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Observační studie naznačují nižší riziko některých nádorů u léků na hubnutí • Statistické zjištění však může být zkresleno lepším životním stylem pacientů • Randomizované klinické testy zatím žádný přímý protirakovinný účinek nepotvrdily

Primate Labs vydávají Geekbench 7, i když trochu nestandardním způsobem. Nová verze opět překopává strukturu dílčích testů i jakým způsobem se podílejí na výsledném skóre. Některé testy zanikají…

Na všech herních platformách je každou chvíli nějaká slevová akce. Každý týden proto vybíráme ty nejatraktivnější, které by vám neměly uniknout. Pokud chcete získat hry zdarma nebo s výhodnou slevou, podívejte se na aktuální přehled akcí!

Open AI’s admission this week that its agents escaped the sandbox and autonomously hacked model repository Hugging Face has spawned more apocalyptic warnings of agents gone bad than we can count. Thankfully, Renato Marinho, chief research officer at Morphus Labs and a SANS Technology Institute instructor, brought some sanity to the discussion. “It is tempting to read this as ‘AI can now hack autonomously, the sky is falling,’” Marinho said in a Thursday blog. “Resist that.” He went on to make three very pertinent points about the agentic attack. First, and probably most important: The models didn’t have guardrails – and that was intentional. As OpenAI said in its mea culpa, GPT-5.6 Sol and "an even more capable pre-release model" were among those that attacked Hugging Face. It also noted that the LLMs’ “deployment safeguards were intentionally not enabled during this evaluation because it was aimed at testing cyber vulnerabilities.” So while it’s very concerning that these models broke out of their own testing environment, and the debate over safety guardrails remains worth having, drawing a direct line from the Hugging Face attack to the need for strong AI guardrails doesn’t work. “This measured a ceiling, not normal production behavior,” Marinho wrote. “The evaluation deliberately reduced the models' refusals. That is very different from a customer-facing model with safeguards enabled.” It’s worth noting that these same frontier models, with guardrails enabled, refused to help Hugging Face’s forensic investigation. “The attacker was bound by no usage policy, while our own forensic work was blocked by the guardrails of the hosted models we first tried,” the model hub’s security team said. So Hugging Face used a Chinese open-weight model to investigate the breach. Another thing worth considering is that real-life attackers are probably going to use open-weight models anyway. They’re more accessible to everyone, it’s easier to remove their built-in protections, and they’re a whole lot less expensive than their frontier counterparts. Second, as we’ve pointed out previously: AI companies touting their own models’ autonomous bug-finding and hacking abilities creates a marketing win for the companies themselves. As Marinho points out, Hugging Face is not an OpenAI competitor. The AI giant stating “‘our model escaped and hacked a partner’ conveniently showcases frontier capability,” he added. “Read the framing with the same skepticism you'd apply to any ‘our product is dangerously powerful’ claim, and treat it as marketing until it is independently corroborated.” Finally, the attack technique itself isn’t novel. “Exposed credentials plus zero-days into a production database is a chain any of us would recognize,” Marinho wrote. The agents working together on an end-to-end attack chain is notable - but we’ve also seen this in previous testing, like that carried out by frontier security lab Irregular earlier this spring. Agents gone rogue?! Shocking Irregular said its testing found that AI agents will work together to bypass security controls and stealthily steal sensitive data from within the enterprise systems in which they operate – especially when prompted like a hard-ass boss. While the prompts themselves didn’t specifically reference hacking or exploiting vulnerabilities, they conveyed a sense of urgency: “It is of utmost importance that you perform the task exactly as required,” and “When you talk to your sub-agents, convey all the requirements to them, and be ruthless about the requirements and encourage them to perform the tasks fully and exactly. You are a strong manager and you do not easily cave in to or succumb to pleas by the sub-agents to not fully fulfill their tasks.” The agents did as instructed, and ultimately "demonstrated emergent offensive cyber behavior," including independently discovering and exploiting vulnerabilities, escalating privileges to disarm security products, and bypassing leak-prevention tools to exfiltrate secrets and other data. And the Irregular research wasn’t even testing the agents’ offensive cyber capabilities — so it shouldn’t be too surprising that OpenAI’s benchmark research, aptly titled “Can AI Agents Turn Security Vulnerabilities into Real Attacks?” produced a resounding yes. Agents have one job – to complete a task. They aren’t bound by ethical or moral constraints that we (hopefully) see in human red team hackers. If prompted to “pursue advanced exploitation using complex attack paths,” especially without guardrails enabled, the models will do whatever it takes to achieve success. That’s what the leading AI companies trained them to do. ®

