system for the Starship that involves it touching down on the ground in a vertical position, as this is how it will arrive on other celestial bodies such as the moon and possibly Mars (at least until any launch and landing infrastructure can be built).

It’s actually already achieved such a landing in Earth-based tests several years ago, but those touchdowns involved shorter “hops” into the atmosphere rather than more complex orbital flights.

It’s certainly an exciting time for SpaceX engineers as they put much of their attention into the continued development of the Starship.

NASA is planning to use SpaceX’s spacecraft to put two astronauts on the lunar surface in the Artemis III mission, which is currently scheduled for 2026, so there is much work to be done.

Both Xiaomi and Apple launched very compelling flagship smartphones this year. In this article, we’ll compare the two. It’s the comparison between the Xiaomi 14 Ultra vs Apple iPhone 16 Pro Max. These two phones are actually quite different in every way. Their internals are far different, and so are their designs. They are both large-format smartphones, though, and do have some things in common.

The Xiaomi 14 Ultra arrived back in February this year, while the iPhone 16 Pro Max followed in December. Both phones are available globally, and both of them are actually quite pricey. Comparing them makes all the sense in the world. We’ll first list their specifications, and will then move to a number of other categories, including design, display, performance, battery, cameras, and audio.

Specs

Xiaomi 14 Ultra vs Apple iPhone 16 Pro Max, respectively

– Screen size:
6.73-inch LTPO AMOLED display (curved, adaptive 120Hz, 3,000 nits max brightness)
6.9-inch LTPO Super Retina XDR OLED ( flat, 120Hz, HDR, 2,000 nits)
– Display resolution:
3200 x 1440
2868 x 1320
– SoC:
Qualcomm Snapdragon 8 Gen 3
Apple A18 Pro (3nm)
– RAM:
16GB (LPDDR5X)
12GB/16GB (LPDDR5X)
– Storage:
512GB (UFS 4.0)
256GB/512GB/1TB (NVMe)
– Rear cameras:
50MP (wide, f/1.6-f/4.0 variable aperture, OIS, multi-directional PDAF, 1.6um pixel size), 50MP (ultrawide, f/1.8 aperture, 122-degree FoV, 0.7um pixel size, dual pixel PDAF), 50MP (telephoto, f/1.8 aperture, 0.7um pixel size, dual pixel PDAF, OIS, 3.2x optical zoom), 50MP (periscope telephoto, f/2.5 aperture, 0.7um pixel size, dual pixel PDAF, OIS, 5x optical zoom)
48MP (wide, f/1.8 aperture, 1/1.28-inch sensor, 1.22um pixel size, sensor-shift OIS), 48MP (ultrawide, f/2.2 aperture, 0.7um pixel size, PDAF), 12MP (periscope telephoto, f/2.8 aperture, 1/3.06-inch sensor, 1.12um pixel size, 3D sensor-shift OIS, 5x optical zoom).
– Front cameras:
32MP (wide, f/2.0 aperture, 0.7um pixel size)
12MP (f/1.9 aperture, PDAF, 1/3.6-inch sensor size, OIS)
– Battery:
5,000mAh
4,685mAh
– Charging:
90W wired, 80W wireless,, 10W reverse wireless (charger included)
38W wired, 25W MagSafe, 15W Qi2 wireless, 7.5W Qi wireless, 4.5W reverse wired (charger not included)
– Dimensions:
161.4 x 75.3 x 9.2mm
163 x 77.6 x 8.3 mm
– Weight:
219.8 grams
227 grams
– Connectivity:
5G, LTE, NFC, Wi-Fi, USB Type-C, Bluetooth 5.4/5.3
– Security:
In-display fingerprint scanner & facial scanning
Face ID (3D facial scanning)
– OS:
Android 14 with HyperOS
iOS 18
– Price:
€1,499
$1,199+
– Buy:
Xiaomi 14 Ultra (Amazon)
Apple iPhone 16 Pro Max (Apple)

