A draft of Russia's national AI plan proposed using AI to analyze 70% of blocked VPN services and lift blocking effectiveness to 95%
The Digital Development Ministry says the measure is absent from the current draft, but Roskomnadzor's separately budgeted and arguably crucial ML filtering project remains
The 95% is an effectiveness target, not accuracy, and sources expect AI to raise false positives, with corporate VPN, cloud and voice traffic most exposed
Russia's Digital Development Ministry considered using artificial intelligence to push the effectiveness of its VPN blocking to 95%, according to a front-page report in Kommersant.
The measure appeared in a draft of the country's national plan for implementing AI through 2030; the ministry now says it is not in the current version.
This isn't the first time such a move has been reported, but some earlier coverage might have been overzealous, to say the least, when it reported that "Russia Eyes AI-Powered VPN Crackdown With 95% Blocking Accuracy." The problem with that headline is that not only was that proposal dropped, but the 95% accuracy figure was never actually in play.
A draft that proposed AI use
None of the above reporting should reassure an estimated 57 million Russians who rely on circumvention tools.
Roskomnadzor (the Federal agency tasked with "Supervision of Communications, Information Technology and Mass Media") has its own machine-learning filtering project budgeted outside the national AI plan, and the money for Russia's blocking infrastructure is set to double next year.
Kommersant, which reviewed the draft, reports that the ministry wanted AI to analyze the characteristics of 70% of all the VPN services Russia blocks. The drafters reckoned that would lift blocking effectiveness to 95%.
Asked about it, the ministry said the current draft of the plan "contains no such information," in Meduza's translation. A source familiar with the discussions told Kommersant that agencies had pitched a range of measures for the plan, some of which may never be implemented, and that the traffic-analysis algorithms already in service are a form of intelligent analysis but do not count as AI.
An AI layer would behave fundamentally differently; rather than matching known signatures, models would weigh packet sizes and timing, session length, and the balance of inbound to outbound traffic, then flag likely VPN servers from indirect clues: how a node behaves, which host it sits on, and what its neighboring IP addresses look like. The payoff is speed, catching new servers before they reach any blocklist.
Kommersant uses the word effectiveness, and the distinction matters: a blocking-effectiveness figure describes how much targeted traffic it stops. It says nothing about how much legitimate traffic gets stopped with it, and that is what the catch is; the solution proposed does not account for any mistakes, just how effective it would be at its job of blocking out VPN traffic.
The 95% target is also less of a leap than some headlines suggest, because Russia already has 92% on paper, at least by its own estimates. A Roskomnadzor subsidy decision for its Main Radio Frequency Center (GRChTs), dated January 2025 and surfaced in May 2026 by journalist Maria Kolomychenko, sets an "average level of effectiveness" of 92% for restricting VPNs via signatures by the end of 2030. Verstka reports that the AI proposal was meant to lift that same indicator to 95%.
The more consequential point is that striking a line from a draft strategy does not strike the capability. Forbes Russia reported in January 2026 that Roskomnadzor planned to spend 2.27 billion rubles (around $29 million) in 2026 on a machine-learning-based traffic-filtering mechanism, a project we flagged when Windscribe saw a roughly 90% drop in its Russian traffic that same month.
The false-positive risk is not hypothetical in Russia, because the government has already learned how hard it is to separate good VPNs from bad ones. When the ministry ordered major platforms in April to block users with VPNs switched on, its guidance promised a whitelist for corporate VPNs.
Telegram founder Pavel Durov claimed the crackdown had "triggered a massive banking failure," and outage-tracking data showed bank disruptions on April 3 2026. A source told RBC at the time that no reliable technical method existed to tell permitted corporate VPN traffic from the rest.
For now the picture on the ground is unchanged: blocking is intensifying with or without an AI mandate. At least 10 VPN providers reported tighter restrictions in the week after September's State Duma elections, and VPN Guild chairman Alexey Kozlyuk described it to Meduza as "a continuation and gradual intensification" of a campaign that began in August.
The Kommersant report also landed a day after RBC revealed a draft ministry order requiring phones on Russia's 5G networks to support the FSB-developed Kuznyechik cipher from March 1, 2032.
Providers keep adapting. "In Russia, VPNs face pressure almost every month; it is a new norm," an Amnezia VPN spokeswoman told TechRadar Pro. But Sarkis Darbinyan of RKS Global and the VPN Guild warned TechRadar that for users, the end result is "an ever-shrinking number of services that actually work."
The 95% target may be gone from one document. The infrastructure, the money, and the intent behind it are not.
California Department of Education third-party partner falls victim to a data breach attack
An estimated three years' worth of students (5.7 million per year) likely affected
It seems access was obtained by a simple password theft
Malwarebytes has reported that millions of current and former California public school students have had their sensitive personal information stole following a massive data breach.
The breach occurred in a Secure File Transfer Protocol (SFTP) system used by Educational Testing Service (ETS) – a third party used by the California Department of Education for managing student information.
According to the report, the attackers were able to regain entry repeatedly over the course of four months, accessing student files filled with names, addresses, dates of birth and more.
California student system breach likely impacting millions
Though ETS hasn't officially disclosed how many students have been impacted, Malwarebytes notes that the state's public schools counted more than 5.7 million students in the 2025-2026 year alone. The state's Department of Education believes students who enrolled in the previous three years could be impacted – or around 17 million students if all three years were equal.
The sensitive files were accessed and copied multiple times between May 22 and September 18, with the activity finally being discovered on September 25, and it all comes down to human vulnerability.
Rather than a software vulnerability, the attackers are believed to have gained access via compromised credentials, though the intricacies around the specific attack vector haven't been shared yet.
ETS and the California Department of Education are both working to notify impacted individuals, who are being given 24 months of identity protection software for free. No evidence seems to have been found to prove that the stolen information has been publicly released, and it doesn't seem that a ransom threat has been made.
"We have implemented additional safeguards, including enhanced monitoring, strengthened access controls, and further third-party review of our security practices," ETS added in a statement (via Reuters).
Ukraine's CCD says suspected Russian bot posts in its sample fell 83.6% on October 8 after a strike shut Yandex's Sasovo data center
The same data shows volume back above pre-strike levels by October 10, with a smaller 42.6% dip after the Vladimir shutdown
Cloudflare and Graphika found no comparable drop in Western political communities, though Russian-language Telegram output fell 5% to 15%
Ukraine's Center for Countering Disinformation (CCD) says suspected Russian bot accounts went almost silent after drones knocked out Yandex data centers earlier this month, with message volume across a monitored network falling 83.6% in a single day.
The findings come from an analytical report the CCD dated October 15 2026. Using Osavul monitoring software, analysts tracked 22,664 messages from 5,201 accounts showing signs of inauthentic behavior across 26 topics in Ukraine's Facebook and Telegram space between October 6 and 12.
The 83.6% figure is real, but the same report shows those accounts posting more than before within 48 hours, and independent data from Cloudflare shows that at least some of the malicious automation running on Yandex's cloud did not die so much as move house.
A collapse in automated traffic after the Yandex Cloud took a beating
The first collapse came on October 8, hours after a drone strike and fire shut down Yandex's Sasovo data center in Ryazan region, home to two of the company's three supercomputers.
Daily messages fell from 4,032 to 660, and active accounts dropped from 1,526 to 258. Telegram took the harder hit, down 90%, while Facebook fell 79.5%. A second, shallower dip followed the October 11 strike that Yandex said had completely halted its Vladimir site, with messages falling 42.6% on October 12, from 4,711 to 2,704. A subgroup of 98 accounts the CCD flagged for justifying the invasion or inciting hatred followed the same curve, down 77.8% on October 8 and 49.2% on October 12.
