News
Amazon has discounted the 256GB Samsung Galaxy Tab S11 Ultra to AU$1,597, dropping it by AU$502 from its AU$2,099 RRP.
That's the same price Amazon is charging for the smaller 256GB Galaxy Tab S11, and AU$2 less than the AU$1,599 it costs direct from Samsung.
This isn’t quite the lowest price we’ve seen for the Galaxy Tab S11 Ultra on Amazon, with the 256GB model briefly dropping to AU$1,547 in June.
Still, today’s AU$1,597 price is only AU$50 more, plus the 512GB model is down to AU$1,799 from the AU$2,449 RRP and the 1TB version has dropped from AU$2,949 to AU$2,299.
Thanks to this discount, the 256GB Galaxy Tab S11 Ultra currently costs the same as Amazon’s smaller Galaxy Tab S11, making this the model I’d choose if you've been pining for the larger 14.6-inch display. Otherwise, the standard S11 may still suit you better if portability is the priority — the 128GB version is 21% off RRP, at AU$1,099.View Deal
Amazon is the simpler option if you just want an easy purchase and fast delivery, but other retailers such as JB Hi-Fi also have the S11 Ultra for just a few dollars more.
Another option worth checking out is Samsung’s current trade-in offer, which could work out cheaper if you have an eligible tablet. Samsung is selling the 256GB Galaxy Tab S11 Ultra for AU$1,599 and, until 26 August, is adding a guaranteed AU$500 bonus credit on top of your device’s assessed trade-in value.
One catch – your old tablet must be eligible for Samsung’s trade-in program and retain an assessed value above AU$0.
But, I hear you ask, what makes the Galaxy Tab S11 Ultra truly worth the rather large price tag?
In our Samsung Galaxy Tab S11 Ultra review, we said we’d be “hard-pressed to find a bigger and better display on an Android tablet”. The 14.6-inch AMOLED screen earned a full 5/5 in our testing, with enough brightness for outdoor use, while the included S Pen and expansive display make it particularly well suited to watching movies, gaming and drawing.
Our reviewer also found Samsung’s One UI software made excellent use of the big screen for multitasking, with the tablet handling a video call, game and floating YouTube window at the same time without slowing down.
Battery life impressed too, lasting 11 hours while streaming 1080p video at full brightness, helping explain why the S11 Ultra remains our best premium Android tablet pick in our buying guide.
No tablet is perfect, and the main caveat for the S11 Ultra from our testing is the MediaTek Dimensity 9400+ chipset, which we found disappointing considering the RRP is over AU$2,000.
Yes, it's a clear improvement over the Tab S10 Ultra and handles games well, but it falls well short of competitors like the M5 iPad Pro for outright processing power.
That said, while the current AU$502 discount does not fix the performance shortfall, at AU$1,597 rather than AU$2,099, it makes our review criticisms considerably easier to swallow. Especially when the closest equivalent iPad Pro is AU$2,599.
So overall, I think that at the current discount, the S11 Ultra is an easy recommendation for those who want the biggest possible screen for movies, games, drawing and multitasking.
Artificial Intelligence (AI) is rapidly evolving. Across industries, many organizations are increasingly deploying AI into systems that must run continuously, securely, and at scale.
As AI adoption accelerates, one thing is becoming clear: infrastructure planning cannot wait.
AI workloads are becoming more interconnected, distributed, and operationally integrated across cloud, data center, and edge environments. Infrastructure planning now requires organizations to align compute, networking, software, memory, and operational requirements across increasingly complex environments.
As a result, many enterprises are beginning infrastructure planning sooner rather than later.
The cost of waitingAs AI becomes more integrated into everyday business operations through continuous inference and agentic AI systems, infrastructure demands are evolving significantly.
Modern AI deployments increasingly require:
- Continuous inference running around the clock
- Multi-agent systems coordinating across applications and databases
- Real-time orchestration across cloud, data center, and edge environments
- Strong governance, security, and operational efficiency
These workloads require more than raw compute performance. They require balanced infrastructure where compute, networking, software, memory, and operational workflows work cohesively at scale.
Because of this, enterprises are beginning AI infrastructure planning earlier, recognizing that planning, testing, and Proof of Concepts (PoCs) for complex systems like this take time.
At the same time, the cost of delaying AI infrastructure planning is becoming more apparent. Delays can slow deployment readiness and postpone AI-driven benefits such as productivity gains and operational automation. As AI demand continues to rise, organizations are prioritizing earlier planning to secure the compute capacity needed to support long-term AI growth.
As AI infrastructure becomes more complex, infrastructure planning needs to begin earlier than traditional IT upgrade cycles. Evaluating workloads, validating deployment models, and ensuring scalability across environments takes time and time is of the essence if we want to be ahead of our competitors.
AI is now a systems challengeThe conversation around AI infrastructure often begins with Graphics Processing Units (GPUs). But as deployments scale, AI performance depends not on individual components, but on how the entire system operates together.
Modern AI infrastructure relies on Central Processing Units (CPUs) for orchestration and data movement, GPUs for large-scale parallel compute, high-speed networking for low-latency communication across systems, and open software platforms for portability and scalability.
As AI systems become more distributed and inference-driven, orchestration and system balance become critical. CPUs play a pivotal role in managing workload coordination, memory access, and GPU utilization, ensuring infrastructure operates efficiently under sustained demand.
This shift reflects a broader industry reality: AI is no longer just a GPU problem. It is a full-stack infrastructure challenge that organization must tackle early on.
Planning for distributed AIAI is also scaling in multiple directions at once.
Some workloads are expanding into large, centralized clusters, while others are moving closer to where data is generated – including edge deployments such as in factories or hospitals, and AI-enabled endpoints like the PCs.
For organizations, this creates unique infrastructure considerations around hybrid cloud, on-premises deployments, edge AI, compliance, and latency-sensitive applications.
This diversity underscores the importance of infrastructure strategies designed for modularity, portability, and adaptability that necessitates upfront planning.
Openness and flexibility matter more than everAs AI innovation accelerates, organizations are prioritizing infrastructure flexibility to support rapidly evolving models, frameworks, and deployment environments.
Open ecosystems can reduce integration complexity while supporting broader compatibility across software frameworks, cloud environments, and deployment architectures. They also provide greater flexibility to evolve infrastructure strategies over time while helping avoid the migration costs that can come with highly closed or single-vendor environments.
For many organizations, openness is no longer just a developer preference. It is becoming an important consideration for balancing performance, operational efficiency, cost optimization, and long-term infrastructure investment.
This is another reason infrastructure planning must happen early. Building AI environments that remain scalable, portable, and adaptable over time require long-term thinking around openness and interoperability from the beginning.
Infrastructure readiness will define the next phase of AIThe next phase of AI growth will reward organizations that take a proactive approach to infrastructure planning.
Organizations that delay infrastructure planning may find it more challenging to deploy AI tools down the road, not only due to not having ample time to plan and test, but not securing the compute resources needed early on.
The cost of waiting is becoming ever clearer.
Ultimately, the companies that succeed in the next phase of AI will not necessarily be those with the largest clusters, but those that plan early and build balanced, scalable, and open infrastructure designed to support continuous innovation in an increasingly AI-driven economy.
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