For years, the world of high-performance AI training has been a kingdom ruled by a single sovereign: NVIDIA. But the latest results from the MLPerf industry benchmark signal a powerful challenger is not just at the gates, it is breaching the walls. In a landmark submission, AMD showcased staggering performance with its Instinct MI300X GPUs that demonstrated not only record-breaking speed but a rapidly maturing software ecosystem. This isn’t just another incremental update; it’s a declaration that the AI hardware race is now a fiercely contested, two-horse race, promising a new era of innovation and competition for the entire industry.

Setting the Gold Standard with MLPerf

To understand the gravity of AMD’s achievement, one must first understand MLPerf. Organized by the MLCommons consortium, MLPerf is the Olympics of AI hardware and software. It provides a level playing field where companies like AMD, NVIDIA and Google submit their platforms to be tested on a suite of real-world, machine-learning training tasks. These benchmarks are the industry’s de facto gold standard for measuring performance, making them a critical barometer for enterprise customers deciding where to invest billions in their AI infrastructure. A strong showing here isn’t just about bragging rights; it’s about proving viability, performance and readiness for the most demanding AI workloads on the planet.

For this latest round, AMD submitted results for a standard server configuration featuring eight of its Instinct MI300X accelerators. This platform is AMD’s heavyweight contender, designed specifically to tackle the massive computational demands of training and deploying large language models (LLMs) and other generative AI systems.

Blistering Speed and Near-Perfect Scaling

The results speak for themselves. AMD’s submission demonstrated remarkable performance, particularly on some of the most popular and demanding models in use today. Using its robust ROCm 6 open software stack, the 8x MI300X server showed impressive “time-to-train” results, a critical metric that measures how quickly a system can train a model to a target level of accuracy.

One of the most significant takeaways was the platform’s scaling efficiency. In the world of AI, throwing more GPUs at a problem doesn’t guarantee a proportional increase in speed. The magic lies in making those GPUs work together seamlessly. AMD’s submission demonstrated near-linear scaling as it increased the number of GPUs, a testament to the maturity of its Infinity Fabric interconnect technology and the ROCm software. For enterprise customers, this is a crucial proof point: it means that as their AI needs grow, they can confidently scale their AMD-based infrastructure and expect predictable, powerful performance gains.

Expanding the Ecosystem with New Models

Perhaps more important than the raw performance numbers is what they were achieved on. For the first time, AMD submitted MLPerf results for training two of the industry’s most influential open-source models: Meta’s Llama 3 70B and Mistral’s Mixtral 8x7B. This is a massive strategic victory. For years, the primary barrier to AMD’s adoption in AI has not been the hardware itself, but the incumbency of NVIDIA’s CUDA software ecosystem. By demonstrating out-of-the-box, high-performance support for the very models that developers and businesses are clamoring to use, AMD is dismantling that barrier piece by piece.

This move signals that the ROCm software stack has reached a new level of maturity and usability that is capable of handling the complex demands of state-of-the-art models. It sends a clear message to the AI community that AMD is not just a hardware provider, but a serious platform contender committed to supporting the open-source models that are driving much of the innovation in generative AI.

The Head-to-Head with NVIDIA

Ultimately, every MLPerf submission is measured against the reigning champion. While direct, perfectly apples-to-apples comparisons are complex, the results place AMD’s MI300X in the same elite performance tier as NVIDIA’s formidable H100 GPUs. In several key benchmarks, AMD’s platform demonstrated performance that was not just competitive, but in some cases exceeded that of the NVIDIA submission in similar hardware categories.

This shatters the long-held perception that NVIDIA is the only viable option for cutting-edge AI training. It provides enterprise CIOs and AI researchers with what they have desperately needed: a choice. The ability to field a competitive bid from a second high-performance vendor introduces competition that can drive down prices, accelerate innovation and reduce the risks associated with being dependent on a single supplier.

Wrapping Up

AMD’s latest MLPerf results are a watershed moment for the AI industry. They represent the culmination of years of strategic investment in both silicon and software, resulting in a platform that can stand toe-to-toe with the best in the world. By delivering record-breaking performance, demonstrating near-perfect scaling and embracing the open-source models that define the modern AI landscape, AMD has proven it is no longer just an alternative but a true competitor for the AI throne. For a field that thrives on competition and innovation, this is unequivocally great news and heralds a future with more choice, better performance and faster progress for everyone.

Whether it’s a thumbnail-sized urban oasis or intended to get you through the zombie apocalypse (or something in between), keeping a home garden is a lot of fun—and a lot of work. It’s worth it, of course—aside from the natural beauty and the fresh veggies you can get from a personal garden, there are a host of physical, emotional, and mental health benefits associated with gardening. That assumes your garden is a success, of course.

If you have a brown thumb and your gardening efforts always turn into a floral graveyard, you might not be experiencing those benefits. If that describes you, the missing ingredient to your gardening triumph might be technology: There are a lot of apps and AI tools out there that can help you with everything from planning to maintaining your garden. If you’re struggling to keep things green, check out these apps and AI-powered tools you can use in your own home garden.