The most telling detail sits in the middle. The October 9 strike at Yandex's Kaluga campus, which the company describes as its largest, only partially took the site offline, and the CCD recorded no fall in activity that day. The two dips line up with the two complete shutdowns, as one would expect if these accounts depended on that infrastructure, though that's not proof on its own.
A surprisingly fast turnaround time?
What the 84% figure that the CCD touts leaves out is how briefly the silence held. Message volume recovered to 2,231 on October 9, then reached 4,585 on October 10 and 4,711 on October 11, both above the pre-strike level. The monitoring window in the report closes on October 12, so it cannot show whether the second dip recovered the same way.
The agency has also been downplaying the event on other forums; two days before the report, the CCD said posting in some clusters of the Pravda, InfoDefense and Nod of Time networks roughly halved for about 12 hours, and stressed that it had not recorded a significant overall decline across Russian bot farms.
On October 16, the CCD's summary of remarks by media literacy head Ivan Slychko described the effect as "a disruption of automation, not the complete disappearance of bots."
It also makes no mention of a similar comparison for ordinary accounts. On the afternoon of October 8, banks, retailers, mobile operators and news sites across Russia suffered outages. Without said information, it is hard to separate bot farms losing their servers from operators inside Russia simply losing connectivity.
The CCD is hardly an impartial player here; it is a body attached to Ukraine's National Security and Defense Council, and it has an incentive to exaggerate victories even when the data doesn't always support them.
President Volodymyr Zelenskyy had already jumped the gun, telling the Warsaw Security Forum that after the strikes "it suddenly became a little easier to breathe on many social media platforms" and that might have been a deciding factor for how the CCD built its narrative.
Beyond Ukraine, the "bots went silent" story mostly rested on a Cloudflare Radar chart showing automated traffic from Yandex's AS13238 network falling to well under 1% of its recent peak on October 11. As we noted at the time, that traffic was far more likely to be crawlers than fake social media accounts, and "bot" is a loose word when automated requests make up around half of all web traffic.
Cloudflare's own post-mortem confirms it. Marina Elmore and Lai Yi Ohlsen found 99.5% of requests from AS13238 were automated, mostly verified Yandex search crawlers, and that the network supplied only about 7% of Russia-origin bot requests. Bot traffic from Russian IP addresses hitting the post, reply, and like endpoints of one social platform dipped just 5.7% across the event period.
Graphika reached a similar verdict from the other direction. Founder John Kelly's analysis of the AfD, MAGA and Reform UK communities that viral posts claimed had gone quiet found none lost their active accounts or heaviest posters.
For now, the most defensible reading of the CCD's numbers is narrower than its headline. Knocking out the right data center can briefly cut the output of Russia-based accounts aimed at Ukraine. Keeping them quiet would mean hitting the servers they move to next, and on the evidence so far, that move takes a day or two.
As one analysis of battlefield computing puts it particularly well, resilience comes from designing for disconnection, and the bot operators appear to have learned that lesson faster than Yandex did.
As excitement around AI continues to build among businesses of all sizes, many leading voices are urging caution for those who might scale too quickly.
At its recent Think EMEA event in London, IBM revealed research which claimed efforts to stay aware of potential risks and challenges posed by AI deployment and scaling are falling somewhat short of expectations.
So what can companies do in order to balance the hype and excitement around what AI can offer, with the need for sensible and thorough governance.
AI as the enabler
"What we are seeing is that if you don't provide an answer to security, to accountability, to responsibility, it is going to be very hard to scale AI in the corporate environment - particularly when we're talking about highly regulated industries - so for us, it's more of an enabler, than something that is going to slow down the adoption,” Ana Paula Assis, Senior Vice President and Chair for IBM EMEA and APAC told us in response to that very question during a media Q&A at the event.
“The question here is really, how do you balance the speed of innovation with the implementation of governance?”
IBM recently released research on the current state of AI and governance, finding that 80% of the companies surveyed claimed that an AI incident had been escalated to board level. However only 40% of those had then been able to use the incident to secure new investment in governance, or cement a new policy.
“I think the reason for that is because it's very hard to balance that innovation with the application of tools, processes, methodologies, to ensure that you are deploying AI at scale in a safe way,” Assis continued.
IBM is reportedly well placed to discuss such growth and progress with AI, with the company’s earlier keynote at Think EMEA claiming it had seen savings of around $5.5 billion in terms of improved productivity and efficiency utilizing AI to improve core processes - money that it was able to redirect to areas including R&D and innovation.
“But we are seeing this as an enabler, and as a key component of the architecture for agentic AI going forward, because these companies have tremendous responsibility - they need to ensure that they have trust from their clients, and they will only be able to do that if they have AI that they can observe and that they can govern.”
“AI is no longer just supporting the business,” Assis said. “It has become the business model. The organizations pulling ahead are not just adding AI, but rethinking how systems, decisions and processes work together with AI.”
If you've ever watched any of the other Snappedtrue crime TV shows, you'll know exactly what you're letting yourself in for here. The latest spin-off, Snapped: Killer Moms, details the experiences and shocking true stories of a range of different women who have been pushed to breaking point while defending their offspring.
The series opens with the story of Kimberly Warth, a woman in Las Vegas whose maternal devotion ended up in her partner being shot in 2022, and Warth spending time in prison ever since.
Each episode will focus on a different case and a different mother who went above and beyond, and then some, to protect her children. Naturally, there will be harrowing moments and violent content here, but if you're into true crime docs like the ones Netflix pump out, you'll be on the edge of your seat with this series.
Here's how to watch Snapped: Killer Moms online and on TV – from anywhere.
Watch Snapped: Killer Moms in the US (without cable)
In the US, you can watch Snapped: Killer Moms live on Oxygenon Sunday, October 18 at 9 pm ET.
No cable? Stream Oxygen through YouTube TV , Sling or Fubo – some of our favorite cable replacement services.
Can I watch Oxygen for free? Yes but only with a free trial – YouTube TV offers a free 21-day trial
If you're keen to watch Snapped: Killer Moms but you're away from the US and access to the show is geo-blocked, then you could use a VPN to access it (assuming you're not breaching any broadcaster T&Cs, of course). You may be surprised by how simple it is to do. Plus, we have a great 75% discount on our #1 VPN...
Oxygen is a US-only network, so you won't be able to catch a linear broadcast or stream of Snapped: Killer Moms in Canada.
If you are traveling at the time of the broadcast, you can use a VPN to access your usual streaming services from anywhere. We use NordVPN (try for 30 days).
Can you watch Snapped: Killer Moms in Australia?
(Image credit: free)
There is no broadcast or streaming option for Snapped: Killer Moms in Australia.
Moving around or working Down Under? You can use a VPN to access your usual streaming services from anywhere. We use NordVPN (try for 30 days).
Snapped: Killer Moms FAQs
What other Snapped shows are there?
The main Snapped series has now been running for 38 seasons, having first aired in 2004.
During that time, we've seen the following spin-offs:
Snapped: Killer Couples - About romantic partners who attempt, or indeed, pull off a murder together
Snapped: She Made Me Do It - About people who claim women manipulated them into committing serious crimes
Snapped: Behind Bars - Follows convicted criminals as they spend time in prison for their crimes
Snapped: Notorious - Follows the most high-profile, dangerous, shocking crimes
Snapped: Rich B*tch Rampage - Hosted by Lisa Vanderpump, details upper class scandals and privileged women who turn to murder (coming in 2027)
We test and review VPN services in the context of legal recreational uses. For example:1. Accessing a service from another country (subject to the terms and conditions of that service).2. Protecting your online security and strengthening your online privacy when abroad.We do not support or condone the illegal or malicious use of VPN services. Consuming pirated content that is paid-for is neither endorsed nor approved by Future Publishing.
