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Intel has announced the new Intel Xeon 600 processors for client workstations, supplying a complete update to its high-end workstation platform with the Intel W890 chipset. This latest generation offers more cores, better PCIe connectivity, faster memory support, and improved power efficiency compared to previous models.  

The need for high-performance compute capabilities is increasing daily across a wide range of industries. With the Intel Xeon 600 processors for workstations, we are delivering the platform professionals’ needs in their daily workflows. Incredible performance, effectiveness, extended AI compute features, a host of vPro technologies, and strong connectivity make this platform the right selection for professionals who depend on the performance and capabilities only a high-end workstation can deliver.  

Hector Guevarez, Director of Workstation Segment Client Computing Group.  

The new Intel Xeon 6000 processors for workstations offer many benefits across fields such as Data Science, AI Development, Engineering Simulation, and Media Content Creation. They offer much better multi-thread performance than previous models, stronger I/O capabilities, better wired and wireless connections, and more support for advanced AI training and inference tasks.  

Built with Intel 3 process technology and Redwood Core+ core architecture. The Intel Xeon 600 processors for workstations now have more cores than across the lineup. For example, the 86-core Intel Xeon 698x delivers up to 61% higher multi-thread performance at the same power level compared to the previous 64-core W3595X1.  

Platform specifications include: 

• The Intel Zion 600 processors for workstations offer up to 86 cores and a 4.8 GHz turbo frequency, which means up to 9 times better single-thread and 61 times better multi-thread performance vs. the previous generation of Intel processors.  

• With added FP16 data type support in Intel® AMX®, these processors can handle AI training and inference tasks much faster, up to 17% better performance in AI and machine learning workloads compared to the previous generation.  

• They support up to 128 CPUs and PCIe Gen 5.0 lanes, providing strong connectivity for multiple GPUs, SSDs, and network cards to meet your workflow needs.  

• These processors support up to 8 channels of DDR5 RDIMM memory at up to 6400 MT/s, an improvement from 4800 MT/s in the previous generation. They now also support DDR5 RDIMM memory at up to 8000 MT/s, which greatly boosts performance for memory-intensive tasks.  

• Continued support for ECC memory and RAS technologies that improve the integrity of critical data and system dependability.  

• Continue to support ECC memory and RAS technologies, which help protect important data and keep systems reliable. Voltage baseline and max voltage limits reporting.  

• (new) per CD IE and per core performance limit reporting  

• Processor Core Tuning  

• AVX2, AVX512, and TMUL negative ratio offset tuning.  

• Per CDIe Ring/Mesh Tuning  

• Intel Turbo Boost 2.0 frequency tuning.  

• Intel Turbo Boost Max Technology 3.0 Tuning  

• They come with built-in Intel Wi-Fi 6E and support for Intel Wi-Fi 7, providing the latest and fastest network connectivity.  

• These processors include Intel vPro technologies for hardware-based security, such as multi-key memory encryption from version control and Intel one-click recovery, making it easier to deploy systems in enterprise settings.  

With the launch of the new Intel Xeon 600 processors for workstations, Intel sets a new record for overclocking on workstation platforms. Working with ASUS and its overclocking system, Intel has set several new world overclocking records at launch.  

Powered by an Intel Xeon 698X processor and ASUS Pro, WS-W890E Sage SE motherboard, Intel and ASUS have set new overclocking world records across 10 benchmarks, including Geekbench 4 multi-core, Geekbench 5 multi-core, and Cruncher (up to 28B). Additionally, the partnership has led to another 10 global first-place submissions, including Geekbench 3 multi-core, Cinebench R20, Cinebench R23, Cinebench R15, and Y Cruncher (up to 100B).  

When it’s available: Intel Xeon 60, the Intel Xeon 600 processors for workstations will be available from OEM and SI partners, as well as boxed versions, starting in late March 2026 or through retail.

Source: Intel Launches new Intel® Xeon® 600 Processors for Workstation 

Scrolling is a theme of the past. Meta has been rolling out major updates to the Reels algorithm across the US for years. Watch time decided which creators became popular. Now Meta’s latest data shows a big change: direct message (DM) shares are the most important signal.  

For US creators, this is more than a small update. It completely changes what it takes to succeed. If people watch your content but don’t share it with friends, your niche will stop growing.  

The Data Behind The Shift: While Sends Per Reach Is King 

Instagram Head Adam Mosseri has openly discussed the platform’s move forward to more meaningful social connections in his 2026 algorithm briefing. He said that cents per reach is a far stronger factor for distribution than likes, comments, or even completion rates.  

The idea is simple: if someone watches a 15-second Reel to the end, they enjoyed it, but if they share it in a DM, they are starting a conversation. In 2026, Meta’s main goal is to keep users engaged and encourage DM activity. That is now the best way to do that. The DM inbox is now more active than the main feed.  

The New Ranking Hierarchy (March 2026) 

To succeed in these new roles, creators need to know which interactions matter most. Social media strategists have outlined the 2026 Reels ranking as follows:  

1. DM shares (the gold standard): sharing privately is now the top sign of valuable content.  

2. Saves: shows the content is worth coming back to or is evergreen.  

3. Watching time for 30 seconds of a 90-second video is now better than a five-second look at a ten-second video.  

4. Deep comments go wrong. These are comments that start a fad or get a reply from the creator.  

5. Lights go on now, seen as the weakest signal and mostly just a vanity metric.  

Strategy: How To Engineer Shareable Content 

The algorithm does not reward creators of shareable content. They need to change how they make content. It’s not simply about catching attention anymore; you want to give people something to say, like “this reminded me of you”.  

