AMD just saved older Radeon cards


When Nvidia announced that it’d be letting older GPUs gain access to DLSS 4, all eyes were on AMD to do the same. Previously, both GPU makers locked their most precious AI-driven frame generation and upscaling tech behind a paywall of sorts: you had to own a GPU from the latest generation in order to try them out.

It didn’t take long for AMD to follow suit, and follow suit it did, with an announcement that just gave older graphics cards a whole new lease on life. With GPU prices through the roof, I dare say this announcement couldn’t have come at a better time.

AMD is bringing FSR 4.1 to older Radeon cards

It’s not just RDNA 4 anymore

In a recent announcement, AMD’s Jack Huynh revealed that the chipmaker would be bringing its latest FSR 4.1 Upscaling to older RDNA GPUs. Many fans were awaiting this announcement, especially since it was revealed that Nvidia did something along those lines with DLSS 4. More than that, a modder was able to get FSR 4 Frame Generation to work on RDNA 3 last year, so users knew it was technically possible. (It was just a matter of “when,” or so we all hoped.)

Until now, FSR 4 was one of the big reasons to look at AMD’s RDNA 4 graphics cards. It marked AMD’s shift to ML-powered upscaling, which meant better image stability, sharper details, and fewer artifacts.

Now, with FSR 4.1 coming to RDNA 3 (RX 7000) in July, and even RDNA 2 in Q1 of next year, gamers who are still running older hardware have a lot to look forward to. A free update that genuinely boosts performance? Yes please. Better yet, AMD says it’ll work across a suite of over 300 supported games at launch, so this isn’t some kind of proof-of-concept. It should have a real impact for many users.

Some questions still remain unanswered, such as how much of the larger FSR suite will be coming to those older GPUs. Unlike AMD’s latest cards, RDNA 3 and 2 cards don’t have AMD’s second-gen FP8 AI accelerators. Without the right hardware, we might not see the full Redstone stack in play, but even just FSR 4.1 upscaling is a huge win.

Quiz
8 Questions · Test Your Knowledge

Greatest GPUs of all time
Trivia challenge

From the 3dfx Voodoo era to modern powerhouses — how well do you know the GPUs that shaped PC gaming history?

GPU HistoryPerformanceMilestonesHardwareBrands

The 3dfx Voodoo2 was a landmark GPU of the late 1990s. What made the Voodoo2 particularly unique compared to most graphics cards of its era?

Correct! The Voodoo2 pioneered a technology 3dfx called SLI (Scan Line Interleave), which allowed two cards to work together by rendering alternating scan lines. This gave enthusiasts a meaningful performance boost and made multi-GPU setups a real consumer option for the first time.

Not quite. The standout feature of the Voodoo2 was its support for SLI (Scan Line Interleave), letting two cards work in tandem. This was a first for consumer graphics and made it the go-to card for serious PC gamers in 1998.

NVIDIA marketed the GeForce 256, released in 1999, with a bold and historic claim. What was that claim?

Correct! NVIDIA coined the term ‘GPU’ specifically to market the GeForce 256, touting its ability to perform hardware Transform & Lighting (T&L) directly on the graphics chip. Before this, T&L calculations were handled by the CPU, so this was a genuine architectural leap.

Not quite. NVIDIA invented the term ‘GPU’ for the GeForce 256, which was the first consumer card to handle Transform and Lighting calculations in hardware on the chip itself. It shifted a major workload off the CPU and changed how games were built going forward.

The NVIDIA GeForce 3 (released in 2001) introduced programmable shaders to consumer graphics. Which major gaming title was closely associated with its launch and helped showcase its capabilities?

Correct! Halo: Combat Evolved was originally shown running on the GeForce 3 hardware at Macworld 2000 when it was still a Mac and PC title, showcasing the card’s programmable vertex and pixel shaders. The GeForce 3 brought DirectX 8-class features to consumers for the first time.

Not quite. Halo: Combat Evolved was famously demonstrated on GeForce 3 hardware during its early reveal, helping showcase the card’s then-revolutionary programmable shader capabilities. The GeForce 3 was a landmark card that introduced DirectX 8 features to the consumer market.