Corbenic AI's Galahad beta saves a model's KV cache to encrypted local NVMe so servers can skip rereading text after GPU memory is cleared or after a restart
Founder Sietse Schelpe tells TechRadar Pro no special hardware is needed, but the paper credits most of the 14x speedup to Blaise, a retrieval component, not SSD loading
LMCache, Nvidia Dynamo and CMX, WEKA and Phison already persist KV cache to storage; Galahad's edge is exact, fail-closed reuse in one small library
Belgian startup Corbenic AI says it can stop GPUs from rereading documents they have already processed, and its founder told TechRadar Pro the trick needs nothing more exotic than the NVMe drive already sitting in the server.
Galahad, which entered beta on October 1 2026, saves a model's key-value (KV) cache, the attention state built while a model reads a prompt, to encrypted local storage and reloads it the next time the same text appears.
In an email to TechRadar Pro, Corbenic introduced Galahad with the line "the GPU never reads the same text twice," and founder and CEO Sietse Schelpe summed it up as "the SSD a server already has becomes part of the AI's working memory."
Catering to a market with plenty of alternatives in tow
The timing of Corbenic's announcement is impeccable; with data centers on course to absorb more than 70% of high-end memory chips in 2026, getting more work out of existing hardware is an easy sell. But Corbenic's own paper shows its headline 14x speedup owes more to reading less text than to loading saved work, and the company is walking into a field where Nvidia, Phison, and a well-funded open source project already offer something similar.
Galahad's approach is slightly different: In standard serving, a model answering a second question about a document recomputes that document's attention state from the first token, a step called prefill. Engines such as vLLM and SGLang already reuse that state while it sits in GPU memory, but Corbenic's paper notes it is evicted under memory pressure and lost whenever the process restarts.
Galahad's KV component, Taliesin, writes each block of text's state to disk under a fingerprint of the exact input bytes, the model, and the tenant, then loads it when those bytes reappear.
Schelpe stressed that the stored copy only comes into play once GPU memory has been cleared or the server has restarted. "So the choice is not SSD or RAM. It is SSD or computing it all again on the GPU," he told TechRadar Pro, adding that the load "happens once, before the first word of the answer."
He volunteered two limits: short texts are not stored because recomputing them is already fast, and network storage is much slower than a local NVMe drive. The paper sets a fail-closed rule on top: "serve without memory, never with wrong memory," so any record that fails a check is simply recomputed.
No special hardware is required, according to Schelpe, just Linux, an Nvidia GPU and a standard NVMe SSD. Asked about CXL, he said, "we tried faster storage, and it didn't make Galahad faster," which matches the paper's H100 test, where a RAM disk and a local disk delivered near-identical throughput.
Schelpe told TechRadar Pro that loading stored state from a fast SSD "is much quicker" than rereading, and that "is where our 14× comes from: 0.59 seconds per question on vLLM." The paper's own tables tell a more complicated story.
The 0.59-second result comes from a recall test with 100 facts hidden in a 96,726-token list, run on Gemma 4 31B, and it used Taliesin plus Blaise. Blaise is a second component that keeps the documents as text and hands the model only the section a question needs, about 668 tokens instead of the whole corpus. The paper itself credits Blaise with lifting the gain on llama.cpp from 3.1x to 14.5x.
With Taliesin alone, vLLM took 15.56 seconds per question, slower than its 8.11-second baseline, and SGLang took 22.02 seconds against 10.29. Only llama.cpp got faster on KV reuse alone, at 3.01 seconds against 9.25. Those comparisons are lopsided in both directions: the baseline used a 12,000-token window that saw only the last 12% of the corpus and answered 10 of 100 questions, while Taliesin let the model attend to everything and answered 98 of 100.
Schelpe positions Galahad against retrieval-augmented generation (RAG) pipelines such as RAGFlow, which split documents into chunks and pass the model whichever pieces a search ranks highest. "When the search misses, the model never sees the answer," he said, citing a benchmark in which Galahad answered 100 of 100 questions and RAGFlow 77.
Asked by TechRadar Pro why nobody else had come up with the strategy, Schelpe said rivals had chased "bigger is better" and that "there is no real glory" in building something like this, adding that he was lucky not to carry the same mental baggage. Corbenic's homepage makes the same point with the slogan "Stop building bigger models. Build the layer below."
The premise does not hold up well. LMCache, an Apache 2.0 project with around 10,900 GitHub stars, already moves KV cache into CPU memory, local SSDs and remote storage, and a 2024 post on its blog pitched it as "letting LLMs read each text only once." Nine days before Galahad launched, Samsung engineers detailed a restart-recoverable NVMe tier for the project, and its commercial backer, Tensormesh, raised $20 million in May from investors including AMD Ventures, CoreWeave, and Nvidia's NVentures.
A security-centric play that still needs independent testing
Galahad's real distinction from most of its prolificly backed competition is narrower. Its paper cites LMCache and Mooncake directly and sets itself apart on exactness, with bit-identical logits as the acceptance test and any mismatch treated as a miss. "Exact, not approximate," Schelpe said, though that contrast lands hardest on techniques such as CacheBlend, which LMCache uses to reuse non-prefix chunks with selective recomputation, rather than on plain offloading.
Corbenic's launch release actually leads with security rather than speed, calling Galahad "a secure vault for working memory" that encrypts stored state, keeps each customer's data separate, and can record activity so developers can roll a server's saved memory back to an earlier point.
The concern behind the pitch is real; researchers showed at NDSS 2025 that KV cache sharing in vLLM and SGLang could let one user reconstruct another's prompts through a timing side channel, and Galahad's library counters that by refusing to start without a tenant identity and model fingerprint. Schelpe also stressed that data stays put: "There's no shared cache network, and nothing is sent to us."
On the flip side, Corbenic is a small Belgium-based team registered as a Delaware corporation, and every number in its main Galahad paper comes from experiments the founder ran himself on rented cloud GPUs, as the paper discloses. Raw logs are promised but not yet published, Blaise's selection method is withheld pending patents, and the papers are self-published arXiv preprints rather than peer-reviewed work.
Its competition includes both proprietary and open-source solutions, most of which have undergone far more testing and will likely see much broader adoption unless Galahad finds industry partners willing to integrate it.
None of that makes the idea wrong. Paying a GPU to reread the same contracts, manuals, and code on every request is a genuine waste, and Schelpe frames the contest sensibly when he calls "the real competitor: buying more GPUs." With memory and storage prices where they are, software that makes existing servers do more will find buyers, and he said early reactions have been positive, with "some nice requests and some learnings already."
What Galahad needs now is someone else's benchmark: a head-to-head with LMCache on the same Nvidia hardware, with each runtime's own prefix cache switched on, and Blaise's retrieval tested on documents it was not built around. Until then, the safest reading is that Galahad is a careful, exact take on KV persistence, and that its most eye-catching numbers come from reading less rather than remembering more.
Intel's GPU D-series sets a new GPU memory record with 480GB of LPDDR5X
Intel chose LPDDR5X instead of GDDR7 for its high-capacity AI accelerator
The D-series supports models containing up to 300 billion parameters
At the recent Open Compute Project Global Summit, Intel unveiled a graphics processor with up to 480GB of memory, an unusually large capacity designed for demanding AI inference workloads.
The GPU, called Intel GPU D-series and previously known by its Crescent Island codename, also hosts specialized computing hardware.
The Intel D-series has now dethroned Nvidia's Blackwell Ultra B300, which offers 288GB of HBM3e memory, as the data center GPU with the highest memory capacity.
Intel prioritizes memory capacity
Rather than using GDDR7 memory associated with modern graphics cards, Intel has chosen LPDDR5X, a memory technology commonly found in laptops and other power-conscious devices.
The configuration suggests that Intel is prioritizing memory capacity and power efficiency for AI inference instead of designing a conventional gaming graphics card.