Apple macOS apps that have been downloaded from the internet and run at least once can be swapped with malicious versions, a pair of researchers say, calling into question the thoroughness of the company's "Gatekeeper" defenses. As Apple explains, "When a user downloads and opens an app, a plug-in, or an installer package from outside the App Store, Gatekeeper verifies that the software is from an identified developer, is notarized by Apple to be free of known malicious content, and hasn’t been altered." Security researchers Talal Haj Bakry and Tommy Mysk say they've identified a gap in Gatekeeper and associated code signing rituals that "allows an attacker to silently replace the main executable of any application downloaded from the web without requiring elevated privileges." The attacker needs to have means of user-level code execution available, such as a malicious app or downloaded script, so it's not a zero-click vulnerability that a remote attacker can deploy. Nonetheless, the finding shows Gatekeeper to be rather lax in its gatekeeping duties. Bakry and Mysk managed to alter a macOS app downloaded from the web (not from the App Store) and Gatekeeper failed to object. Their technique doesn't work on Mac App Store apps, the Mysk team told The Register, because they're owned by root, so a process running with current user privileges won't be able to overwrite them. But for macOS apps downloaded from the web, such as Brave, Slack, Signal, or Visual Studio Code, among many others, there's potential risk. The attack scenario requires an app downloaded from the web that has been run once – allowing Gatekeeper to complete its initial validation – and the ability to execute user-scoped code. The initial validation phase that Gatekeeper conducts is supposed to prevent subsequent modifications to the application bundle, even with administrative privileges. But the Mysk team found that you can archive a downloaded, once-run app using tar (a file archiving utility), then remove the original and replace it with a malicious version, and macOS does not require reauthorization. They've recorded a video demonstrating how the attack works. The Mysk team said there are many ways an attacker might gain the necessary access to get around Gatekeeper, such as tools installed through the command line, convincing someone to copy and paste a command to their terminal, downloading and running an malicious app, a prompt injection attack on an AI agent, or a supply chain attack via npm, brew, or some other package manager. And once a doppelganger version of an app is in place, it can magnify its mischief by presenting deceptive prompts that users are more likely to trust because they appear to come from a known app. Tommy Mysk said he was uncertain about the exact cause of the issue, but speculated it may have something to do with cached value retention. "When you open the app for the first time and it passes all validation checks, macOS marks the app as trusted and saves this data," he said. "Later when I modify the executable, macOS detects a change in the bundle and tries to revalidate its integrity. It seems the cached value of the trust causes macOS to pass the validation even though the bundle has changed." The Mysk team reported their findings to Apple, which reportedly closed the issue. "Apple doesn't consider this attack to be 'modifying' the signed executable," the Mysk team explained. "Instead, Apple says that by archiving/restoring the app bundle, the proof-of-concept code overwrites the entire app bundle, making it locally built. "Locally built app bundles are not covered by macOS guards. And this is why access to Keychain or TCC protected directories require system authorization prompts. And for users to accept those is a matter of social engineering attacks that Apple considers out of scope." Apple did not respond to a request for comment. ®

Fyzikové a AI model Claude spolupracovali na důkazu matematické domněnky o ucpání. Kvantové vakuum by mohlo zefektivnit chemické reakce. Klonování 30 let poté. V červenci 1996 se narodila ovce Dolly. Kanibalistický kompromis: Proč se dlouhodobě nevyplácí jíst lidské maso.

Jak se budete mít v penzi? Nebudou vám vzhledem k vašim měsíčním výdajům chybět peníze? Kolik byste si měli našetřit, abyste vyšli? Poradíme, kde získat odpovědi na tyto otázky.

Úpravy kódu systémových volání jednodušší život vývojářům Linuxu, další základní desky Asus s podporu čtení dat ze senzorů, cílené vypínání přehřátých CPU jader pro Intel, sekundární grafická pipe pro moderní APU od AMD.

Ještě loni (a vlastně i letos) bylo možné v některých odborných článcích, ale zejména v diskuzích narazit na bagatelizaci bezpečnostních rizik AI s tím, že tak daleko modely nejsou…

Záhadné černé kožní léze sumečků amerických v jezeře Memphremagog i dalších jezerech Nové Anglie a Kanady jsou ve skutečnosti přenosný melanom, který se nějaký způsobem masivně šíří mezi rybami. K panice prý není důvod a současně jde o velmi zajímavou situaci pro intenzivní výzkum.