The ATI Radeon 9700 Pro, released in 2002, is widely considered one of the most impactful GPU launches ever. What DirectX feature class did it introduce to the consumer market?

Correct! The Radeon 9700 Pro was the first consumer GPU to fully support DirectX 9 and Shader Model 2.0, and it did so while also significantly outperforming NVIDIA’s competing cards at the time. It’s often cited as one of the greatest GPU launches in history due to its combination of features, performance, and value.

Not quite. The Radeon 9700 Pro was the first consumer card to bring full DirectX 9 and Shader Model 2.0 support to the market, leapfrogging NVIDIA’s lineup. It was so well-received that it’s still talked about as one of ATI’s — and the industry’s — greatest GPU launches ever.

The NVIDIA GeForce GTX 1080 Ti, released in 2017, became a legendary card for enthusiasts. Approximately how much GDDR5X video memory did it feature?

Correct! The GTX 1080 Ti shipped with 11 GB of GDDR5X memory on a 352-bit bus, which was remarkably generous for a consumer card at the time. Combined with its Pascal architecture, it delivered near-Titan X performance at a lower price and remained competitive for years after its launch.

Not quite. The GTX 1080 Ti packed 11 GB of GDDR5X memory, which was unusually large for a consumer-grade card in 2017. This generous VRAM buffer, paired with its powerful Pascal architecture, is a big reason it remained relevant and beloved by gamers for so many years.

3dfx Interactive, maker of the legendary Voodoo series, eventually went out of business. Which company acquired 3dfx’s assets and intellectual property in 2002?

Correct! NVIDIA purchased 3dfx’s assets, patents, and intellectual property in late 2000, with the deal finalized around 2002. This acquisition eliminated one of NVIDIA’s most formidable rivals and gave NVIDIA access to 3dfx’s engineering talent and SLI patents, which NVIDIA later revived under its own branding.

Not quite. It was NVIDIA that snapped up 3dfx’s assets and patents after the company collapsed. The acquisition was a pivotal moment in GPU history, removing a major competitor and handing NVIDIA the SLI technology it would later resurrect for its own multi-GPU platform.

AMD’s Radeon RX 480, launched in 2016, caused a stir in the budget GPU market. What was its approximate launch price that made it so disruptive?

Correct! The RX 480 launched at just $199 for the 4 GB model (with the 8 GB model at $229), delivering performance that rivaled cards costing significantly more. It brought strong 1080p gaming performance to a mainstream price point and is credited with forcing NVIDIA to be more competitive in the mid-range segment.

Not quite. AMD priced the RX 480 at $199 for the 4 GB version, which caused a sensation because it punched well above its weight class. The card’s aggressive pricing pressured the entire market and is still remembered as a big win for consumers looking for affordable 1080p gaming performance.

NVIDIA’s RTX 20-series, launched in 2018, introduced real-time ray tracing to consumer GPUs. What is the name of the dedicated processing unit on these cards responsible for accelerating ray tracing calculations?

Correct! The Turing architecture introduced dedicated RT cores specifically designed to accelerate Bounding Volume Hierarchy (BVH) traversal and ray-triangle intersection calculations — the most computationally expensive parts of ray tracing. Without these dedicated units, real-time ray tracing at playable frame rates would not have been practical.

Not quite. NVIDIA built dedicated RT cores into its Turing-based RTX cards to handle the heavy math behind real-time ray tracing. While the cards also feature Tensor cores for AI-based tasks like DLSS, it’s the RT cores that are purpose-built for accelerating ray and triangle intersection calculations.

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This boosts one of AMD’s strongest selling points

AMD never really struggled with value

Closeup of graphics card in ITX case Credit: Ismar Hrnjicevic / How-To Geek

AMD’s Radeon graphics cards have long been the value pick when compared to Nvidia. In this generation, AMD didn’t even attempt to vie for the top dog position against Nvidia. Instead, it targeted the mainstream market with the RX 9060 XT and the RX 9070 XT.

Pricing has fluctuated on the flagship card, but in the last few months, that’s been due to the ongoing RAM-pocalypse. Although more expensive than the MSRP implies, the RX 9070 XT offers better value than Nvidia in pure raster performance.