Intel says the accelerator can support large language models containing up to 300 billion parameters on one card, while an eight-GPU system can accommodate models exceeding one trillion parameters.
These capabilities are intended for lengthy prompts, simultaneous inference requests, enterprise AI services, and robotics applications requiring responsive processing.
“Inference is becoming the defining AI workload, and enterprises need infrastructure that delivers the right performance at the right cost,” said Anil Nanduri, vice president, AI Products & GTM, Intel Data Center Group.
“With Intel GPU D-series, we're focused on making high-capacity inference practical to deploy and economical to scale.”
The 480GB capacity is particularly relevant when AI systems must keep substantial model weights and associated data accessible during inference.
However, memory capacity alone does not establish how quickly a GPU will serve requests, since performance also depends on bandwidth, computing resources, software, and workload characteristics.
Intel rates the D-series at 1.3 petaflops for FP8 computation, a numerical format commonly used to accelerate AI operations while reducing memory requirements compared with higher-precision formats.
Its 1.5TB/s memory bandwidth provides a separate measure of how quickly data can move between memory and the processor.
Intel wants the accelerator to fit existing data centers
The company specifies a 350W power envelope and a standard PCIe form factor, alongside air cooling and software compatibility across its GPU platforms.
These characteristics are intended to simplify deployment in existing data centers without requiring specialized cooling arrangements or entirely new rack infrastructure.
Intel also plans to connect the accelerator with its Xeon processors, allowing CPUs to coordinate tasks, manage data movement, and handle workloads that do not require GPU acceleration.
This approach addresses AI systems increasingly built around multiple stages, including retrieval, reasoning, orchestration, external tools, and execution.
“To realize their full potential, organizations need infrastructure that can match different workloads with the right intelligence, right compute resources and right economics. That's what we mean by 'put every prompt in its place.”
As of the time of writing, Intel has not revealed the public retail price or a specific general-availability date for the GPU D-series.
Zotac Magnus One Ultra’s Core Ultra 7 265 delivers 20 cores and speeds of up to 5.3 GHz
Nvidia rates the desktop RTX 5080 at 1,801 AI TOPS for artificial intelligence workloads
The desktop GPU features 10,752 CUDA cores, 336 Tensor cores, and 84 RT cores
Zotac has released a compact desktop computer built around Nvidia's GeForce RTX 5080, with barebones pricing starting at approximately ~ $2,640 in Japan.
The Magnus One Ultra EU275080C combines a desktop graphics card with Intel's Core Ultra 7 265 processor inside an 11.46L enclosure.
The 20-core processor runs at 2.4GHz and boosts up to 5.3GHz, and Zotac says the device is one of the smallest PC to pair a desktop RTX 5080 with a desktop Intel Core Ultra chip.
Desktop RTX 5080 power comes in a compact enclosure
The system uses Nvidia’s Blackwell architecture and supports DLSS 4.5 with Multi-Frame Generation, ray tracing, 4K gaming and local artificial intelligence workloads.
Its desktop RTX 5080 includes 16GB of GDDR7 memory, distinguishing it from compact computers that rely on integrated graphics or mobile GPUs.
The memory runs on a 256-bit bus with 960GB/s of bandwidth, alongside 10,752 CUDA cores, 336 Tensor cores and 84 RT cores.
Nvidia rates the GPU at 1,801 AI TOPS, and it is considered to be about 33% ahead of the RTX 4080 in games.
Nvidia also sells laptop RTX 5080 graphics hardware, but those mobile components are not equivalent to the desktop version.
Zotac fits the hardware inside a chassis measuring 126 x 365.5 x 249 mm, with a total weight of approximately 6.34 kg.
An 850W power supply supports the configuration, while the system offers two memory slots accommodating compatible DDR5 modules and a maximum capacity of 96GB.
The GPU’s 360W rating takes about 42% of that supply, and memory is rated at DDR5-6400 for CSODIMM and 5600 for SODIMM.
Storage expansion comes through two M.2 slots, supporting PCIe 5.0 x4 and PCIe 4.0 x4 connections respectively.
The connectivity options include Thunderbolt 4, Wi-Fi 7, Bluetooth 5.4 and eight USB connections (four USB 3.2 Gen 2, four USB 3.2 Gen 1) for peripherals and external devices.
Display outputs comprise three DisplayPort 2.1b connectors, one HDMI 2.1b port, one HDMI 2.0 connection, and a DisplayPort-compatible Thunderbolt 4 port.
Together, these connectors support up to six total displays — or four simultaneous 4K displays driven directly by the dedicated graphics card.
With compatible displays and Display Stream Compression, DisplayPort 2.1b supports configurations reaching 4K at 480Hz or 8K at 165Hz.
The HDMI 2.1b output supports up to 4K at 480Hz or 8K at 120Hz, depending on the connected display and configuration.
Network connectivity includes one 5Gbps Ethernet port and one 1Gbps Ethernet connection, alongside a UHS-II SD card reader and audio inputs.
Barebones pricing leaves buyers to configure the system
Zotac lists several configurations, with Japanese direct-sale prices ranging from ¥418,000 (~$2,647) to ¥750,200 (~$4,750) depending on memory, storage, and operating system.
The entry-level package excludes all three, which means that users interested in this model will have to separately purchase the memory, storage and OS.
A version with 16GB of memory and a 1TB SSD costs ¥522,500 (~$3,309) with Windows 11 Pro installed.
The model with 32GB or 64GB of RAM and a 1TB drive costs ¥561,000 (~$3,552) and ¥654,500 (~$4,144), respectively, while the highest-priced option includes 96GB of memory for ¥750,200 (~$4,750).
There is also a Windows version of the Zotac Magnus One Ultra that comes with 32GB of RAM and two 1TB SSDs and costs ¥588,500 (~$3,727).
Microsoft will make Maia its main inference engine at multi-gigawatt scale, following Google and Amazon into in-house silicon
Microsoft's upcoming Maia 300 trades a modest 36% compute gain for 2.5x Maia 200's memory bandwidth, betting on memory-bound decode, with production in 2027
Hyperscalers supply just over half of Nvidia's data center revenue at a 75% gross margin, so each move to in-house chips affects not only its pricing power but the chip industry's sustainability as a whole
In 2024, Microsoft told the European Commission that Google was the only company vertically integrated at every AI layer, from chips to a mobile app store, and that "everybody else must rely on partnerships to innovate and compete."
Now, it has revealed plans to follow the same route without breaking a sweat.
In an Official Microsoft Blog post, Scott Guthrie, executive vice president of Cloud + AI, said Microsoft will scale its Azure Maia accelerators to multi-gigawatt deployments starting in 2027 and make Maia the primary inference system for Microsoft's products and services.
The vehicle of its choice is Maia 300, which Rani Borkar, president of Azure Hardware Systems and Infrastructure, and Saurabh Dighe, corporate vice president of systems planning and architecture, detail in an architecture deep dive.
Feeding the beast, not growing it, as it takes the Google route
The move is a pointed statement from one of Nvidia's biggest customers, and it lands as bubble worries grow louder. Gartner forecast this week that AI providers will spend nearly $1 trillion on infrastructure in 2026 alone, and that AI revenue won't cover the build-out profitably until at least 2032.
If the largest buyers of AI hardware stop paying Nvidia's margins for the work they do most, the most richly valued part of that build-out starts to look exposed. Whether Maia 300 is the final catalyst for such a move, though, is a harder case to make.
Maia 300 moves from Maia 200's single die to a multi-chiplet package, with compute on TSMC's N2P process and memory-controller and I/O functions split onto separate dies. Eight HBM4 stacks deliver 288GB at 18TB/s per accelerator, against 216GB at 7.1TB/s for Maia 200, according to StorageReview's breakdown of Microsoft's spec sheet.