• Relatable Relativity: reels that get shared most often in 2026 focus on specific, relatable experiences. It could be a work-from-home habit or a unique parenting challenge. The aim is to make viewers feel validated, so they want to send it to someone who relates.  

• The educational Save and Share: lists of hacks and tutorials are becoming more popular than ever. If you share a simple three-step solution to a common problem, people will share it to help others or save it for themselves.  

• Using Trial Reels or Test Hooks: Meta has launched trial Reels across the US. This feature lets you show a video to people who don’t follow you for 24 hours before it appears on your profile. Use this to see which hooks get the most shares. If a trial Reel gets a lot of shares compared to views, it could be because it gets a big boost when you post it for everyone.  

The Original Penalty 

It has also sharpened its aggregator penalty in this update. Reels with watermarks from competing platforms (such as TikTok or slop-content). Unorganized clips with no creative editing are being strictly throttled. The 2026 algorithm uses AI-powered recognition to detect if a video is recycled. Original audio, clear voiceovers, and native-identity editing tools are now required to reach the explored page.  

Managing New Ads and Algorithm Settings 

Now, US users have more control over the Your Algorithm hub. They can choose to downrank some topics or prioritize others for creators. Changing topics is riskier than before. If you usually post about workouts but switch to crypto, your main audience may be downranked, so they won’t see your new content even if they follow you.  

Conclusion: The Future Of Distribution 

The takeaway for 2026 is simple: Engagement is now about intent, not only numbers. A brand with 10,000 views and 500 shares will do better than one with 100,000 views but no shares. Creator, your focus ought to shift from “How can I make them watch?” to “How can I make them talk?” Master the DM stuff, and you master the 2026 algorithm.

Source: The Instagram algorithm: How it works and strategies for 2026 

Mobile Conductor: The industry changed direction at CES 2026 when NVIDIA CEO Jensen Huang reviewed the Vera CPU’s technical details, while most of the industry focused on the Blackwell GPU supplies and HBM3e production in 2025. NVIDIA CEO Jensen Huang was working behind the scenes to develop the chip that would transform it from a GPU supplier into a full-stack data center architect.  

Vera CPU is not simply an upgrade to the base architecture. It is a purpose-built agent processor made to remove the last bottleneck in the AI pipeline: serial processing and data management for autonomous agents, with mass production starting this quarter. Vera is set to become the core of the most advanced supercomputers in 2026.  

The Architecture: 88 Olympus Cores 

At the core of Vera lies the Olympus microarchitecture: a custom-designed ARMv9.4a implementation, unlike its predecessor, which had 72 cores. Vera features 88 high-performance cores per die. These aren’t generic, off-the-shelf ARM designs; they are AI-hardened cores with a specific focus on branch-forecasting accuracy for the intricate decision-making trees used by agentic AI.  

The Vera CPU has 512 MB of L3 cache, which is 40% more than the Grace architecture. This on-chip memory helps lower the delay of KV cache lookups, which is important for long context inference. In a dual-socket setup called the Vera-Vera Super Chip, one node can use 176 cores, providing the power needed to handle the heavy data traffic in a Rubin-class data center.  

Breaking the Memory Wall: HBM4 Integration 

Vera’s biggest challenge from standard CPUs is its memory system. Instead of using LPDDR5X, NVIDIA has built HBM4 (high bandwidth memory) right into the CPU. This builds a shared memory pool with this Rubin GPU and offers 2.2 TB/s of memory bandwidth.  

Our system in the news: this unified memory design is a big deal! The CPU can use the GPU’s memory directly without extra copying. When an AI agent needs to perform tasks such as database searches, API calls, and model updates, the Vera CPU manages everything without the usual PCIe slowdown. NVIDIA says this makes the agentic processing five times more efficient than x86-based head nodes.  

The Agentic Instruction Set 

Vera stands out among competitors, such as Intel’s 2026 Diamond Rapids and AMD’s Turin, thanks to its support for enemy agent extensions. This is a special instruction set designed for the chain-of-thought processing used in models such as GPT-5.2 and Gemini 3.1.  

These extensions speed up the token-to-action process when a model needs to use a tool, such as doing a web search or writing to a database. The Vera CPU sends that work to a special secure logic engine on the chip. The main cores continue to focus on high-level reasoning, helping avoid the slowdowns often seen in complex autonomous tasks.  

Connectivity: NVLink 6 and CXL 3.1 

Vera is the first CPU to support NVLink 6, providing a fast 3.6 TB/s connection to the Rubin GPU. It also uses CXL 3.1 for memory pooling in 2026 computers. Vera will let multiple racks share a large global memory pool, enabling the training of world models with more than 50 trillion parameters.  

By adding Bluefield DPU logic to Vera, networking tasks such as encryption, packet inspection, and storage visualization are managed at the processor’s edge. This frees up about 15% of the core capacity that was previously lost to data center overhead.  

