https://i.imgur.com/dNY9tNv.png

I'm not sure I would call Ventoy a Rufus alternative.

Qualcomm takes on NVIDIA with new Dragonfly CPU and AI chips by Pradeep Viswanathan Microsoft, Google, Amazon, AMD, Meta, Apple, OpenAI, and several others have been developing their own chips for AI infrastructure. However, NVIDIA still remains the dominant player in the market. Today, Qualcomm announced a major expansion of its data center infrastructure portfolio to better compete with NVIDIA. The new lineup includes the Qualcomm Dragonfly C1000 CPU, Qualcomm High Bandwidth Compute technology, the Dragonfly AI300 inference accelerator, new connectivity products, and custom silicon solutions. Qualcomm claims that this new lineup improves performance per watt, token throughput, and total cost of ownership for AI data centers. The Dragonfly C1000 is a new data center CPU built with Qualcomm’s custom Oryon cores. This chip will feature more than 250 cores, frequencies above 5GHz, and a chiplet-based design. Qualcomm claims that this new C1000 can deliver more than 2x better performance per watt compared to existing server CPU offerings based on specifications. The Dragonfly C1000 will support PCIe Gen 7 with more than 2TB/s of connectivity, along with CXL, advanced RAS features, and both air and liquid cooling. Qualcomm expects the Dragonfly C1000 to be commercially available in 2028. Additionally, Qualcomm and Meta announced a multi-year, multi-generation agreement under which Qualcomm will supply Dragonfly C1000 data center CPUs for Meta’s next-generation server fleet. Qualcomm also announced High Bandwidth Compute, a new near-memory computing architecture designed to address AI’s memory bandwidth bottleneck. HBC Gen 1 will debut with the Dragonfly AI250, which is expected to sample in mid-2027. The AI250 will deliver 133TB/s per card, an 18x increase in effective memory bandwidth compared to the AI200 with LPDDR5X. The new Dragonfly AI300 with HBC Gen 2 is a rack-level AI inference platform from Qualcomm. Qualcomm claims that the AI300 can deliver 4x to 8x better performance per watt compared to existing GPU-based architectures based on memory bandwidth per watt per card. The Dragonfly AI300 is expected to be available in 2028.

IBM reveals sub-1nm chip technology, production expected in another 5 years by Pradeep Viswanathan TSMC is now leading the chip manufacturing industry with its 2nm-class process node called N2. Samsung Foundry also has a 2nm-class process node called SF2. TSMC says N2 entered volume production in Q4 2025. Samsung says SF2 started mass production in 2025. Today, IBM announced the world’s first sub-1-nanometer chip technology, marking another major semiconductor research milestone. The new technology is based on a 0.7nm, or 7-angstrom, node and uses a new transistor architecture called “nanostack.” The new design vertically stacks and staggers nanosheet-based transistors so that more components can fit into the same chip area while also improving performance and power efficiency. IBM claims that this new sub-1nm chip can pack nearly 100 billion transistors onto a chip the size of a fingernail. This offers almost twice the density, up to 50 percent higher performance, or 70 percent better energy efficiency when compared to IBM's 2nm node design announced back in 2021. Also, IBM mentioned that this new architecture can deliver 40 percent SRAM scaling. It is important to consider that this announcement from IBM is a research milestone rather than a near-term process node launch. Back in 2021, IBM unveiled the world’s first 2nm chip design, claiming 50 billion transistors on a fingernail-sized chip and major performance and efficiency gains. Five years later, IBM’s 2nm technology has still not entered mainstream commercial production. That is because IBM is no longer a major commercial chip manufacturer. It sold its chip manufacturing business to GlobalFoundries years ago and has since then focused only on semiconductor research, IP development, and partnerships. To productize its 2-nm chip technology, IBM partnered with Japan’s Rapidus, but it has not resulted in anything shipping at scale. IBM says that its new sub-1nm technology can reach production as early as within the next five years. If that happens, it will likely depend on manufacturing partners, advanced EUV tooling, and years of yield improvements.