Microsoft hasn't abandoned compute so much as rebalanced around memory. In decode, the token-by-token phase that dominates long reasoning chains and agent loops, accelerators spend much of their time waiting on data rather than doing arithmetic.
The Maia 300 tries to close that gap by keeping data near the compute that uses it, through a non-uniform memory layout across chiplets, software-managed on-die SRAM, and narrower number formats, including an enhanced MXFP4 and sub-3-bit storage formats, with hardware conversion between them. The Maia 300 announces something the rest of the industry already knew, albeit more subtly: Custom silicon is no longer a side project for hyperscalers.
Google, the company Microsoft singled out two years ago, began recognizing revenue from TPU system sales delivered to customer data centers in the second quarter, CFO Anat Ashkenazi said on Alphabet's July earnings call, while CEO Sundar Pichai said the company is "seeing strong demand for TPUs as standalone systems from other customers."
The incentive is plain from Nvidia's own results. In the quarter to July 2026, the company reported $96.2 billion in revenue, $89 billion of it from data centers, at a 75% gross margin. CFO Colette Kress put hyperscale revenue at $49 billion on the earnings call, meaning a little over half of Nvidia's data center business comes from a small group of buyers, the biggest of which now design their own accelerators.
Every inference workload that moves in-house is one where that buyer pays closer to manufacturing cost instead of Nvidia's margin. Microsoft told investors in July that Maia 200 was up to 40% cheaper to operate than Nvidia's latest chips on OpenAI and Microsoft models.
It did, however, fail to name a particular GPU it called a "leading GPU-based system," an important distinction given Nvidia's focus on efficiency via Vera Rubin, which OpenAI even refused to compare against outright.
The reveal didn't move stocks much; on Monday, Nvidia shares closed down 1.29% after the reveal, while Microsoft's rose 1.14%, and Microsoft's opaqueness about efficiency gains may be one reason.
The other reason is equally important: Evercore ISI analysts estimate Maia will account for only about 10% of Microsoft's AI chip spending by the first quarter of 2027, CNBC reported, and Nvidia has guided to $108 billion in revenue for the current quarter.
Scale is arguably an ongoing constraint, however; at 167 kW per rack, a gigawatt of rack power is roughly 6,000 Maia 300 racks, or about 575,000 accelerators, before cooling and facility overheads.
Going the Google route hasn't stopped anyone buying from Nvidia either. Amazon, with its "top three" chip business, is still set to take more than 3 million Nvidia GPUs by mid-2028.
Alphabet, which now sells its own TPUs, raised its 2026 capex guidance to between $195 billion and $205 billion in July, lists Vera Rubin alongside its own TPUs, and Ashkenazi said it is "still in a supply constraint environment."
Microsoft keeps Nvidia and AMD for training, and Satya Nadella said earlier in 2026 that "because we can vertically integrate doesn't mean we just only vertically integrate."
Jensen Huang's answer to the custom silicon question on Nvidia's August call could have been written about Maia: "Whereas many of these XPUs are inference-specific chips for one cloud or one service, NVIDIA is a platform." Microsoft's counter-bet is somewhat different: one cloud is big enough.
The deeper problem for the pin theory is that custom silicon changes who collects the margin, not how much gets spent. Gartner warns that the industry needs about $8.5 trillion in revenue between 2026 and 2030 to earn a reasonable return on its build-out, versus no more than $3.6 trillion it expects AI to generate. Microsoft isn't responding by building less; it is planning gigawatts more, and cheaper tokens arguably help close that revenue gap rather than widen it.
Some of what Microsoft saves will also flow elsewhere in the supply chain. Maia 300's pitch rests on HBM4, the same constrained memory Nvidia's Rubin uses at the same 288GB per package, and its compute dies come from TSMC, which supplies everyone.
Google's TPUs are developed and supplied under a long-term agreement with Broadcom, whose CEO Hock Tan said that "many investors in AI don't understand what they're putting their money into," and who expects chip shortages to ease only around 2028 or 2029. Nvidia isn't sitting idle either; it already paid $20 billion to license Groq's SRAM decode technology, a sign it sees the same inference threat coming.
Until production silicon ships and someone outside Microsoft can measure it, Maia 300 is a well-argued design whose most important numbers are ones that only Microsoft has seen, and a reminder that the biggest threat to Nvidia's margins may be its best customers rather than its rivals.
RGB TVs, called ‘True RGB LED’ here by Sony, are still in their infancy, with RGB regularly touted as the technology that will topple OLED at the top of the TV mountain — and of the few I’ve tested so far, the Sony Bravia 9 II is the one that comes closest to succeeding. It takes everything I liked about the step-down Sony Bravia 7 II (also True RGB LED) and levels it up, including picture quality, sound and gaming performance, all of which are up there with the best TVs of 2026.
The Bravia 9 II’s picture quality is its crowning achievement. It delivers vibrant yet accurate-looking colors which make movies pop, fluid motion that’s great for sports and, with its anti-glare matte screen, it’s perfect for bright-room viewing too. Its most impressive elements, though, are its contrast and reproduction of dark tones, which are arguably the best I’ve seen from a backlit TV — if this is what RGB is capable of now, then expect it to rival the very best OLED TVs. While viewing angles could still be better, and some blooming exists, this is an excellent backlit TV.
The Bravia 9 II’s built-in sound is strong overall. Its speakers deliver remarkable detail, with a good sense of space that fills a room more than most TV speakers, and it delivers clear speech. I did find its bass and low-end lacking however, and it didn’t deliver the power I expected; so while the speakers are good enough that you don’t necessarily need one of the best soundbars, cinephiles may want to invest.
Gaming is another of the Bravia 9 II’s strong suits. It comes with a comprehensive set of features, (though sadly only has two HDMI 2.1 ports out of its four HDMIs available), and delivers great overall performance that handles even the fastest-paced games well.
The Bravia 9 II’s major downside is its price. At $3,599 / £3,499 / AU$5,995 for the 65-inch model I tested, this is one of the most expensive TVs on the market, at around a full $500 / £500 / AU$750-1000 dollars more than both rival OLEDs and other RGB TVs. And prices are unlikely to drop, as Sony doesn’t often discount its TVs, so if you’re looking for the best value you may want to go elsewhere, despite this TV’s excellent performance.
Sony Bravia 9 II review: Prices & release date
The Bravia 9 II's colors are one its real strengths (Image credit: Future)
Release date: August 2026
65-inch: $3,599.99 / £3,499.99 / AU$5,995
75-inch: $4,599.99 / £4,299.99 / AU$7,495
85-inch: $6,499.99 / £5,499.99 / AU$9,995
115-inch: $30,999.99 / £22,999.99 / AU$44,995
The Bravia 9 II is Sony’s flagship RGB LED TV for 2026, sitting above the Sony Bravia 7 II. It’s available in 65, 75, 85 and 115-inch sizes. The 65-inch model I tested launched for $3,599.99 / £3,499.99 / AU$5,995, putting it at the premium end of the market.
At the time of writing (October 2026), the Bravia 9 II’s prices remain unchanged in the US and UK, but in Australia prices have dropped across the 65-85-inch sizes, with the 65-inch now available for AU$4,795.
Sony Bravia 9 II review: Specs
Screen type:
RGB LED
Refresh rate:
120Hz
HDR support:
Dolby Vision, HDR10, HLG
Audio support:
Dolby Atmos, DTS:X
Smart TV:
Google TV
HDMI ports:
4 (2x HDMI 2.1)
Built-in tuner:
ATSC 1.0 (US)
Sony Bravia 9 II review: Benchmark results
1 / 4
Brightness Measurements
Taken using a colorimeter and test pattern generator. Click the test name for measurements and select the chosen test from the dropdown menu.