Power Efficiency: The 2nm Milestone 

Manufactured on a refined 2nm-class process, Vera is optimized for high-performance computing, with a TDP of about 450 W for the full superchip. It delivers three times the performance of a 2024 x86 server and uses 40% less power when idle for 2026 and 2027. This power profile is the difference between a project being viable or being cancelled due to grid constraints. NVIDIA’s green supercomputing initiative is built entirely on Vera’s ability to do more with every joule of energy.  

The 2026 Supercomputer Roadmap 

The first videos of the Vera CPU will be from the Vera Rubin Superchip, which pairs one Vera CPU with one Rubin GPU. These will be installed in the NVL72 rack, which works as a single large AI processor.  

Research institutions and cloud providers such as AWS, Azure, and Google Cloud have already placed priority orders for Vera-based systems. These machines are expected to lead to breakthroughs in several fields:  

• Climate Science Coron Running Earth 2 Simulations with 10X Higher Granularity  

• Drug discovery: Simulating protein folding in real time using agentic feedback loops.  

• Autonomous Systems Column: Training the Alfa Romeo R1 model for level 3 self-driving vehicles  

Conclusion: The end of the general-purpose era 

A range of CPU specs shows that general-purpose server CPUs are no longer sufficient for high-end AI workloads. By designing a chip for AI agents as its primary users, NVIDIA has built a specialized powerhouse that other companies will likely try to match over the next few years.  

Vera is far more than a CPU. It leads the AI system as production begins in early 2026. Enterprises now need to focus less on which CPU to buy and more on how quickly they can adopt the Vera-Rubin architecture to remain competitive in a world driven by autonomous agentic intelligence.

Source: Built for Accelerated Systems at Scale 

OpenAI and Amazon announced a multi-year partnership to speed up AI innovation for businesses, startups, and consumers worldwide. Amazon plans to invest $50 billion in OpenAI, starting with $15 billion now and an additional $35 billion over the next 4 months, subject to certain conditions.  

Working together to deliver advanced AI tools to businesses worldwide, OpenAI and Amazon are building a stateful runtime environment using OpenAI’s models. The new environment will be available through Amazon Bedrock.  

Stateful developer environments represent the next step in using advanced AI models. They allow models to access resources such as computing power, memory, and identity. With a stateful runtime environment, developers can retain context, preserve previous work, use multiple software tools and data sources, and access computing resources. These environments are built to support active projects and workflows.  

These stateful developer environments will be optimized for AWS’s infrastructure and will work with Amazon Bedrock Agent Core and other AWS services. This way, customers’ AI applications and agents will run smoothly alongside their other AWS applications. The stateful runtime environment is expected to launch in the next few months.  

Making OpenAI’s most advanced enterprise platform available to AWS customers 

AWS will be the only third-party cloud provider for OpenAI Frontier. This will give more businesses access to OpenAI’s most advanced enterprise platform as demand for AI grows across industries.  

Let’s organizations build, deploy, and manage teams of AI agents that work across real business systems with shared context, built-in governance, and strong security. Companies do not need to manage underlying infrastructure as businesses move AI from testing to production. Frontier makes it easy to quickly, securely, and at scale add powerful AI to existing workflows.  

OpenAI will use Trainium’s computing power to meet the growing demand from Amazon customers. OpenAI and AWS are increasing their current $38 billion multi-year agreement by another $100 billion over the next 8 years. As part of this, OpenAI will use about 2 GW of Trainium capacity through AWS to support Stateful Runtime, Frontier, and other advanced workloads. This deal will help lower costs and make large-scale AI production more efficient.  

With this agreement, OpenAI secures long-term computing capacity and works with AWS to use custom-built silicon chips with its larger computing system. This setup lets businesses use AI on demand without managing the underlying infrastructure.  

This Commitment covers both Tranium 3 and the upcoming Tranium 4 chips, which will support many advanced AI workloads. Tranium 4 is expected to be available in 2027 and will offer much better performance, including higher FP4 compute power, more memory bandwidth, and greater high-bandwidth memory capacity to support more powerful AI systems.  

Custom Models Will Be Available to Support Amazon’s Customer-Facing Applications 

OpenAI and Amazon will collaborate to develop custom models for Amazon developers to use in customer-facing applications. Amazon teams will be able to adapt OpenAI models for different AI products and agents that serve customers directly. These new models will add to the options already available to Amazon developers, like the Nova family, giving teams more tools to build and deliver at scale.  

OpenAI and Amazon share a belief that AI should show up in ways that are practical and genuinely useful for people, said Sam Altman, co-founder and CEO of OpenAI. Combining OpenAI’s models with Amazon’s infrastructure and worldwide reach helps us put powerful AI into the hands of businesses and users at a real scale.  

We have many developers and companies eager to run devices powered by OpenAI models on AWS. Our unique collaboration with OpenAI to provide stateful runtime environments will change what’s possible for customers building AI apps and agents, Andy Jassy, President and CEO of Amazon, said. We continue to be impressed with what OpenAI is building, and we’re excited not only about their decision to go big on our custom AI silicon (Trainium), but also about our opportunity to invest in the company and partner with them over the long term.

Source: OpenAI and Amazon Announce Strategic Partnership 

Google has started the countdown for developers and early enterprise users. In a technical bulletin released this morning, the company confirmed that the Gemini 3 Pro Preview, its experimental platform for the latest multi-model architecture, will be shut down on March 9. By March 26, 2026, users will have seven days to move their production workflows and store data to the stable Gemini 3.1 environment.  

