NVIDIA GPU prices are surging across multiple generations. By October 11, the lowest used RTX 5090 asking price had reached $5,879.80, up 60% since August, while used RTX 40-series prices rose by a median of around 30%. Limited supply, rising memory costs and AI demand may all be contributing factors. If you're planning to sell graphics card, it's worth checking current market prices—but remember that asking prices don't necessarily reflect actual sale values.

Not many people realize that external GPUs are evolving beyond traditional gaming accessories.

WICI One has unveiled a $1,999 wireless Wi-Fi 7 GPU enclosure with a built-in 4TB SSD, designed to let users access a remote GPU as if it were locally connected. The concept is particularly interesting for local AI, where GPU compute and fast storage are increasingly important.

Wireless connectivity still has limitations compared with direct PCIe connections, but the product points to a future where powerful GPU resources don't necessarily have to sit inside the same PC.

As demand for GPU compute continues to grow, unused graphics cards may also have more value in the secondary market. If you have surplus hardware, you can sell GPU instead of leaving it unused.

Read more: New wireless Wi-Fi 7 external GPU enclosure comes with a built-in 4TB SSD for local AI

In Q1 2026, Samsung Electronics finalized DRAM contracts with price increases exceeding 100%—a dramatic escalation from the 70% increase projected just weeks earlier. Even Apple reportedly accepted the higher prices to secure LPDDR5X supply for upcoming devices.

The underlying driver is clear: AI infrastructure is reshaping the memory market.

Hyperscalers and AI infrastructure companies are consuming enormous amounts of wafer capacity for high-bandwidth memory (HBM), putting additional pressure on conventional DRAM and NAND supply. Analysts at Gartner and IDC have projected that AI data centers could account for a significant share of high-end DRAM demand in 2026.

The impact is spreading across the broader hardware market:

• DRAM and NAND contract prices have risen sharply.
• DDR4 spot prices have increased faster than DDR5 in some periods as manufacturers reallocate production capacity.
• Memory is becoming a much larger component of PC and server costs.
• The secondary memory market is shifting from simple depreciation toward a more liquid market for existing inventory.

The 2026 “Rampocalypse” is more than a typical memory-price cycle. AI-driven demand, HBM production, and manufacturing capacity constraints are changing the economics of memory across PCs, servers, and data centers.

For businesses holding unused or surplus memory inventory, higher prices also create an opportunity to sell RAM while demand remains elevated.

Read the full analysis: Samsung’s 100% DRAM Price Hike and Why Even Apple Had to Pay Up

Meta plans to start production of its in-house AI chip, called Iris, in September as part of a major expansion of its AI infrastructure. The company aims to double its computing capacity to 14 gigawatts by 2027, reduce reliance on external chip suppliers, and lower AI operating costs.

The cheaper AI tokens get, the more expensive memory becomes. Inference costs fell 280-fold in two years; enterprise AI spending tripled anyway, and DRAM contract prices just posted a record 90-95% quarterly jump. Jevons saw this pattern in 1865 with coal. A look at what actually rebalances the chip market — and why the answer is fabs in late 2027, not smarter models or ASICs.

Spending $2,700 on 8TB storage for a $700 game console?

SanDisk announced the officially licensed Optimus GX PRO 850P M.2 NVMe SSD for PS5 and PS5 Pro, offering up to 8TB of PCIe 4.0 storage with a custom heatsink and high performance, but its extremely high pricing has quickly become the main talking point...

#SanDisk #SSD #PlayStation #Gaming

Amazon is no longer just building AI chips for internal AWS use. It is preparing to sell its Trainium AI chips directly to external customers, turning itself from a cloud provider into a direct AI hardware competitor to NVIDIA. The bigger story is that hyperscalers (Amazon, Google, Microsoft, Meta) increasingly want to control their own AI infrastructure instead of depending entirely on NVIDIA.

AMD bought MEXT, a startup whose software makes cheap NAND flash act like DRAM, so a server runs on far less of the expensive stuff. With DRAM contract prices up ~90%+ this year, the logic isn't subtle. How predictive memory tiering actually works?

AMD Ryzen AI Max+ 395 vs Nvidia DGX Spark vs Apple Mac — plus when a GPU tower still beats all three. A practical hardware guide for IT managers, developers, and small-business owners weighing a local LLM machine.

The short version: there’s no single winner. The right pick comes down to three numbers — how big a model you need to run (memory capacity), how fast it has to run (memory bandwidth), and which software you depend on (CUDA, ROCm, or Metal). A discrete-GPU tower is fastest but hits a VRAM wall; AMD’s Strix Halo mini PCs give the most memory per dollar on Windows; Nvidia’s DGX Spark adds the CUDA stack at a premium; Apple’s Macs offer high bandwidth and silence without CUDA. An appendix at the end collects what early buyers of the AMD “lunchbox” are actually reporting.

How to Choose the Best Mini PC for Local AI

Interesting...The AI race is moving so fast that they can not wait for a permanent building

Arm challenged it on two fronts at once. At Computex 2026, NVIDIA's RTX Spark put a 20-core Arm CPU + Blackwell GPU inside mainstream Windows laptops, while Arm's first data-center chip — the AGI CPU — hit mass production with Meta, OpenAI and Oracle aboard. So what happens to x86 servers and parts out there now? Here is the full article: Arm vs x86