Sony Bravia 9 II
HDR Filmmaker Mode (10% window)
4416
HDR Filmmaker Mode (Fullscreen)
974
Samsung R95H
HDR Filmmaker Mode (10% window)
1977
HDR Filmmaker Mode (Fullscreen)
612
Sony Bravia 7 II
HDR Filmmaker Mode (10% window)
1114
HDR Filmmaker Mode (Fullscreen)
829
01,5003,0004,5006,000
nits
HDR Filmmaker mode Data
Product
HDR Filmmaker Mode (10% window) (nits)
HDR Filmmaker Mode (Fullscreen) (nits)
Sony Bravia 9 II
4416
974
Samsung R95H
1977
612
Sony Bravia 7 II
1114
829
Sony Bravia 9 II
HDR Standard Mode (10% window)
4044
HDR Standard Mode (Fullscreen)
903
Samsung R95H
HDR Standard Mode (10% window)
2066
HDR Standard Mode (Fullscreen)
600
Sony Bravia 7 II
HDR Standard Mode (10% window)
1295
HDR Standard Mode (Fullscreen)
788
01,5003,0004,5006,000
nits
HDR Standard Data
Product
HDR Standard Mode (10% window) (nits)
HDR Standard Mode (Fullscreen) (nits)
Sony Bravia 9 II
4044
903
Samsung R95H
2066
600
Sony Bravia 7 II
1295
788
Sony Bravia 9 II
SDR Filmmaker Mode (10% window)
105
SDR Filmmaker Mode (Fullscreen)
110
Samsung R95H
SDR Filmmaker Mode (10% window)
115
SDR Filmmaker Mode (Fullscreen)
185
Sony Bravia 7 II
SDR Filmmaker Mode (10% window)
90
SDR Filmmaker Mode (Fullscreen)
94
050100150200
nits
SDR Filmmaker Mode Data
Product
SDR Filmmaker Mode (10% window) (nits)
SDR Filmmaker Mode (Fullscreen) (nits)
Sony Bravia 9 II
105
110
Samsung R95H
115
185
Sony Bravia 7 II
90
94
Sony Bravia 9 II
SDR Standard Mode (10% window)
821
SDR Standard Mode (Fullscreen)
703
Samsung R95H
SDR Standard Mode (10% window)
413
SDR Standard Mode (Fullscreen)
187
Sony Bravia 7 II
SDR Standard Mode (10% window)
608
SDR Standard Mode (Fullscreen)
476
02505007501,000
nits
SDR Standard Data
Product
SDR Standard Mode (10% window) (nits)
SDR Standard Mode (Fullscreen) (nits)
Sony Bravia 9 II
821
703
Samsung R95H
413
187
Sony Bravia 7 II
608
476
HDR color gamut
Closer to 100% is better. Taken using a colorimeter and test pattern generator.
Sony Bravia 9 II
UHDA-P3
95.6
BT.2020
88.4
Samsung R95H
UHDA-P3
148
BT.2020
94.1
Sony Bravia 7 II
UHDA-P3
99.2
BT.2020
87.7
037.575112.5150
HDR Filmmaker mode Data
Product
UHDA-P3 ()
BT.2020 ()
Sony Bravia 9 II
95.6
88.4
Samsung R95H
148
94.1
Sony Bravia 7 II
99.2
87.7
Picture accuracy
A lower score is better. Taken using a colorimeter and test pattern generator.
Delta-E
Sony Bravia 9 II
5.3
Samsung R95H
3.7
Sony Bravia 7 II
6.5
01234567
Delta-E Data
Product
Value
Sony Bravia 9 II
5.3
Samsung R95H
3.7
Sony Bravia 7 II
6.5
Input lag
Lower is better. Taken using a Leo Bodnar 4K HDMI Video Signal Lag Tester.
Sony Bravia 9 II
4K 60Hz
9.8
1080p 120Hz
5.2
Samsung R95H
4K 60Hz
11.7
1080p 120Hz
9.4
Sony Bravia 7 II
4K 60Hz
10.4
1080p 120Hz
5.7
03.757.511.2515
ms
HDR Filmmaker mode Data
Product
4K 60Hz (ms)
1080p 120Hz (ms)
Sony Bravia 9 II
9.8
5.2
Samsung R95H
11.7
9.4
Sony Bravia 7 II
10.4
5.7
The above EOTF graphs show the accuracy of the Sony Bravia 9 II for hitting different HDR brightness levels in grayscale. The closer to the yellow line, the more accurate a TV is. As with all our tests, these results are out of the box, without calibration, to demonstrate the performance most people will see. As you can see, the Bravia 9 II did very well when set to Professional modeFutureWhile Cinema mode is the preferred picture mode for brighter rooms and scenes, the EOTF graphs show the Bravia 9 II loses accuracy in the 40-80% brightness range, but does well with blacksFutureSpectral power distribution refers to the intensity of light that a source will display at various wavelengths of color. It can reveal how accurate a source can show color at different light levels, and can be instructive to understand how a TV is handling color. The Bravia 9 II scored well in Professional mode FutureIn the Bravia 9 II's Cinema mode results were again excellent, with more distinct reds compared to Professional mode Future
Sony Bravia 9 II review: Features
The Bravia 9 II has a good range of ports, but only two of its HDMI ports are 2.1 (Image credit: Future)
RGB LED panel
XR processor with AI features
Google TV interface
The Bravia 9 II uses an RGB mini-LED panel (dubbed True RGB LED by Sony), which uses red, green and blue mini-LEDs, rather than the standard white you’ll find in regular mini-LED panels, to boost color reproduction.
It uses Sony’s XR Processor, which powers several AI picture features such as XR Clear Image AI for upscaling, as well as XR Motion Clarity, which analyzes movement on screen to produce a clearer, judder-free image.
In terms of HDR support, the Bravia 9 II supports Dolby Vision, but there’s no support for HDR10+. If you’re looking for a TV that supports both you’ll need to look at Hisense, TCL and Philips models. The Bravia 9 II also supports IMAX Enhanced content.
For audio, the Bravia 9 II has an 80W speaker system, consisting of two tweeters, two full-range speakers, two subwoofers and two beam tweeters. Several audio features, such as Voice Zoom 3, Auto Calibration and Acoustic Multi-Audio+, are included. There’s support for Dolby Atmos and DTS:X formats too.
The Bravia 9 II has two fully-specced HDMI 2.1 ports set for gaming, with 4K 120Hz, VRR and ALLM support. Perfect For PS5 features are also present, including Auto HDR Tone Mapping and Auto Genre Picture Mode.
Like all Sony TVs, the Bravia 9 II uses Google TV as its smart TV interface. This gives you access to all major apps such as Netflix, Prime Video and Disney Plus, and, in the UK, BBC iPlayer and ITVX. Google TV with Gemini is included, which can be controlled hands-free and recommend content.
Features score: 4.5 / 5
Sony Bravia 9 II review: Picture quality
Movies like The Super Mario Galaxy Movie are great for showcasing the Bravia 9 II's vibrant colors (Image credit: Nintendo / Universal Pictures / Future )
Excellent color reproduction
Accurate, OLED-level blacks
Some mild blooming off-axis
The Bravia 9 II’s measured brightness is fantastic. In Cinema and Professional mode (the latter is the closest to Filmmaker Mode) it hit 4,180 nits and 4,416 nits peak HDR brightness respectively. This puts it at double what rival flagship RGB TVs like the Samsung R95H hit (1,977 nits in its own Filmmaker Mode).
It also delivers superb fullscreen brightness, hitting 974 nits HDR brightness in Cinema and Professional modes. It even delivers big SDR numbers, hitting 703 nits fullscreen SDR brightness in Standard and 656 nits in Cinema, making it a great choice for broadcast TV in bright rooms.