This shutdown ends a fast-paced testing phase that started in late 2025. The preview was an important space for testing Gemini 3’s agentic reasoning features, but now Google is focusing its computing resources on the more efficient, optimized 3.1 production version.  

The Migration Mandate: What Happens on March 9 

Teams using the Gemini 3 Pro Preview endpoint through Google AI Studio or Vertex AI must meet the deadline beginning at 12:00 a.m. PT on March 9. Any API calls to the preview models will return 410 (GONE) errors.  

This transition entails more than just changing a name. Developers need to prepare for several important changes:   

• End Point Redirection: Update all calls to use Gemini 3.1 Pro or Gemini 3.1 Ultra stable endpoints.  

• Contest window adjustments: The previous supported a 2-million-token window, but the 3.1 stable release has a better contest recall (the needle-in-the-haystack metric). However, you may need to update billing settings for high-value token use.  

• System Instructions column: The 3.1 architecture has improved safety and is better for those instructions. Early feedback indicates that prompts designed for a more permissive purview may need minor adjustments to maintain consistent output in the stable version.  

Why Is Google Moving So Fast 

The seven-day warning shows the competitive pressure of the Spring AI wars. In 2026, OpenAI retired older models to focus on GPT-5.2, and NVIDIA’s Rubin platform has lowered inference costs. Google now needs to move users to its most hardware-efficient models.  

Gemini 3.1 is much better optimized for the latest TPU v6 clusters by closing the preview. Google is freeing up a large amount of computing power for the upcoming Gemini 3 Live and Project Astra integrations, planned for late Q2.  

Critical Tasks for Developers 

Prevent service interruptions. Technical leads should focus on these tasks before next Monday:  

• Audit API keys: Identify every new application instance still using the preview model.  

• Deadshot Fine Tuning Coulombe: If you have fine-tuned versions of the Gemini 3 Pro review, they will not transfer automatically. You need to start fine-tuning jobs again on the Gemini 3.1 base model right away.  

• Evaluate output latency. The stable 3.1 version usually gives a 15% faster time-to-first token (TTFT), which was used this week to run A/B tests and ensure your UI/UX can handle faster response times without causing issues in downstream parsing.  

The Roadmap Ahead 

The preview is the last step before the full launch of Gemini 3 Ultra for Enterprise clients. The stable 3.1 environment provides enterprise-grade reliability (EGR rating) needed for sectors with strict compliance requirements, such as finance and health care.  

While a 7-day window is aggressive, it shows Google’s commitment to a stronger, more efficient AI lineup. The time for experimental review work is ending, and the era of deployed Agentic AI has begun. Script templates for batch-migrating your Vertex AI model configurations to the Gemini 3.1 stable standpoint.

Source:  Gemini 3.1 Pro

Key points 

• President Donald Trump is set to meet with leaders from Amazon, Google, Meta, Microsoft, XAI, Oracle, and OpenAI at the White House next week.  

• These companies will sign a pledge to power their data centers with their own energy.  

• The centers are facing criticism because many people blame higher utility bills on the large amount of electricity these facilities use.  

Trump tells major tech companies to generate their own power for AI data centers and launches a new ratepayer protection pledge to help control rising electricity prices in the U.S.  

During yesterday’s State of the Union address, President Trump discussed the rising power costs caused by large-scale AI expansion and offered a solution. He announced a new ratepayer protection pledge, saying companies will now have to build their own power plants for data centers and generate their own electricity for AI workloads.  

In recent years, major tech companies have used large data centers to drive the AI boom, building huge sites that run thousands of GPUs. At the same time, these chips require significant energy and must be kept cool, which increases overall power consumption. Until now, these companies have plugged into the grid and bought electricity as usual, but this has put a strain on the system.  

Now people living nearby are paying more for electricity because their area is drawing more power from the grid. We have an old grid; it could never handle the kind of numbers, the amount of electricity that’s needed, said Trump. Last year, one report said energy prices had already risen by as much as 36% in some states, and another suggested the problem could get even worse.  

In 2028, data centers are expected to use 12% of the country’s total power, up from 4% in 2018. This has a big impact on regular people, who must pay more for the same amount of household electricity and now live near constantly noisy facilities. The companies responsible for the increases are less affected by higher prices.  

Tech companies want us to imagine a future in which their AI can tell us what to cook, wear, and do in our free time. The advice is often questionable. For example, would you really want to put glue on your pizza? Still, we are told that this is the next step in computing. Meanwhile, the huge amount of electricity needed to answer these questions is already pushing up consumer electricity prices nationwide, since the power grid isn’t ready for the sudden surge in demand.  

This week reports that higher energy use from data centers has already led to a 6.5% rise in energy prices between May 2024 and May 2025. That’s just the average. Connecticut saw an 18.4% increase, which means prices jumped by 36.3%. These numbers are likely to keep going up as tech companies expand their AI infrastructure.  

To keep pace, utilities are increasingly relying on ageing fossil-fuel plants to generate enough electricity to meet crushing demand. Newsweek says Dominion Energy, which serves much of Virginia, has asked regulators to require large-node customers to pay a fair share of grid upgrade costs without reform. Electricity prices in parts of Virginia are expected to climb as much as 25% by 2030.  

Major tech companies will join President Trump at the White House next week to formally sign the ratepayer protection pledge that he announced during his historic State of the Union address, White House spokeswoman Taylor Rodgers said on Wednesday.  

