Apple M6 chip and M5 Ultra launch

Apple's M6 is the first Apple silicon chip built on TSMC's 2-nanometer (N2) process, debuting in a redesigned Mac mini with a 12-core CPU, a 12-core GPU carrying a Neural Accelerator in every core, and a new Dual 16-core Neural Engine ^[1]. Memory bandwidth reaches up to 170GB/s - a modest 10% bump over M5, though 2.5x the original M1 ^[1]. The bigger structural story is M5 Ultra, Apple's first quad-die design: two dual-die M5 Max chips fused through Apple's UltraFusion interconnect, which now carries over 4.4TB/s of bandwidth between the four dies so they behave as one processor ^[2]. That architecture scales to a 36-core CPU, an 80-core GPU, and up to 512GB of unified memory at 1.2TB/s of bandwidth - 50% more than M3 Ultra ^[3]. Notably, Apple stuck with TSMC's base N2 node rather than the newer, pricier N2P node, reportedly to lock in supply and control manufacturing costs - trading away roughly a 5% node-level performance and power edge for a more secure, cost-controlled 2nm supply chain ^[4].

Apple frames both chips explicitly around running large language models entirely on-device - private, cloud-free 'agentic' workloads with no per-token cloud costs ^[5]. TechCrunch reads this partly as competitive necessity: Apple has trailed rivals on proprietary generative AI models - it still leans on Google's Gemini to power Siri - and is instead betting on on-device privacy and raw local compute as its differentiator ^[6]. Independent Apple analyst Federico Viticci puts numbers on the payoff: he projects a mid-size open model like Qwen 3.5-35B could exceed 60 tokens per second on a base M6 Mac mini, more than triple his roughly 17 tokens-per-second estimate for the outgoing M4, while DeepSeek-V4-Flash could exceed 120 tokens per second on M5 Ultra ^[7]. He also estimates that clustering four M5 Ultra Mac Studios together could deliver roughly 3x single-machine performance, enough to run 744-billion-plus parameter frontier models locally - though he flags that the base M6 Mac mini lacks Thunderbolt 5, so unlike M5 Pro machines it can't join that kind of cluster at all ^[7].

While Apple's messaging centered on the AI-compute leap, reaction on Reddit skewed hard toward price shock: the Mac mini's jump to an $899 starting price, up from roughly $499-599 in the M4 era, drew heavy pushback, with many commenters tying it to a broader 'AI/RAM bubble' - DRAM and storage costs rising industry-wide as hyperscalers buy up memory for AI datacenters, with some threads alleging price-fixing among memory makers. X.com sentiment ran the opposite direction: commentary there framed the base M6 Mac mini as a local-inference value play, emphasizing what a 32GB configuration could run rather than what it cost, while treating M5 Ultra and its $5,499 Mac Studio starting price as an enthusiast or pro-tier flex piece rather than something built for typical consumers. The more technical r/LocalLLM discussion converged less on price outrage and more on capability limits, with many arguing the base M6's memory bandwidth is too slow for serious inference beyond modestly sized models - pushing the community's practical preference toward a mid-tier M5 Ultra configuration instead of the entry-level mini.

Multiple pre-announcement previews - citing Bloomberg's Mark Gurman - reported that Apple would skip standalone M6 Pro, Max, and Ultra variants altogether and instead compress the M6 generation to accelerate M7, a chip that reporting tied to bigger on-device AI gains and, longer term, to powering Apple Intelligence's own servers starting around 2029. That is reporting, not an Apple statement, but what actually shipped lines up with the account: M6 landed only in the Mac mini, while the Mac Studio refresh paired M5 Ultra and M5 Max - not a new M6-tier Ultra chip ^[5].