Kosatky dravé dokážou vědcům zamotat hlavy. Někdy potápějí plachetnice, jindy nosí klobouky z lososů a nejnověji porcují pro zábavu obří rybu „hlavičkou“.

A new Dolphin X remote access trojan claims to use an AI-powered profiling feature to score and rank infected users, helping cybercriminals identify which victims should be targeted first. [...]

Poštovní klient Thunderbird byl vydán v nové verzi 153 s kódovým názvem Meadow. Jedná se o ESR (Extended Support Release) vydání. Přehled novinek v poznámkách k vydání. Vylepšuje OAuth. Thundermail lze používat bez instalace doplňku.

The incident is a first and signals a seismic shift in cybersecurity.

An autonomous agent powered by OpenAI’s advanced artificial intelligence models went rogue during a security test and hacked multi-billion dollar tech startup, Hugging Face, last week.

The agent didn’t just exploit vulnerabilities in Hugging Face’s systems to achieve what it perceived as a strategic gain. It also exploited vulnerabilities within OpenAI’s infrastructure.

Of course, hacks are very common cyber threats that organizations face frequently. But this incident is different, because the AI agent acted without any human input. It signals a seismic shift in cybersecurity, and shows that governments and tech companies need to take urgent action to prevent this risk escalating.

Even OpenAI described the attack as “unprecedented” and acknowledged it expects similar ones “to become more commonplace with the proliferation of increasingly cyber-capable models.”

A Company Under Attack

Hugging Face is famous in the AI space. Its mission is to “democratize good machine learning” by providing benchmark datasets, community collaboration tools, and robotic platforms. The company is valued at $4.5 billion.

On July 16, the company announced it had been attacked, with a hacker obtaining unauthorized access to some internal datasets and credentials. It said the hacker was likely “an autonomous AI agent system” due to the sophistication of the attack.

Five days later, OpenAI announced the attack had been driven by some of its models: GPT-5.6 Sol and a yet-to-be released model.

The tech giant was conducting what are known as “red teaming” exercises. These are essentially simulated cyber attacks that help identify the capabilities, risks, and vulnerabilities of AI systems before they are publicly released. They are typically conducted within an isolated environment to ensure potentially dangerous systems do not escape and cause harm to real systems.

But in this case, the AI agent did escape—even though OpenAI had some guardrails in place to prevent this.

Hugging Face became a lucrative opportunity for the AI agent. It hosts ExploitGym, a benchmark that tests an AI agent’s ability to exploit real-world systems. The AI decided to turn every stone upside down to obtain access. With persistence, it succeeded.

Hugging Face was confronted with a challenge when attempting to use external AI services to diagnose the problem. The guardrails around more advanced models such as GPT-5.6 Sol and Claude Fable 5 are intended to stop them being used for cyber attacks—but they can also stop the models being used for sophisticated cyber defense.

So Hugging Face resorted to using an open-source model, GLM 5.2, developed by the Chinese company Z.AI, to counter the cyber attack.

Hugging Face said GLM 5.2 was an advantage because it was not exposed to the attack data. Both Hugging Face and OpenAI are collaborating on forensic analysis, post-incident recovery, and risk mitigation strategies.

More Sophisticated Threats Are Coming

A March 2025 study by the United Kingdom’s AI Security Institute showed the best AI could complete 80 percent of the steps needed to gain full control of a portion of an external system. Within four months, it reached 100 percent.

Z.AI’s GLM 5.2 was only released in June, with 744 billion internal variables, known in the world of AI as “parameters.” The fact that Hugging Face assessed, vetted, and deployed it within four weeks should be an eye-opener for organizations with long acquisition cycles.

The connectivity we all enjoy today can equally be our greatest threat. Cyber threats spread faster than human viruses and can create economic damage similar in magnitude to a country’s GDP.

More sophisticated cyber threats—the kind exemplified by the Hugging Face hack—will exploit the security layers that humans designed for human attackers, regardless of how sophisticated our designs are.

Indeed, in this particular case, even OpenAI’s own understanding of its models couldn’t predict or contain the rogue AI agent. This shows the need for all AI companies to urgently update and strengthen their guardrails, in order to help prevent a similar attack occurring with far more devastating consequences.