Under this bold initiative, these massive companies will build, bring in, or buy their own power supply for new AI data centers, ensuring that Americans’ electricity bills will not increase as demand grows, she added.  

Data centers, which are key to expanding computing power and supporting the AI innovation Trump supports, have faced public backlash. Many people worry they will end up paying higher bills for these energy-hungry facilities.  

President Donald Trump is committed to ensuring American AI dominance while simultaneously lowering costs for working families, Rogers said.

Sources: Trump orders Big Tech to generate its own power for AI data centers — reveals new ‘ratepayer protection pledge’ to curb rising electricity prices in the US 

Google, Microsoft, Amazon to pay their own data centre power bills? Trump summons big tech leaders to woo public

OpenAI is retiring from the first version of its video generation tool.  

Access to SORA1 will end for users in the United States on March 13th, 2026. This change is part of the transition to SORA2, which replaces the original one model.  

Important Information About the Shutdown: 

• What you need to do: Be sure to expect your data, including generations, likes, and social activity, before the shutdown date. After that, this content will no longer be available.  

• To export your data, go to the Data Controls tab in your web settings or visit the Privacy Portal to download your information.  

• About SORA2: The new SORA2 model includes improved features such as greater accuracy in modeling physical loss and the ability to generate longer videos.  

SORA1 data will be permanently deleted shortly after the March 13 deadline.  

SORA is changing, and soon everyone will use the updated version, SORA2.  

Now, some people can use either SORA2, the current version, or SORA1, the older version that supports older video and image features. As we continue to improve SORA, SORA1 will be phased out.  

In the United States, Sora One will no longer be available on March 13th, 2026. If you have made content in Sora One, please export your data before then.  

How can I access content I created in SORA1? 

After SORA1 is retired, your SORA1 creations and social activity, such as likes and remixes, will be lost. To save your content, export your SORA data before SORA1 is removed.  

To export your data on Sora:  

• Open Sora on the web.  

• Click on the bottom-right corner of the page, then Settings.  

• In the Data Controls tab, select Export Data and submit your request.  

If you are on old Sora:  

• Click on your profile icon on the top right of the page.  

• Select Settings.  

• In the Data Controls tab, select Export data and submit your request.  

You can also download individual images and videos from your library by hovering over your media, clicking it, and selecting download.  

Your account details and content will be part of the export. The data will be sent to your registered email as a file. You can download; processing might take a while, but you will get a notification when it is ready. Your export will include data from SORA1, SORA2, and ChatGPT.  

SORA1 has already been removed in your region. Data export will be available for a limited time before it is permanently deleted.  

If you are having trouble downloading images and videos, you can visit our privacy portal to request your data.  

• Navigate to the “Make a Privacy Request” button.  

• Click Download My Data.  

• Verify your account.  

• Direct your country of residence to the privacy portal.  

How Can I Create Videos After Sora 1 Is Retired 

Then keep making videos with Sora 2, which will be the main Sora version in the US after March 13, 2026.  

How Can I Create Images After Sora 1 Is Retired 

Once SORA1 is removed, you won’t be able to create images in SORA anymore.  

You can still make images using ChatGPT. Find out more about how to create images in ChatGPT.  

When will SORA1 be removed? 

In the US, Sora 1 will be removed on March 13, 2026. After that, Sora will always open in Sora 2, and you won’t be able to go back to Sora 1.  

SORA2 is not yet available in your country. You can keep using SORA1 until SORA2 launches. We will let you know when SORA2 is coming to more countries.  

What happens to my data after SORA1 is fully deprecated? 

If you have Sora 1 creation, we will email you before the last chance to export your data. You will have a limited time to export after Sora 1 is fully removed. After that, you won’t be able to get your Sora 1 content anymore.  

Why Is Sora1 Being Deprecated 

SORA1 uses outdated technology by forcing everyone onto a single version. We can make SORA simpler and keep improving SORA2 for both web and mobile.

Source: How can I access content I created in Sora 1? 

Next week kicks off with Apple’s first big product announcements of 2026. Tim Cook has hinted at a big week ahead using the Apple Launch hashtag. Apple has media events planned in New York, Shanghai, and London on Wednesday, March. 

We are looking forward to the iPhone 17E, a brand new budget MacBook, and some smaller updates to the Mac and iPad lines.  

Low-Cost MacBook 

Viewers suggest the new MacBook will look similar to the MacBook Air. It is expected to have an aluminum body in several colors and a display that’s either 12.9 or 13 inches, depending on which report you believe.  

A low-cost MacBook might be thin and light, since it’s expected to use a low-power A-series chip that doesn’t require much cooling, though this hasn’t been confirmed yet. Apple once had a 12-inch MacBook with a slim design and a low-power Core M chip, so that this new model could be a modern version of that older machine.  

Thinner and lighter typically means more expensive, while Apple products, so a super slim design might not be what Apple is optimizing for. Making the low-cost MacBook thinner than the MacBook Air could confuse the MacBook lineup.  

With the low-cost iPad, Apple keeps the price down by using older display technology that’s not as thin as we see. We see the same strategy with the low-cost MacBook: a thicker chassis and a super-efficient chip mean a long battery life, which is ideal for a learning setting.  