Planning tools

If you’ve never seriously gardened before, or just need some assistance planning it all out in terms of space, species, and layout, there are several tools that can make this process simpler for you:

• VegPlotter is a simple, straightforward app ideal for folks who find complex, feature-packed tools to be difficult to use. You create a simple layout of your garden, select what you’re growing there, and the app tracks a calendar of growth and harvest times for you. It will even warn you if you don’t have enough space for your chosen plants. Cost: Free for up to 40 plantings; $22 or $35 per year for more options.

• Planter is an alternative garden planner that’s easy to use—you just drag cute little pictures of plants onto a grid representing your garden. Planter automatically decides how many “squares” of your grid each plant needs, which makes it easier to lay everything out. Cost: Free, $24.99 per year, or $99 for a lifetime plan.

• Garden Planner is a good choice if you need to plan out more than just plants, as it allows you to include pathways, fencing, and other landscaping features in your garden plan. It’s not as user-friendly as VegPlotter or Planter, but it’s relatively straightforward. Cost: $48 after a 15-day free trial.

• Smart Gardener is a great tool if you want to feed yourself and your family from your own vegetable garden, as it will quiz you on how many people are in the household, what zones you live in, and other basic info to help you plan what to grow and when you’ll be able to harvest it. Cost: $15 for 90 days; $40 for a year.

Reference

• Seed to Spoon offers a lot of tools for planning and caring for a garden, including a robust companion plant reference that makes it stand out. The app shows you which plants will thrive together, making it simple to fill up your garden with a harmonious selection. Cost: Free version includes 10 free plants, $4.99 for 1 month, $24.99 for 6 months, and $46.99 for a year.

• PictureThis is a simple, but powerful, plant ID tool. With just a quick photo of any plant, you’ll find out what you’re looking at, an assessment of its relative health, and information about its care. This is a necessity if you’re taking over an existing garden, if you’re “ungardening,” or if you wake up to discover invader plants insinuating themselves into your planned space. Cost: Free.

Journaling

A garden is a living thing, and all living things change over time. Keeping a garden journal is a crucial aspect of gardening, as it lets you track what you’ve grown, what yields were like, and what works and what doesn’t in your specific little spot. While you can certainly keep track of all this using a variety of tools (some of the other apps listed here include this feature, and there’s always old-fashioned pen and paper), Gardenize is a simple garden journal tool that makes it easy to input information about your garden. It automatically timestamps everything and makes it easy to see the impact of your choices over time. Cost: $8.99 for one month, $3.74 per month for 12 months, or $6.66 per month for three months.

All-in-one

Sometimes you don’t want a bunch of apps that do specific things—you want a single app that does everything:

• Seedtime is a comprehensive gardening app that many gardeners swear by. It includes just about everything you need, from a harvest and planting calendar generated from a list of what you want to grow to planning, layout, and journaling tools. It even tracks your inventory of seeds, fertilizer, and other supplies and lets you know when they expire or need replenishing. Cost: Free for the limited version, $7 per month for a basic subscription, or $9 for an unlimited subscription.

• Growbot AI is a general AI tool—it’s basically ChatGPT but specifically for gardening. You can ask it anything you need to know and get an answer back in natural language, including specific numbers and timelines, if applicable. It’s only available on iOS, however, so if you’re an Android user, you might be stuck using a more generic AI chatbot for your questions. Cost: Free, or $29.99 annual subscription.

A group led by RSS co-creator Eckart Walther has launched a new protocol designed to standardize and scale licensing of online content for AI training. Backed by publishers like Reddit, Quora, Yahoo, and Medium, Real Simple Licensing (RSL) combines machine-readable terms in robots.txt with a collective rights organization, aiming to do for AI training data what ASCAP did for music royalties. However, it remains to be seen whether AI labs will agree to adopt it. TechCrunch reports: According to RSL co-founder Eckart Walther, who also co-created the RSS standard, the goal was to create a training-data licensing system that could scale across the internet. “We need to have machine-readable licensing agreements for the internet,” Walther told TechCrunch. “That’s really what RSL solves.”

For years, groups like the Dataset Providers Alliance have been pushing for clearer collection practices, but RSL is the first attempt at a technical and legal infrastructure that could make it work in practice. On the technical side, the RSL Protocol lays out specific licensing terms a publisher can set for their content, whether that means AI companies need a custom license or to adopt Creative Commons provisions. Participating websites will include the terms as part of their “robots.txt” file in a prearranged format, making it straightforward to identify which data falls under which terms.

On the legal side, the RSL team has established a collective licensing organization, the RSL Collective, that can negotiate terms and collect royalties, similar to ASCAP for musicians or MPLC for films. As in music and film, the goal is to give licensors a single point of contact for paying royalties and provide rights holders a way to set terms with dozens of potential licensors at once. A host of web publishers have already joined the collective, including Yahoo, Reddit, Medium, O’Reilly Media, Ziff Davis (owner of Mashable and Cnet), Internet Brands (owner of WebMD), People Inc., and The Daily Beast. Others, like Fastly, Quora, and Adweek, are supporting the standard without joining the collective.

Notably, the RSL Collective includes some publishers that already have licensing deals — most notably Reddit, which receives an estimated $60 million a year from Google for use of its training data. There’s nothing stopping companies from cutting their own deals within the RSL system, just as Taylor Swift can set special terms for licensing while still collecting royalties through ASCAP. But for publishers too small to draw their own deals, RSL’s collective terms are likely to be the only option.

Read more of this story at Slashdot.