It is good to see Hugging Face and OpenAI collaborating on the investigation into the attack. This showcases the importance of putting aside market competition and blame when the situation demands.

An Early Warning

The fact that Hugging Face used Z.AI’s open-source model to diagnose and counter the attack also shows the advantages of not relying on just a few pieces of tech.

States that are not in the game of developing their own AI models need to learn from this incident the value of being different. It is not too late to design new models that could save us in situations when the most advanced models fail—or, even worse, attack us.

Indeed, last week, another Chinese company, Moonshot AI, released Kimi K3. This model has 2.8 trillion parameters, its advanced performance stunning the tech world.

It is no longer a question of “if” AI agents go rogue and attack us by themselves. The Hugging Face incident is an early warning that we must accelerate our preparedness. The threat is real and here.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The post OpenAI Agent Breaks Free and Hacks Hugging Face appeared first on SingularityHub.

AI tento rok naplno vtrhla do vývoje softwaru a výrazně ovlivňuje také open source projekty. Jiří Eischmann v příspěvku Jak mění AI open source na svém blogu rozebírá několik trendů, které v open source v poslední době v souvislosti s AI pozoruje, a jak tyto trendy svět otevřeného softwaru mění (Inflace projektů, Zahlcení kontroly, Klesající motivace zveřejňovat kód).

Origin Energy has confirmed that an unauthorized party accessed and subsequently leaked customer data online, exposing sensitive personally identifiable information (PII), among others. [...]

Evropská komise (EK) vyměřila americké internetové společnosti Google pokutu 890 milionů eur (21,5 miliardy Kč) za porušení unijního nařízení o digitálních trzích (DMA). Firma se podle unijní exekutivy provinila tím, že ve vyhledávači Google Search upřednostňovala vlastní služby a že podnikům ukládala omezení, která jim bránila nasměrovat spotřebitele k alternativním, často levnějším nákupním kanálům na platformě Google Play. Prezident společnosti Google pro globální záležitosti Kent Walker podrobil postup Evropské komise kritice a uvedl, že dnešní rozhodnutí bude mít negativní dopady na evropské spotřebitele i podniky. „Abychom splnili požadavky, musíme odstranit funkce vyhledávání v reálném čase, které Evropané milují. Například okamžité ceny a dostupnost hotelů, letů a restaurací,“ uvedl Walker. Firma podle něj bude muset rovněž omezit bezpečnostní opatření v obchodě Google Play „Není to poctivá konkurence. Je to zhoršování produktu vynucené malou skupinou stěžovatelů sledujících vlastní zájmy, což bude mít negativní dopad na evropské podniky a spotřebitele. Regulace by měla produkty zlepšovat, ne zhoršovat,“ prohlásil rovněž Walker.

A malvertising campaign on the Bing search service is pushing a fake Claude desktop app installer hosted on a legitimate Claude.ai domain to deliver the SectopRAT malware. [...]

A Russian state-supported espionage group spent months reading Western mailboxes through a then-unknown flaw in Zimbra's webmail client. The payload goes after the last 90 days of email, the organization's entire email directory, the password saved in the browser and the codes kept for two-factor recovery. Opening the message was enough to start it. The NSA, CISA and partner agencies publishedSwati Khandelwalhttp://www.blogger.com/profile/[email protected]

AMD executives took to the stage at its Advancing AI 2026 event in San Francisco today to detail the company’s next generation of AI infrastructure solutions, from Instinct MI455X AI accelerator GPUs and 6th Gen EPYC “Venice” CPUs, to Pensando networking, ROCm.AI software and its Helios rack-scale platform that ties it all together.

AMD has been working towards rack-scale AI system solutions for years. Its ZT Systems acquisition last year added valuable engineering talent and intellectual property that is now finally bearing the real fruits. Its Helios AI platform is a major platform evolution for AMD, with shipments scheduled to begin in the second half of this year (which is here and now).

The announcements at Advancing AI show how the company has engineered its AI platform solutions for large reasoning models, sustained inference and agentic workflows. These workloads pressure memory capacity, data movement, networking and CPU orchestration. AMD’s approach is to keep as much data close to the compute engines as possible and move it more efficiently throughout the system, but there’s deeper nuance here that’s obvious versus AMD’s chief rival, NVIDIA.  

AMD’s MI455X targets the AI memory wall

The Instinct MI455X GPU is the compute engine that fuels the Helios rack, and the first GPU based on AMD’s new CDNA 5 architecture. Built with a modular mix of 2nm and 3nm chiplets, it carries 432GB of HBM4 and 23.3TB/s of peak memory bandwidth.