The budget MacBook will likely have:  

• a dimmer screen  

• no True Tone  

• no backlit keyboard  

• slower SSD speeds  

• and no N1 chip  

Colors 

The MacBook will come in a selection of fun colors, and Apple has tested light yellow, light green, blue, pink, silver, and dark gray, according to Bloomberg. Not all of those colors are likely to ship, but it sounds like we’ll get at least four of them.  

Analyst Ming-Chi Kuo expects the MacBook to come in yellow, silver, blue, and pink, the same colors as the iPad. The Book is planned to use its own chip rather than an M-Series Mac chip. Apple is planning to use an A-Series chip. The low-cost MacBook is expected to use the A18 Pro chip, which Apple first debuted in the iPhone 16 Pro.  

The A18 Pro uses a second-generation 3nm process. It has a 6-core CPU with four performance cores and two efficiency cores, along with a 6-core GPU and a 16-core Neural Engine for AI-based tasks, as shown in Geekbench benchmarks. The A18 Pro has an average single-core score of 3451 and a multi-core score of 8572. For comparison, the M4 iPad Pro scores 3694 in single-core and 13732 in multi-core. (Apple’s next MacBook Air will use the M5 chip.  

The A18 Pro is faster than the M1, which Apple used in cheaper MacBook Air models for years. In single-core performance, the A18 MacBook would be close to the M4 chips in Macs and iPads, but multi-core performance would still lag. The A18 chip would be more than powerful enough for day-to-day use, such as web browsing, document creation, watching videos, and even light photo and video editing. It won’t be ideal for system-intensive games or tasks like 4K video editing and 3D rendering, but it will do almost anything an iPhone or iPad can do.  

Apple is designing a budget MacBook for students, aiming to offer an Apple version of the affordable Chromebooks many students use.  

RAM 

Max starts with 16 GB of RAM, but the iPhone 16 Pro has 8 GB. The minimum for Apple Intelligence: we can expect an A18 Pro MacBook to have at least 8 GB of RAM to support Apple Intelligence, but Apple may equip it with 16 GB, as all Macs do.  

Storage 

The MacBook Air starts at 256 GB of storage, but Apple might launch the budget MacBook with just 128 GB.  

Ports  

The A18 Pro chip in the iPhone 16 models does not support Thunderbolt, so the MacBook will only use USB-C at 10 GB and will not reach Thunderbolt speeds. This means display connectivity will be limited, and the A18 Pro MacBook will probably support just one external display.  

Price 

The MacBook Air starts at $999, but the new Low-Cost MacBook is expected to be priced significantly lower.  

Apple will likely not want to price the new MacBook much lower than its iPads. The low-cost iPad with the A16 chip starts at $349, and the iPad Air with the M2 chip starts at $599. Pricing the MacBook between $599 and $799 would keep it less expensive than the MacBook Air or iPad Pro but just above or around the iPad Air’s price, $599, which would match the price of some popular Chromebooks often used in schools. A $699 or $799 price would be in a similar range but a bit more premium. $599 is also the price of the iPhone 17e, Apple’s most affordable phone, which uses a slightly less powerful A18 chip.  

iPhone 17e 

The iPhone 16e, which launched in February 2025, is due for a refresh. The iPhone 17e is getting some useful upgrades over the iPhone 16e, making it even more worth the purchase price.  

Design 

The iPhone 17E will have a design similar to the iPhone 16E with a 6.1-inch display, a single rear camera, and black and white color options.  

Display 

The iPhone 17E is expected to use the same display as the iPhone 16E, so that it will have a 60 Hz refresh rate. Apple added 120 Hz Pro Motion to the standard iPhone 17 in 2025. This feature is not expected on the more affordable iPhone 17E.  

The iPhone 17e will remain the only new iPhone without 120 Hz support. These improve video playback and make scrolling smoother when viewing web pages.  

The iPhone 16e does not have an Always-On Display, and this is likely to change with the iPhone 17e. Always-On Displays require an OLED screen with a minimum brightness of 1 nit, which is only available on Apple’s more expensive devices. The Apple iPhone 17e will also lack HDR and have lower brightness compared to flagship models. It has eliminated it in its new flagship phones, but some rumors suggest that the iPhone 17e will feature a dynamic island instead of a notch, giving it a more modern look.  

The Dynamic Align is a peel-shaped cutout on the iPhone’s display that houses the TrueDepth and front-facing cameras. It uses less screen space than the notch and is better integrated into the iPhone’s design. More’s indicated that we could get a Dynamic Align. Other rumors suggest the iPhone 17e will retain the notch, so the Dynamic Align upgrade isn’t guaranteed.  

A19 Chip 

The iPhone 17e will use Apple’s A19 chip, the same one found in the iPhone 17. This chip is built on an improved M3P3DMX process and offers a 5-10% performance boost over the A18 chip.  

Apple could be planning to use a downclocked version of the A19 chip in the iPhone 17e, meaning its performance would not fully match that of the iPhone 17’s 5-core GPU. Instead of a 5-core GPU like the one in the iPhone 16, the iPhone 17e could get a similar downgrade.  

Aside from the improved CPU and GPU, the A19 features an upgraded display engine, an image signal processor, and a neural engine for enhanced AI performance. Every GPU core features a neural accelerator to boost the performance of local AI models. The iPhone 17e is expected to have 8GB of RAM, just like the iPhone 16e. Other Apple models come with 12GB.  