Speaking of bright rooms, the Bravia 9 II demonstrates much better handling of mirror-like reflections than the step-down Sony Bravia 7 II, which I found quite reflective in our testing lab. When I viewed the Bravia 9 II and Samsung R95H side-by-side, the Bravia 9 II’s own matte screen was seriously effective.
The Bravia 9 II did a good job of upscaling SDR/HD content. Watching videos on YouTube, textures did look sharper, and although it wasn’t to a 4K-level, it was improved. Watching HD movies like Fight Club on Disney Plus, textures looked crisp and contrast was improved.
The Bravia 9 II, also demonstrated excellent picture quality with 4K Blu-ray reference discs. Its color reproduction during The Super Mario Galaxy Movie was gorgeous, with deep, rich colors that really popped on-screen. Any time the Lumas appeared, they had a sense of depth to them, thanks to how bold their colors were.
Even in live-action movies like Speed Racer, the Bravia 9 II demonstrates its superb colors (Image credit: Warner Bros / Future )
Colors in live-action content looked similarly great. In Speed Racer, the bold aesthetic of the Racer family home, with orange doors, green and purple walls and yellow furnishings, not only looked vivid to suit the movie’s cartoon-ish nature, but also had a nice accuracy to them.
The Bravia 9 II’s HDR color gamut results were fantastic overall. It clocked 98.6% UHDA-P3 coverage, a number consistent with other mini-LED and OLED TVs, but most impressive was its 86.7% BT.2020 coverage. While I’ve tested higher on some RGB TVs, this puts the Bravia 9 II in line with QD-OLED models.
The Bravia 9 II has excellent contrast, perfect for movies like The Batman. Even in bright conditions, the movie looks great on this TV (Image credit: Warner Bros / Future )
Another area where the Bravia 9 II impressed was its contrast and black-tone reproduction. In The Batman, as Bruce works in the Batcave, the monitors on his desk were bright, and contrasted well with the dark tones in the surrounding walls and crevices. The Bravia 9 II’s peak brightness meant HDR highlights, such as overhead lamps, really dazzled on screen, and this didn’t come at the expense of dark tones.
In Project Hail Mary, scenes of space demonstrated just how good the Bravia 9 II’s black levels were, especially for a backlit-TV. In theory, OLED has an advantage when it comes to reproducing deep blacks, in that it uses individually controlled pixels, but I found that the Bravia 9 II rivalled most OLEDs. Not only were black levels deep, but there were few signs of blooming when viewing head-on, with the Bravia 9 II showing superb backlight control.
It’s no surprise that the Bravia 9 II’s black tones were good, as when I measured its out-of-the-box grayscale accuracy, it hit a staggering 0.92 in Cinema mode. Anything below 3 is indiscernible to the human eye, and a number like this rivals most OLEDs.
The Bravia 9 II delivers great motion as well. Watching soccer with the TV set to Standard mode, it was able to handle the fast-paced action with minimal signs of judder, though it’s best to set the motion settings to Custom and set Smoothness and Clearness to 1, as at the full 2 the TV could sometimes be aggressive with its processing. For movies, with the TV set to Cinema, action always looked smooth. Watching Speed Racer, the Bravia 9 II handled the swerving cars, and even the commentators panning across the screen, well.
It wasn’t all plain sailing for the Bravia 9 II, however. Viewed off-center, signs of blooming crept back in, with halos of light appearing around bright objects on dark backgrounds. Although there was still significantly less blooming than you’d find on other mini-LEDs, OLED still takes the crown for off-angle viewing.
Picture quality score: 5 / 5
Sony Bravia 9 II review: Sound quality
The Bravia 9 II has very good built-in soundFutureThe Bravia 9 II forgoes more traditional sound modes for deeper sound settings, such as an EQ (pictured)Future
Excellent detail
Great virtual surround-sound processing
Bass could be better
Sony’s TVs are often among the very best for built-in sound, so I had high expectations for the Bravia 9 II and spoiler alert, it mostly lived up to them. The Bravia 9 II 65-inch I tested uses a multi-speaker array consisting of two beam tweeters, two frame tweeters, two mid-range drivers and two subwoofers.
Watching the Penguin/Batmobile chase from The Batman, I was impressed most of all by the Bravia 9 II’s detail. As the Batmobile glides in and out of traffic, there were lots of effects I’d not been able to hear when watching the movie on other TVs, such as the creaking of tall lorries, the wind whipping past the Batmobile as debris fell around it, and the clattering of metal. The same was true with effects you’d hear in height channels, such as rain, which wasn’t crystal clear, but was still audible — no mean feat for TV speakers.
The Bravia 9 II also showcased very good virtual spatial sound, with the sound extending beyond the screen to create a somewhat immersive experience. Speech was also clear throughout, with the Penguin’s shouting audible even amid the chaos of the scene.
One area where the Bravia 9 II is lacking, however, is its bass. For such a large-framed TV with two subwoofers, I expected more punch in the lower tone range. The Batmobile’s engines had some heft to them, but it didn’t have the rumble I was looking for. This isn’t uncommon for a TV, but I was surprised that in this case it was one of Sony’s models that was lacking.
Sound quality score: 4.5 / 5
Sony Bravia 9 II review: Design
The Bravia 9 II feels premium, with a weighty pedestal stand and build FutureIts anti-glare screen is effective at eliminating mirror-like reflections Future
Sturdy but weighty build
Thicker than other TVs
Looks premium
Like several other RGB models I’ve tested, the Bravia 9 II is bulkier than a lot of other TVs. The Bravia 9 II’s frame is chunkier and deeper than you’d expect for a modern TV, so this is something to bear in mind if you’re going to be fitting it into a tight space. It’s weighty as well — this 65-inch TV is easily one of the heaviest I’ve set up.
That said, the Bravia 9 II still looks like a premium TV, with a central pedestal stand consisting of three metal components that feel and look appropriate for a TV of this price. The stand also comes with a clear bit of plastic that can be attached to the rear of the stand to create what the brand calls a ‘mirage effect’, where the stand seemingly disappears when viewed head-on, to give the TV a floating effect.
The Bravia 9 II’s supplied remote is perfectly serviceable, with shortcut buttons to major apps like Netflix and buttons laid out in a standard format. However, it feels lightweight and cheap, which is disappointing for a TV in this price range. It does however use USB-C charging, similar to Samsung and Hisense’s remotes, which is a nice touch.
Design score: 4 / 5
Sony Bravia 9 II review: Smart TV & menus
Google TV is a decent smart TV platform, but the banner ads on its home page (pictured) can be very distracting (Image credit: Future)
Google TV interface
Tons of picture settings
Google home page quite cluttered
The Bravia 9 II uses Google TV as its smart TV platform, with access to major apps including Netflix and Disney Plus, and, in the UK, to major UK apps like BBC iPlayer and ITVX. There’s also the option of hands-free control via Google Assistant, and the TV is compatible with Google Cast, Alexa and Apple Airplay2.
Google TV’s home page is a good hub for recommending relevant content, as well making content you’re watching easy to find, particularly on YouTube. One issue with the home page, which Google TV has had for a while, is that its large banner ads can make the screen look cluttered, and push your apps further down the screen.
The Bravia 9 II has a comprehensive suite of settings, especially for adjusting the picture. There are plentiful settings to enable you to tailor the picture to your liking, and while some of these are a little hidden (the ambient light sensor is in a totally separate menu for example), the menus are mostly easy to navigate and use. While I don’t find Google TV as aesthetically pleasing or easy to use as LG’s webOS, Samsung’s One UI Tizen, or Roku, it’s a perfectly fine smart TV system.