Nvidia often wins on ray tracing, AI features, and DLSS in general, but AMD has always had a habit of making raw performance and VRAM look attainable. Adding FSR 4.1 to these older cards, which still pack a lot of VRAM and plenty of performance (looking at you, RX 7900 XTX), plays right into AMD’s strongest selling points.

GIGABYTE Gaming Radeon RX 9070 XT 16GB.

Brand

GIGABYTE

Cooling Method

Active

AMD’s RDNA 4 graphics cards get the full benefit of FSR 4.1, and the RX 9070 XT is the flagship card. It can rival the RTX 5070 Ti at a much lower price point.


How does this compare to Nvidia’s updates?

Let’s address the elephant in the room

Nvidia GeForce RTX logo on a 4070 Ti gaming GPU. Credit: Justin Duino / How-To Geek

Nvidia was the first to lock DLSS frame gen to a certain generation, but AMD followed with RDNA 4, restricting FSR 4 to RDNA 4. To that end, Nvidia improved things for those who are still running older RTX cards, but the lineup and software availability is still quite fragmented (and confusing for some).

DLSS frame generation is still locked to RTX 40-series and newer, and MFG is an RTX 50-series exclusive. Meanwhile, performance on older cards hasn’t always been stellar, although most users still see the update as a net gain (which it certainly is).

That puts AMD in an interesting spot. FSR 4.1 coming to RDNA 3 and RDNA 2 makes the comparison a whole lot less one-sided than it used to be. If you own an older Radeon GPU, AMD is giving you a reason to hold on to it a little longer instead of shelling out money on upgrades. That’s pretty neat.


The side of the EVGA NVIDIA GeForce GTX 970 SSC GAMING ACX 2.0 graphics card sitting on a desk.


The ‘fake frames’ era: Why DLSS 4.5 is just a crutch for unoptimized AAA games

Upscaling and frame generation revolutionized gaming, but it’s not all good news.

This could make used and renewed Radeon GPUs a lot more tempting

This is music to my ears in these market conditions

The AMD 7900 XTX. Credit: AMD

AMD’s RDNA 3 and RDNA 2 cards still offer a lot of value, and with this new update, their prices might actually rise, so if you’re planning to buy one, now is the time.

Cards like the RX 7800 XT and the RX 7700 XT remain a solid option for AAA gaming, especially with FSR 4.1 upscaling on the horizon. Their prices on Amazon aren’t too bad, either. You can buy the RX 7800 XT for close to $550, making it a solid $150-$200 cheaper than the RX 9070 XT.

The RX 7900 XTX held up well in price, which makes it a trickier pick. It still goes for over $1,100. Although it packs more RAM than the RX 9070 XT, and actually beats it in rasterization, the extra $400 makes it a tough sell.

One way or another, digging into older AMD GPUs is once again super relevant, and that’s always nice to see.


This is what long-term GPU support should look like

This is a good move, and one I hope we see more of from both AMD and Nvidia. New GPU features don’t have to come to every old card forever, but when the hardware is still very capable, pushing upgrades via software gets tricky. FSR 4.1 may not make RDNA 3 and RDNA 2 brand new again, but it’ll make these cards a lot easier to recommend even in 2026.



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ZDNET’s key takeaways

  • Trusted quality data is the backbone of agentic AI.
  • Identifying high-impact workflows to assign to AI agents is key to scaling adoption.
  • Scaling agentic AI starts with rethinking how work gets done. 

Gartner forecasts that worldwide AI spending will total $2.5 trillion in 2026, a 44% year-over-year increase. Spending on AI platforms for data science and machine learning will reach $31 billion, and spending on AI data will reach $3 billion.

The global agentic AI market will reach $8.5 billion by the end of 2026 and nearly $40 billion by 2030, per Deloitte Digital. Organizations are rapidly accelerating their adoption of AI agents, with the current average utilization standing at 12 agents per organization, according to MuleSoft 2026 research. This rate is projected to increase by 67% over the next two years, reaching an average of 20 AI agents. 