MagSafe Compatibility 

The iPhone 16 does not have a MagSafe charging ring, but it is expected to add this feature. iPhones have used MagSafe since the iPhone12, so there is a wide array of MagSafe cases and accessories. The iPhone 16e is not compatible with these accessories, which is a major limitation.   

Without MagSafe, the iPhone 16E can only charge wirelessly at 7.5 W. With MagSafe, charging would increase to at least 15 W. The current iPhone 17 models can charge at 25 W with MagSafe, while the iPhone Air is limited to 20 W.  

Camera 

The iPhone 17e is expected to feature a single 48-megapixel wide-angle camera on the back, with no upgrades rumored. The iPhone 16e lacks a camera control button, and there is no indication that Apple will add one to the iPhone 17e.  

The iPhone 17 model features an upgraded 18-megapixel center-stage front-facing camera, but rumors suggest the iPhone 17e will continue to use the same 12-megapixel front camera as the iPhone 16e.  

C1X model and N1 chip 

The iPhone 17e will adopt Apple’s C1X model. The chip for Apple’s first iPhone, the C1X model, is faster and more efficient than the C1 model used in the iPhone 16e.  

Apple says the C1X modem is up to 2x faster than the C1 and far more energy-efficient than Qualcomm modems.  

Apple could also update the iPhone 17 models with Apple’s Wi-Fi and Bluetooth N1 networking chip, which brings speed and efficiency enhancements and thread support. Leaked Apple code suggests the chip will not only be included in the iPhone 17E to keep costs down, but that Apple also plans to add it to other models.  

Pricing 

The iPhone 16e is priced at $599, and no price changes are expected for the iPhone 17e.  

MacRumors Coverage 

Apple isn’t holding an event for the new announcements, so there won’t be a video. Pre-advert new products to be unveiled via press release on Monday, Tuesday, and Wednesday. Stay tuned to MacRumors for details on everything Apple unveils.  

Apple is adding a special experience for members of the media on March 4, 2026, during which we expect Apple to showcase new products. MacRumors will attend and share a hands-on look at what Apple has to offer.  

The special experience will take place at 9 am Eastern Time.

Source: What to Expect From Apple’s Big Week: iPhone 17e, Low-Cost MacBook, New iPads, and More 

AMD Ryzen AI Max+ 395, also known as Strix Halo, is currently the most powerful x86 APU available and offers a big performance boost over other options. It features 16 Gen5 CPU cores, over 50 peak AI TOPS with XDNA2 NPU, and a large integrated GPU with 40 AMD RDNA 3.5 Compute Units. This makes it a major upgrade for high-end thin-and-light devices. You can get the Ryzen AI Max Plus 395 with system memory options ranging from 32 GB to 128 GB of unified memory, with up to 96 GB of that available as VRAM with AMD Variable Graphics Memory.  

The Ryzen AI Max+ 395 performs especially well with consumer AI tasks such as using LM Studio, which LAMA CPP powers. LM Studio is becoming a popular choice for running language models on your own device, even if you have no technical background. It makes it easy to use new AI text and vision models right away.  

The new AMD Ryzen AI Max series, the Strix Halo platform, continues to lead in LM Studio performance.  

As a primal, the model size is dictated by the number of parameters and the precision used. Generally speaking, doubling the number of parameters (on the same architecture) or the precision will also double the model size. Most of our competitors’ current-generation offerings in this space max out at 32 GB of one-package memory. This is enough shared graphics memory to run large language models (up to 16 GB).  

Benchmarking Text and Vision Language Models in LM Studio 

For this comparison, we used the Asus ROG Flow Z13 with 64 GB of unified memory. We limited the language model size to 16 GB so it would work on a competitor’s 32 GB laptop. We measured latency by looking at the time to first token (how long it takes the model to start responding) and tokens per second.  

The results show that the Asus ROG Flow Z13, which uses the integrated Radeon 8060S and 256GB of bandwidth, easily achieves 2.2 times the token throughput of the Intel Arc 140V.  

The performance uplift is very consistent among different model types (whether you are running Chain of Thought, Deep Seek R1, Deep Tales, or Standard models like Microsoft Phi 4) and different parameter sizes.  

In Time-to-First-Token Benchmarks, the AMD Ryzen AI Max+ 3950X processor is up to 4x faster than the competition on smaller models like LAMA 3.2 3B Instruct.  

For larger models with 7 or 8 billion parameters, like Deep Sea R1 Distal Queen 7B and Deep Sea R1 Distal Llama 8B, the Ryzen AI Max+ 395 is up to 9.1 times faster. With 14 billion parameter models, which is about the largest that fits on a standard 32GB laptop, the Asus ROG Flow Z13 is up to 12.2 times faster than a laptop with an Intel Core Ultra 258V. This is more than 10 times faster than the competition.  

The larger the LLM, the faster the AMD Ryzen AI Max+ 395 processor responds to your queries. Whether you are chatting with the model or giving it large summarization tasks with thousands of tokens, the AMD system will respond much more quickly. This advantage grows as the prompt gets longer, so the more demanding the task, the greater the speed difference. The IBM Grand Light Vision is one example, and the recently launched Google Gemma 3 family of models is another, with both providing highly capable vision capabilities to next-generation AMD AI PCs. Both these models run performantly on an AMD Ryzen AI Max+ 395 processor.  