Xiaomi 14 Ultra vs Apple iPhone 16 Pro Max: Design

The Xiaomi 14 Ultra is made out of aluminum and glass. The thing is, there is a vegan leather model out there too, but only in China. There is also a variant with a titanium frame, but only in China. The iPhone 16 Pro Max, on the other hand, is made out of titanium and glass. There is only one variant in terms of build materials. The iPhone 16 Pro Max is slightly taller, wider, and thinner than the Xiaomi 14 Ultra. It’s heavier than the global variant of the Xiaomi 14 Ultra, but lighter than one of the models of Xiaomi’s flagship, it all depends. Both phones are set between 220 and 230 grams, though, so they’re not particularly light.

You’ll find flat sides on both of these phones, though the implementation is a bit different. The Xiaomi 14 Ultra’s back side is not completely flat, unlike what the iPhone 16 Pro Max offers. A flat display is included on both phones, though, along with thin bezels. The Xiaomi 14 Ultra has a display camera hole at the top of its display, while the iPhone 16 Pro Max includes a pill-shaped cutout known as Dynamic Island.

The Xiaomi 14 Ultra has its power/lock and volume rocker buttons on the right-hand side. Those are the only buttons included on the phone. The iPhone 16 Pro Max has two more. It includes its power/lock button on the right side, along with the Camera Control key. On the left, you’ll find the volume up and down buttons, and the Action Button. The Xiaomi 14 Ultra also has an IR blaster at the top.

You’ll notice a big camera oreo on the back of the Xiaomi 14 Ultra. Four cameras sit in there, and that camera island does protrude quite a bit on the back. The iPhone 16 Pro Max has a much smaller camera island in the top-left corner of its back. Three cameras sit on the inside. Both smartphones are IP68 certified for water and dust resistance. They’re both quite slippery too, though the vegan leather model of the Xiaomi 14 Ultra does add more grip.

Xiaomi 14 Ultra vs Apple iPhone 16 Pro Max: Display

The Xiaomi 14 Ultra features a 6.73-inch 3200 x 1440 LTPO AMOLED display. That panel is flat, and it supports an adaptive refresh rate of up to 120Hz. It also supports Dolby Vision and HDR10+ content. The peak brightness here is 3,000 nits, in theory, and the screen-to-body ratio is at around 89%. The display aspect ratio is 20:9, while the Xiaomi Shield Glass protects this display.

The iPhone 16 Pro Max, on the other hand, has a 6.9-inch 2868 x 1320 LTPO Super Retina XDR OLED display. This display is also flat, and it offers an adaptive refresh rate of up to 120Hz. HDR10 content is supported, as is HDR10 content, while the peak brightness is at 2,000 nits. The screen-to-body ratio sits at around 91%, while the display aspect ratio is 19.5:9. The Ceramic Shield glass protects this display.

Both of these displays are great. They’re both sharp, vivid, and have great viewing angles. They also get more than bright enough and have those inky blacks that people love. The Xiaomi 14 Ultra’s panel does get a bit brighter when needed. It also supports high-frequency PWM dimming, unlike the iPhone 16 Pro Max’s panel. Both displays do offer really good touch response, though. You’ll likely be more than happy with either one of these two panels.

Xiaomi 14 Ultra vs Apple iPhone 16 Pro Max: Performance

The Xiaomi 14 Ultra is fueled by the Snapdragon 8 Gen 3 processor, a 4nm chip. That is still Qualcomm’s best chip, even though its successor is coming later this month. That chip is backed with up to 16GB of LPDDR5X RAM and UFS 4.0 flash storage. The iPhone 16 Pro Max is fueled by the Apple A18 Pro processor, which is a 3nm chip. That processor is backed by 8GB of RAM and NVMe flash storage. Neither phone supports storage expansion, by the way.

Both of these processors are immensely powerful, as are both phones in general. In day-to-day use, no matter what you use them for, both smartphones deliver outstanding performance. They’re very snappy in every way, handling browsing, image editing, multimedia consumption, and everything else is not a problem. Getting both phones to stutter is not an easy task, actually.