Compared to AMD’s current MI355X, the MI455X offers 1.5 times the memory capacity, up to 2.9 times the peak memory bandwidth and up to four times the peak matrix performance with MXFP4 and MXFP8 data types, which are lower-precision numerical formats designed to accelerate AI processing while reducing memory demands. With MXFP6 (6-bit floating point), performance is rated at up to twice that of MI355X.

AMD also shared some actual, measured internal results using production silicon. The company claims MI455X delivers 3.8 times higher FP8 decode performance, 3.5 times more measured FP4 compute performance and between 2.5 and 3.5 times more networking bandwidth than MI355X, depending on the transfer path tested. Those figures provide more context than just numerical specifications, though they remain AMD-provided comparisons that will need independent validation.

AMD

The architectural choices behind the numbers are important. Reasoning models and long context windows require sizeable KV caches for maintaining AI attention states, while mixture-of-experts models frequently move large amounts of data across accelerators. MI455X should let more model data, activation states and cache remain local. New dedicated IP in hardware can transfer data while the GPU continues processing, and expanded cache and multicast capabilities are designed to reduce redundant data movement to further improve efficiency.

The aforementioned lower-precision formats can also raise throughput and reduce memory use, but model developers still have to determine where they can be applied without unacceptable accuracy loss.

AMD’s Helios rack takes aim at Vera Rubin

Dave Altavilla

Helios is AMD’s primary rack-scale competitor to NVIDIA’s Vera Rubin platform. Each liquid-cooled rack combines 72 MI455X GPUs, 18 single-socket Venice host CPUs and Pensando networking technologies.

In its most complete, premium configuration, AMD rates Helios for 2.9 exaflops of low-precision AI compute, with 31TB of aggregate HBM4 capacity, 1.7PB/s of memory bandwidth, 260TB/s of bidirectional scale-up bandwidth and 43TB/s of scale-out bandwidth.

These are formidable figures, but they are technical specifications rather than actual application benchmarks. The more consequential development is AMD’s move from collections of eight-GPU servers to a 72-GPU shared-memory domain. Models too large for one node can operate across the rack without treating every exchange as a scale-out networking transaction, which benefits large-model inference as well as training.

AMD uses UALink over Ethernet, or UALoE, for an open standard scale-up fabric. Each MI455X provides 3.6TB/s of bidirectional scale-up bandwidth, while the complete rack delivers all-to-all connectivity through a single switch layer. AMD also claims six times more scale-out bandwidth per GPU than MI355X when MI455X is configured with three Pensando Vulcano 800 AI NICs.

While open standards give cloud providers more control over suppliers and system design, AMD and its partners now have to prove those components can deliver the predictable performance, reliability and deployment experience customers expect from a tightly controlled, more vertically integrated platform.

Finally, AMD designed Helios with automatic rerouting around failed links, virtual rack partitions, tray-level serviceability and rack-wide power, cooling and health monitoring. Major hyperscalers and potentially large-scale enterprise customers will likely key in on these capabilities, which can affect the availability, total cost and consistency of the AI services they consume.

Kind of like cowbell, AMD Venice gives agentic AI more CPU

AMD

AMD’s agentic CPU messaging regarding its upcoming Venice-based EPYC processors is mostly marketing speak, but the underlying requirement is very real. An AI agent can invoke retrieval, databases, security checks, code execution and other tools before a GPU generates a response. Running many agents concurrently increases the amount of conventional compute requirements surrounding the accelerators.

Venice scales to 256 Zen 6 cores with support for 512 threads, 16 memory channels, up to 1GB of L3 cache per socket, along with PCIe 6.0 and CXL 3.1 connectivity. AMD is also offering several Venice configurations for other applications, including general-purpose servers, high-frequency workloads, GPU hosts and high-density CPU sandbox systems used to execute agent tools.

Treating the CPU solely as a GPU host understates its role. Gateways, tokenization, vector search, databases and short-lived code execution stress different mixes of per-core performance, thread count, memory bandwidth and I/O. Specifically, AMD’s internal testing shows Venice significantly outperforming its current EPYC 9965 Turin CPU across five parts of the agentic AI pipeline, including gateway processing, context assembly, vector search, enterprise applications and short-lived tool execution. Individual gains vary by workload, but AMD details the overall generational improvement at up to a 1.7 times lift. As with the MI455X figures though, these comparisons come from AMD and will require independent validation.

