Apple is reshaping the competitive landscape of AI hardware through an on-device inference strategy, directly challenging the potential incremental market share NVIDIA could capture in the AI inference space.

Apple's newly unveiled M5 Ultra-powered Mac Studio, which began shipping on September 22, supports up to 512GB of unified memory and enables multi-machine cluster setups for distributed inference workloads. Apple has previously demonstrated four Mac Studio units working together to run a trillion-parameter model.

Apple's hardware chief Johny Srouji told Reuters that once enterprises purchase the hardware outright, they no longer need to pay per-token cloud fees for each inference request. For teams with stable workloads, the economics of on-premise compute could prove more favorable than long-term cloud GPU rentals.

The significance of this narrative for NVIDIA lies not in directly stealing existing orders, but in this: if more inference tasks migrate to edge devices or enterprise-owned machines, NVIDIA's capture of incremental AI inference activity could fall short of current market expectations.

Currently, Apple's stock shows strong technical momentum, while NVIDIA remains range-bound in a wide trading band without a clear medium-term trend. Some analysts suggest using options to express a bullish view on Apple and a bearish view on NVIDIA.

Product positioning shift: from "using AI" to "running AI"

Apple has fundamentally changed how it positions the Mac Studio, moving from selling machines that "can use AI" to selling machines that "can run AI."

The core of this shift lies in the structural difference in inference costs: iPhone and Mac users have already paid for their chips, so tasks processed locally no longer require additional cloud inference requests, and this same logic is now extending to the enterprise side.

The M5 Ultra Mac Studio supports up to 512GB of unified memory, with this configuration arriving in late October. The large memory capacity allows users to load models that a single conventional GPU cannot accommodate, while clustering multiple machines can distribute the weights of a trillion-parameter model.

Apple has previously demonstrated four Mac Studio systems running a trillion-parameter model, making local inference technically viable as a substitute for portions of cloud GPU clusters.

However, the real test beyond Apple's demonstration lies in inference speed, concurrent user capacity, and total cost of ownership relative to cloud rental—these three factors are the key variables in enterprise procurement decisions.

NVIDIA remains in the supply chain, but incremental share is in question

The Mac Studio will not replace data center GPUs across all workloads—Apple itself uses NVIDIA GPUs on Google Cloud for certain demanding AI tasks.

But NVIDIA does not need to lose existing orders for the relative narrative to shift: once more inference runs on device-side or enterprise-owned machines, NVIDIA's share of incremental AI activity could fall below market assumptions.

NVIDIA's stock still lacks a medium-term trend, trapped in a wide range and trading near the top of that range, while implied volatility sits at its lowest level since before the pandemic.

Low volatility typically reflects high market confidence about future direction, but the potential shift in the inference narrative is precisely a wildcard—current options pricing allocates almost no risk premium for "inference moving on-premise."

For this reason, Goldman Sachs believes that buying NVIDIA put options before earnings expectations are revised downward is a low-cost way to express this transformation. From a relative perspective, it prefers Apple calls over NVIDIA puts.

Analysts suggest watching for actual inference speed and total cost of ownership data after the 512GB configuration arrives in late October, as well as enterprise customer feedback on procurement of on-premise inference solutions.