An interesting point to note here: when running vision models, the time to first token also measures how long the model takes to analyze your image. Vision 3.2 3b is up to 4.6x faster in Google JAMA 3 4b and up to 6x faster in Google JAMA 3 12b. The Asus ROG Flow Z13 came with a 64 GB memory option, so it can also effortlessly run the Google JAMA 3 27b vision model, which is currently considered SOTA (state-of-the-art) in vision.  

Another example is running the Deep Seek R1 distal quan32b in 6-bit precision, while 4 bits are the industry standard for most users. Coding often requires higher precision for accuracy. With this setup, you can code a gaming classic in about 5 minutes.

Source: AMD Ryzen™ AI MAX+ 395 Processor: Breakthrough AI Performance in Thin and Light 

Introduce Vision OS 26 today, a major update with new spatial experiences and features for Apple Vision Pro. Every day use feels increasingly immersive and personal, thanks to:  

• Widgets that fit into your space  

• AI-powered spatial scenes that add life-like depth to photos  

• Improve personas and look more natural.  

• Mutual experiences for Vision Pro users in the same room  

Vision OS 26 also adds support for 180-degree, 360-degree, and wide-field-of-view content from Insta360, GoPro, and Canon. While new enterprise APIs enable organizations to create unique experiences on Vision OS, with support for PlayStation VR 2 Sense controllers, players can enjoy a new class of games on Apple Vision Pro 1.  

Apple Vision Pro has set the standard for spatial computing, and with Vision OS 26, we are taking it even further, said Mike Rockwell, Apple’s Vice President of Vision Products Group. We are thrilled for users to try new ways to connect, explore, work together, and enjoy, including customizable apps and widgets, new spatial scenes for photos, and improved personas on Apple Vision Pro.  

Widgets Become Spatial 

Widgets on Apple devices give users personalized information at a glance. With Vision OS 26, widgets become spatial, fitting right into your space and showing up each time you use Apple Vision Pro. You can customize widgets with different frame widths, colors, and depths. New widgets, such as clock, weather, music, and photos, offer unique ways to interact.  

Users can decorate their spaces with favorite widgets, including:  

• stunning panoramas and special photos of their beloved memories  

• clocks with distinctive face designs and quick access to their go-to playlists and songs on Apple Music  

The widgets app helps users find widgets, including those from compatible iOS and iPadOS apps. Developers can also create their own widgets using WidgetKit.  

Enhanced Shared Spiritual Experiences 

Users love how Vision OS lets them connect with family, friends, and colleagues remotely. With Vision OS 26, they can share spatial experiences with fellow Apple Vision Pro users in the same room. They can come together to watch the latest 3D blockbuster, play a spatial game, or collaborate with coworkers. Users can also add remote participants from across the world via FaceTime, enabling connection with people near and far.  

Dassault Systèmes, a leading provider of engineering and 3D design software, is exploiting this capability with its 3D Live app, enabling the visualization of 3D designs both in person and with remote colleagues.  

With Vision OS 26, personas feel more natural and familiar thanks to industry-leading volumetric rendering and machine learning. The all-new personas are now striking in their expressiveness and sharpness, offering a full-size profile view and remarkably accurate hair, lashes, and complexion. Personas are still created on the device in a matter of seconds, and new improvements to the setup process allow users to adjust and preview how their persona looks spatially and even pick glasses from over 1,000 variations.  

Introducing Spatial Scenes 

Noise makes photos look more realistic by using a new generative AI algorithm and computational depth. This creates spatial change with multiple perspectives, so users feel like they can lean in and look around.  

Users can view spatial scenes in photos, the spatial gallery, and Safari apps. Developers can use the Spatial Scene API to make their apps increasingly immersive. Zillow uses this API in their Zillow Immersive App, letting users view homes and apartments with added depth and dimension.  

New Ways To Browse, Play, And Watch 

With spatial browsing in Safari, users can transform articles, remove distractions, and see spatial scenes that come to life as they scroll. Web developers can embed 3D models in web pages so users can shop, browse, and interact with 3D objects directly in Safari.  

Ryzen OS 26 supports native playback of 180-degree, 360-degree, and wide-field-of-view content from Insta360, GoPro, and Canon. Users may enjoy their exciting 2D action footage the way it was meant to be seen. Developers can incorporate this new playback capability into their apps and websites.  

Vision OS 26 now supports the PlayStation VR 2 Sense controller. Developers can create highly engaging games for Apple Vision Pro using features such as advanced motion tracking, finger touch detection, and vibration feedback.  

Enterprise APIs and Tools 

Businesses worldwide are using Spatial Computing on the Apple Vision Pro to improve their processes across design, training, sales, and education. With new team device sharing, organizations can easily set up and manage shared devices. Users can save their eye and hand data, vision description, and access settings to their phone with iPhone iOS 26, then use them on another Vision Pro. This makes sharing devices much simpler.  

The OS 26 now supports Logitech Muse, a spatial accessory made for Apple Vision Pro. It allows for more accurate input and new ways to use collaboration apps like Spatial Analogue.  

Enterprise APIs, such as the new Protected Content API, ensure that only authorized users can view confidential materials, including medical records and business forecasts. These tools also block copying screenshots and screen sharing.

Source: visionOS 26 introduces powerful new spatial experiences for Apple Vision Pro