Also: How to build better AI agents for your business – without creating trust issues

According to IDC, by 2026, 40% of all Global 2000 job roles will involve working with AI agents, redefining long-held traditional entry, mid, and senior level positions. But the journey will not be smooth. By 2027, companies that do not prioritize high-quality, AI-ready data will struggle to scale generative AI and agentic solutions, resulting in a 15% loss in productivity. While 2025 was the year of pilot experiments and small production deployments of agentic AI, 2026 is shaping up to be the year of scaling agentic AI. And to scale agentic AI, according to IDC’s forecast, companies will need trustworthy, accessible, and quality data. 

Scaling agentic AI adoption in business requires a strong data foundation, according to McKinsey research. Businesses can create high-impact workflows by using agents, but to do so, they must modernize their data architecture, improve data quality, and advance their operating models. 

McKinsey found that nearly two-thirds of enterprises worldwide have experimented with agents, but fewer than 10% have scaled them to deliver measurable value. The biggest obstacle to scaling agent adoption is poor data — eight in ten companies cite data limitations as a roadblock to scaling agentic AI. 

Also: AI agents are fast, loose, and out of control, MIT study finds

McKinsey identified the top data limitations as primary constraints that companies face when scaling AI, including: operating model and talent constraints, data limitations, ineffective change management, and tech platform limitations. 

Data is the backbone of agentic AI

Research shows that agentic AI needs a steady flow of high-quality, trusted data to accurately automate complex business workflows. Successful agentic AI also depends on a data architecture that can support autonomy — executing tasks without human intervention. 

Two agentic usage models are emerging: single-agent workflows (one agent using multiple tools) and multi-agent workflows (specialized agents collaborate). In each case, agents will rely on access to high-quality data. Data silos and fragmented data would lead to errors and poor agentic decision-making. 

Four steps for preparing your data 

McKinsey identified four coordinated steps that connect strategy, technology, and people in order to build strong foundational data capabilities. 

Also: Prolonged AI use can be hazardous to your health and work: 4 ways to stay safe

  1. Identify high-impact workflows to ‘agentify’. Focus on highly deterministic, repetitive tasks that deliver value as strong candidates for AI agents. 

  2. Modernize each layer of the data architecture for agents. The focus on modernization should support interoperability, easy access, and governance across systems. The vast majority of business applications do not share data across platforms. According to MuleSoft research, organizations are rapidly adopting autonomous systems. The average enterprise now manages 957 applications — rising to 1,057 for those furthest along in their agentic AI journey. Only 27% of these applications are currently connected, creating a significant challenge for IT leaders aiming to meet their near-term AI implementation goals. 

  3. Ensure that data quality is in place. Businesses must ensure that both structured and unstructured data, as well as agent-generated data, meet consistent standards for accuracy, lineage, and governance. Access to trusted data is a key obstacle. IT teams now spend an average of 36% of their time designing, building, and testing new custom integrations between systems and data. Custom work will not help scale AI adoption. The most significant obstacle to successful AI or AI agent deployment is data quality, cited as the top concern by 25% of organizations. Furthermore, almost all organizations (96%) struggle to use data from across the business for AI initiatives.  

  4. Build an operating and governance model for agentic AI. This is about rethinking how work gets done. Human roles will shift from execution to supervision and orchestration of agent-led workflows. In a hybrid work environment, governance will dictate how agents can operate autonomously in a trustworthy, transparent, and scaled manner. 

The work assigned to AI agents 

McKinsey highlighted the importance of identifying a few critical workflows that would be candidates for AI agents to own. To begin, an end-to-end workflow mapping would help identify opportunities for agentic use. McKinsey found that AI adoption is led by customer service, marketing, knowledge management, and IT. It is important to identify clear metrics that validate impact. Teams should identify the data that can be reused across tasks and workflows.

Also: These companies are actually upskilling their workers for AI – here’s how they do it

McKinsey concludes that having access to high-quality data is a strategic differentiator in the agentic AI era. Because agents will generate enormous amounts of data, data quality, lineage, and standardization will be even more important in the agentic enterprise. And as agentic systems scale, governance becomes the primary level for control. The data foundation will be the competitive advantage in the agentic era. 





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