The same can be said for gaming. They can play simpler games without a problem, and the same goes for semi-demanding and truly graphically-demanding games. Both of these phones can handle even the most demanding games on their respective app stores without a problem. They both get warm, but not too warm nor does that affect their gaming performance. Genshin Impact, for example, is not a problem for either phone.

Xiaomi 14 Ultra vs Apple iPhone 16 Pro Max: Battery

A 5,000mAh battery sits inside the global Xiaomi 14 Ultra model. The Chinese variant does include a 5,300mAh unit, but that’s not the one we used. The iPhone 16 Pro Max features a 4,685mAh battery. Apple’s iPhones always have smaller battery packs than their Android counterparts, mainly due to the differences in how iOS and Android function. That doesn’t have to mean that the iPhone 16 Pro Max has worse battery life. And in this case, it does not.

The Xiaomi 14 Ultra has great battery life in its own right, but the iPhone 16 Pro Max shades it in that regard. Both smartphones can go up to 7 hours of screen-on-time, and then some. The iPhone 16 Pro Max always has more battery juice left at that point, quite a bit more. We were able to push it way past that point. In all honesty, the Xiaomi 14 Ultra can also go higher than that, but it cannot keep up with the iPhone 16 Pro Max.

Do note that we gaming does affect battery life quite a bit, as do other demanding tasks. Even when we did play games during the day, both of these phones were able to go the distance. Their battery life is so good that even demanding users will be pleased, though your mileage may vary, of course.

When it comes to charging, the Xiaomi 14 Ultra shames the iPhone 16 Pro Max. It supports 90W wired, 80W wireless, and 10W reverse wireless charging. The iPhone 16 Pro Max supports 38W wired, 25W MagSafe wireless, 15W Qi2 wireless, 7.5W Qi wireless, and 4.5W reverse wired charging. The thing is, the Xiaomi 14 Ultra also comes with a charger, unlike the iPhone 16 Pro Max.

Xiaomi 14 Ultra vs Apple iPhone 16 Pro Max: Cameras

The Xiaomi 14 Ultra has four 50-megapixel cameras on the back. Its main 50-megapixel camera includes a 1-inch type sensor and variable aperture. A 50-megapixel ultrawide camera (122-degree FoV) is also included, as is a 50-megapixel telephoto camera (3.2x optical zoom). The last camera on the back is a 50-megapixel periscope telephoto unit (5x optical zoom). All those cameras use Leica lenses.

The iPhone 16 Pro Max, on the flip side, has a 48-megapixel main camera (1/1.28-inch sensor), a 48-megapixel ultrawide unit, and a 12-megapixel periscope telephoto camera (5x optical zoom). The thing is, all three cameras on the back of the iPhone 16 Pro Max have smaller sensors than their counterparts on the Xiaomi 14 Ultra. On top of that, the iPhone 16 Pro Max does not offer variable aperture.

Both smartphones are very capable in the camera department, though. They do offer considerably different results. The Xiaomi 14 Ultra shoots more contrasty shots that are closer to real life. The iPhone 16 Pro Max does love to use warmer color tones in images, and the images do look more processed in comparison. It’s all a matter of preference. We personally preferred shots from the Xiaomi 14 Ultra most of the time, and that is especially true for low light.

The Xiaomi 14 Ultra does do a better job with telephoto shots, especially those up to 5x. Their ultrawide cameras are about on par when it comes to performance. Both smartphones are very capable when it comes to macro photography, though we did prefer such photos from the iPhone 16 Pro Max. Apple’s flagship still has the upper hand in the video department.

Audio

Both of these smartphones include stereo speakers. Those speakers on both are more than loud enough, though the ones on the iPhone 16 Pro Max seem to be a bit louder. That’s not something everyone will notice, though. The sound quality is good from both phones.

Neither smartphone includes an audio jack, however. You can still use their Type-C ports for wired audio connections, though. Alternatively, the Xiaomi 14 Ultra offers Bluetooth 5.4, while the iPhone 16 Pro Max supports Bluetooth 5.3.