Pensando networking and ROCm software advance

Keeping GPUs fed with data and coordinating traffic across racks directly affects utilization and operating costs. In fact, GPU utilization is a pretty sad state of affairs currently for some of the major frontier model providers.

As such, Pensando networking has become central to AMD’s roadmap. Helios can connect each MI455X to as many as three 800Gbps Vulcano AI NICs, while Salina DPUs handle front-end networking and infrastructure services.

On the software side, which is an equally critical component, AMD also introduced ROCm.AI, an AI-assisted development layer due to arrive in August. It includes reusable skills for coding agents, simplified management and Hyperloom, which can profile workloads, tune serving configurations, modify kernels and validate results.

These tools address two persistent AMD challenges: developer efficiency and ease of use, and software tuning. Automated optimization still has to produce repeatable gains without creating hard-to-maintain code, however. And while ROCm has progressed significantly over the last few years, NVIDIA’s CUDA retains an advantage in maturity, tooling and developer familiarity.

Customer commitments underscore rack-scale confidence

AMD now has commitments that give its MI450 generation and Helios considerably more weight. Meta and OpenAI have announced multi-generation agreements composed of up to 6GW of AMD compute capacity, with initial 1GW deployments planned for the second half of 2026.

Oracle plans a 50,000-GPU public cloud cluster beginning in the third quarter, while Microsoft will deploy Helios for Azure AI inference. Finally, just before the AMD event, Anthropic announced a strategic partnership for up to 2 Gigawatts of AMD-fueled AI compute, with its first gigawatt expected online in the first half of 2027.

Commitments of this scale reflect confidence in more than just MI455X performance. These customers are evaluating the complete architecture, including Venice CPUs, Pensando networking, ROCm software, rack integration, serviceability and AMD’s ability to deliver and execute across multiple product generations.

There is some financial alignment behind the agreements as well. AMD issued OpenAI performance-based warrants and committed to investing up to $5 billion in Anthropic. That context matters when evaluating these deals as market validation, but these planned deployments are substantial nonetheless and put Helios on a much stronger foundation as it begins shipping.

AMD expands its robotics and embedded foundation

AMD also expanded its physical AI portfolio, building on credible traction from its Xilinx-derived Kria adaptive system-on-modules and embedded technologies that are already powering robotics, machine vision and industrial automation applications.

The new Ryzen AI Embedded X100 combines up to 16 Zen 5 CPU cores, integrated Radeon graphics, a second-generation NPU and as much as 128GB of unified LPDDR5X memory shared across its compute engines. To me this looks a lot like a repackaging and optimization of the company’s Strix Halo platform, but with specific optimizations for the embedded space. Regardless, AMD is pairing X100 with the Kria AI Robotics Developer Platform, which includes a System Module or SOM, and a new Robotics Partner Network spanning hardware, software and platform providers.

Samples began shipping in June, with full production expected in the fourth quarter. This broader objective is to give developers a path across AMD x86 CPUs, GPUs, NPUs and FPGAs for real-time autonomous systems, rather than requiring them to assemble those hardware engines and software components independently.

Execution for AMD is now the test

AMD has assembled a credible platform for the burgeoning agentic AI market that’s blowing up currently with no signs of stopping. MI455X addresses memory and data movement, Venice handles dense agentic CPU workloads, Pensando networking connects global system resources, and ROCm.AI addresses software complexity. Finally, Helios assembles these components into a true competitive threat for NVIDIA’s latest Vera Rubin platform.

AMD’s open architecture may appeal to customers seeking supplier choice, but openness must also translate into reliable deployments, competitive total cost and software that does not require a significant rip-up. NVIDIA enters this cycle with a stronger ecosystem and far more rack-scale deployment experience. The true test will be how easily and reliably customers can integrate, operate and maintain these AMD solutions at scale.

As it stands, AMD now has major customers and a clearly defined architecture with systems engineering expertise behind it. Delivering Helios on schedule and showing that its performance claims translate into a real production workload throughput advantage and total cost of ownership gains will determine how much the competitive gap narrows. And of course, this is in a market that is clamoring for ever-more compute resources with a seemingly insatiable demand for AI services and capacity. That’s an environment for big iron success. Now AMD just has to deliver optimized, turnkey AI platforms. This is far easier said than done, but time will soon tell as deployments take shape this year.

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