Smart TV & menus score: 4 / 5
Sony Bravia 9 II review: Gaming
The Bravia 9 II is great for gaming, with brilliant response times (Image credit: Future)
9.8ms input lag time
Good performance overall
Only two HDMI 2.1 ports
The Bravia 9 II has a good array of gaming features, supporting 4K 120Hz, VRR, Dolby Vision gaming and ALLM, as well as Perfect for PS5 features including Auto HDR Tone Mapping. Sadly, only two of its four HDMI ports are 2.1 compatible for gaming, which is still a step behind a lot of other rival TVs from LG, Samsung, Hisense, and TCL.
I measured the Bravia 9 II’s input lag at 9.8ms (4K 60Hz), which is a nice improvement over other Sony TVs I’ve tested, including the brand’s Bravia 8 II flagship OLED, which registered 16.8ms (also in 4K 60Hz).
Actual gameplay on the Bravia 9 II is very smooth. When I played Battlefield V, movement felt fluid and responsive, with the Bravia 9 II showing no judder during a chaotic desert mission where I dodged enemy fire while jumping in and out of vehicles. I did notice that during some particularly fast-paced action, where I sprinted across the battlefield, there was a slight blurring of finer details, but overall the game looked great, with the Bravia 9 II’s excellent picture quality for movies translating over to games.
Gaming score: 4.5 / 5
Sony Bravia 9 II review: Value
The Bravia 9 II's supplied remote has everything you need, but feels cheap for a TV at this price (Image credit: Future)
Premium price
Pricier than rival RGBs and OLEDs
Great performance for the money
There’s no denying that the Bravia 9 II is a premium-priced TV. It sits at the top end of the market and is pricier than most, if not all, of its major rivals. The 65-inch model I tested costs $3,599 / £3,499 / AU$5,999, which is pricier than a flagship OLED like the LG G6, the 65-inch model of which costs $2,499 / £2,699 / AU$4,999. It’s pricier than even the Samsung R95H, that brand’s flagship RGB TV, with its 65-inch costing $2,799 / £2,899 / AU$4,499.
Both those rival TVs have more features than the Bravia 9 II, especially for gaming, and they deliver very good performance in their own right. That being said, the Bravia 9 II is easily the most impressive RGB TV I’ve tested, delivering superb performance that exceeded my expectations. It’s just a shame that you’re paying a premium for that performance — and based on Sony’s history when it comes to pricing, it’s unlikely that the Bravia 9 II’s price will fall into line with competitors.
Value score: 3.5 / 5
Should you buy the Sony Bravia 9 II?
The Bravia 9 II's black tones are one of its most impressive features (Image credit: Future)
Sony Bravia 9 II
Attributes
Notes
Rating
Features
Impressive anti-glare screen, RGB panel and good gaming features
4.5/5
Picture quality
Stunning colors, impressive contrast and black tones, excellent backlight control and reflection handling. Some blooming when viewed off-center
5/5
Sound quality
Very good overall sound quality with exceptional detail and accuracy, along with a wide soundstage, but bass is lacking.
4.5/5
Design
Premium build with sturdy feel, but thicker and weightier than a lot of other TVs
4.5/5
Smart TV and menus
Google TV has good app recommendations, but banner ads are distracting. Good amount of picture settings; sound settings could be confusing for some
4/5
Gaming
Fantastic gaming performance with a good array of features, but only two HDMI 2.1 ports.
4.5/5
Value
Pricier than most rival RGB and OLED TVs, and beyond the budgets of many potential buyers. Very good performance for the price, however
3.5/5
Buy it if...
You want superb picture quality The Bravia 9 II's rich, gorgeous colors, excellent contrast, OLED-level blacks and impressive backlight control all combine to deliver a near-complete picture experience
You want a TV for bright rooms The Bravia 9 II's anti-glare screen eliminates mirror-like reflections effectively, which combined with its high brightness, makes it terrific for bright rooms
You want the best of RGB The Bravia 9 II is the best RGB TV I've tested so far, showing a real evolution of LED, backlit TVs that makes the tech competitive with OLED
Don't buy it if...
You're on a budget The Bravia 9 II is one of the pricier TVs on the market, with a higher price tag than most rival RGB and OLED TVs in the same sizes.
You have multiple game consoles The Bravia 9 II only comes with two HDMI 2.1 ports, meaning those with multiple consoles may want to look elsewhere.
You want to watch from an angle While the Bravia 9 II's backlight control is excellent, there is still some blooming present when viewed off-center. OLED is still the best for off-axis viewing
Also Consider
Sony Bravia 9 II
Samsung R95H
Sony Bravia 7 II
LG G6
Price (65-inch)
$3,599 / £3,499 / AU$5,995
$2,799 / £2,899 / AU$4,495
$2,599 / £2,299 / AU$3,299
$2,499 / £2,699 / AU$4,995
Screen type
RGB LED
Micro RGB
RGB LED
OLED
Refresh rate
120Hz
165Hz
120Hz
165Hz
HDR support
Dolby Vision, HDR10, HLG
HDR10+, HDR10, HLG
Dolby Vision, HDR10, HLG
Dolby Vision, HDR10, HLG
Smart TV
Google TV
One UI Tizen
Google TV
webOS 26
HDMI ports
4 (2x HDMI 2.1)
4 x HDMI 2.1
4 (2x HDMI 2.1)
4x HDMI 2.1
Samsung R95H One of the Sony Bravia 9 II's main rivals, the Samsung R95H delivers bold colors, solid black levels and an effective anti-glare screen. The R95H is cheaper, and has a more comprehensive feature set for gaming too. That being said, the Bravia 9 II's picture and sound quality are better — but the price difference may be too great for some to ignore.
Sony Bravia 7 II The Bravia 7 II is the step-down model to the Bravia 9 II, and while the 7 II doesn't have the 9 II's full picture or sound quality, what it does deliver, at a much cheaper price, is very good indeed, showing what RGB is capable of at this price point. People with bright rooms may want to look elsewhere however, as the Bravia 7 II isn't great with reflections.
LG G6 LG's flagship OLED is cheaper than the Bravia 9 II, and has the same rich colors, fantastic contrast and black tones. It's even great for bright rooms, showing only minimal reflections. The G6 is also better for gaming, and comes at a cheaper price. OLED fans will want to pick this, while non-OLED fans may want to opt for the Bravia 9 II.
Tested using different SDR and HDR sources and in varying lighting conditions
Measurements taken using Portrait Displays' Calman color calibration software
After spending a few days establishing the Bravia 9 II's best pictures modes (Cinema for brighter rooms and HDR content, Professional for dark-room viewing, and Standard for sport and SDR TV), I watched reference movies and TV shows to test its picture and sound quality.
I used 4K Blu-rays to analyze its color, contrast, detail, motion and backlight control. I also used 4K Blu-rays to analyze its built-in sound quality, in particular using a scene from The Batman.
For gaming, I used an Xbox Series X and played Battlefield V.
Once these tests were complete, I took measurements using a Murideo Six G 8K test pattern generator, Jeti Spectral 15VA Spectroradiometer and Klein K-10A colorimeter (profiled together) and used Portrait Displays' Calman color calibration software to take said measurements.
Brightness measurements were taken using white window patterns in both SDR and HDR, noting results for windows 1-100% in size. These were taken in the TV's Professional, Cinema and Standard modes.
Next, I analyzed its HDR color gamut coverage using both the UHDA-P3 (the standard used to master 4K Blu-ray and digital cinema releases) and BT.2020 ( a wide color-gamut format) color spaces.
After this, I measured the Bravia 9 II's color and grayscale accuracy using the Delta-E averages, which measures the difference between the source test pattern and what's shown on screen.
Finally, I analyzed its input lag in both 4K 60Hz and 1080p 120Hz using a Leo Bodnar 4K HMDI Input Lag Tester.
