On June 25, IBM announced the world's first sub-1 nanometer (nm) chip technology, reaching the 0.7nm node (7 angstroms). The breakthrough was published at the VLSI 2026 conference and confirmed by IBM's official newsroom.
The Nanostack Architecture
The key innovation is called "nanostack" — a 3D nanosheet-based design that vertically stacks transistors, a first for the industry. This goes beyond the nanosheet transistors IBM itself pioneered at 2nm. By stacking and staggering transistors using 3D sequential integration, IBM packs roughly 100 billion transistors onto a chip the size of a fingernail — nearly double the density of its 2nm chip from 2021.
According to IBM's published results, the new architecture delivers:
• Up to 50% more performance than the 2nm node
• Or up to 70% greater energy efficiency at equivalent performance
• 40% scaling improvement in SRAM, critical for high-bandwidth AI workloads
Why This Matters for AI
The timing of this announcement is no coincidence. AI inference at scale is currently memory-bandwidth limited, not just compute limited. The 0.7nm node's SRAM improvements directly translate to more on-chip memory for model weights, reducing the need to shuttle data between the chip and external DRAM.
For builders running LLM inference, this means:
• Higher throughput per watt — more tokens per joule
• Larger on-chip caches — better support for models with large KV caches
• Denser compute — trillion-parameter models become feasible on single servers
IBM says it sees a path to production "in as early as the next 5 years." That's consistent with semiconductor timelines — they're showing working CMOS inverters now, which is the equivalent of running "Hello World" on a new process.
Commercialization Path
IBM typically licenses its chip technology rather than manufacturing its own consumer chips. The company has already partnered with Lam Research, Tokyo Electron (TEL), and SCREEN Semiconductor Solutions to develop High NA EUV lithography processes at its Albany, NY research facility.
The Albany facility will be critical: it's set to receive ASML's High NA EUV lithography tool, which prints the ultra-precise circuits needed for sub-1nm nodes. IBM and its partners have already demonstrated working devices with these tools.
Context: The Chip Landscape
This announcement comes amid a chip technology inflection. While TSMC and Samsung are ramping 2nm and 3nm production, IBM's 0.7nm demonstration shows that Moore's Law still has life — at least at the architectural level.
The race is now multi-dimensional:
• IBM/Samsung alliance → Nanostack architecture, licensed model
• TSMC → N2 (2nm) in production by late 2026
• Intel → 18A (1.8nm) with PowerVia backside power delivery
• Apple → Silicon in-house, reportedly skipping M6 Pro/Max for M7 with AI focus
Practical Takeaways
If you're planning AI infrastructure for 2028-2030, here's what to watch:
1. Don't expect sub-1nm in your laptop by 2027. The timeline is ~5 years to production, meaning late 2020s at earliest. 2. Watch the IBM licensing partners. Samsung and potentially others will be the ones manufacturing real chips on this process. 3. The big win is inference efficiency. Models don't get smaller, but the hardware that runs them gets dramatically more power-efficient. 4. SRAM scaling is the sleeper hit. More on-chip memory means less latency for real-time AI applications.
Sources
• IBM Newsroom: IBM Debuts World's First Sub-1 Nanometer Chip Technology
• Hacker News discussion (486 points)
• [VLSI 2026 Symposium — Reboh et al. "NanoStack Transistor Architecture for CMOS 7A Node and Beyond"]()
• [Zhang et al. "Area and Performance of Staggered-Channel Nanostack SRAM Bitcells" VLSI 2026]()