As Huawei and other tech companies build their own AI processors, the hardware market is gaining more options. But the road from a chip to a monthly AI bill is a long one.

Nvidia is no longer the only choice

For years, Nvidia has been one of the most important names in AI infrastructure. Its GPUs are widely used to train and run AI models, from chatbots to systems that generate images, voice and video. Now, however, other options are emerging.

According to Reuters on 13 October 2026, citing IDC data, Nvidia's share of AI accelerators shipped into China's data-center market fell to 34% in the first half of 2026. Over the same period Huawei reached about 28%, equivalent to 572,000 AI accelerators shipped.

This is a share by chip volume in one specific market, not a global AI-chip share or industry revenue. One more caveat: Nvidia's 34% is measured on data-center AI chips, while Huawei's 28% is measured on the overall AI-accelerator market — two different baskets, so the figures should not be read as a precise head-to-head.

The shift shows businesses have more hardware choices. But a new option does not mean an entire AI system can switch over instantly.

A powerful chip alone cannot run AI

Imagine a company that wants to offer an AI app that can chat in Vietnamese, read documents, generate images and help write content. It cannot simply buy chips, plug them into servers and start charging users. For AI to run reliably, several layers of technology must work together.

The journey from chip to AI app runs through four layers:

  • AI chip — runs the model's computations.
  • Software and models — frameworks, drivers, optimized libraries, compatibility.
  • Operating infrastructure — servers, memory, networking, power, cooling, monitoring.
  • The paid AI service — apps, APIs, subscriptions and customer support.

A cheaper chip can cut equipment costs. But if the software is not optimized, runs slower or needs more servers to serve the same users, the cost benefit can shrink significantly. That is why vendors compete not only on chips but on software ecosystems.

On 19 October, Reuters reported that Amazon introduced TorchNeuron, a tool that lets developers use PyTorch with Trainium chips without rewriting much code. It shows that lowering software-switching costs is also a key part of the AI-infrastructure race.

The real cost is in every AI answer

When you ask a chatbot a question, the system must run the model to produce an answer. This process is called inference. Each request consumes computing resources, and for systems serving millions of people, these small costs add up to a significant operating bill.

A key metric is the cost to generate a million tokens, rather than just the price of the chip. A token is a unit of text the model processes; a long answer or large document needs more tokens. Nvidia also stresses that token throughput, power and optimized software determine running costs — this is the manufacturer's own argument, so specific performance claims should be checked against independent testing.

What matters is not which chip is cheapest, but which system handles the most useful work at a reasonable cost, low latency and stable quality.

Why do subscriptions stay flat even when chips get cheaper?

Suppose an AI provider cuts compute cost by 30% thanks to new hardware (a hypothetical example, not a confirmed saving from Nvidia or Huawei). If compute is 40% of total service cost, the direct saving is only about 12% of the total, all else equal.

On top of that, the savings may be reinvested into better models, more capacity, faster responses or new features. The price users pay also depends on business strategy, competition and what the market will bear. So a lower production cost does not mean the subscription price drops at the same rate.

How will Vietnamese users benefit?

For users in Vietnam, AI-chip competition can bring notable changes, even if not immediately on the monthly bill. With more hardware platforms and infrastructure providers, AI developers have more deployment options — especially meaningful for apps serving Vietnamese, customer care, document processing or work automation.

Better operating efficiency can let apps offer more usage within the same plan. An AI video tool, for example, might keep the same price but raise the number of generations, cut waiting time or improve quality. Users may not see a lower price, but can still get more value. These, however, depend on competition and each provider's decisions, and are not guaranteed outcomes.

The AI race is not only about the chip

Options like Huawei Ascend, Amazon Trainium and other AI accelerators show the industry is diversifying its infrastructure. But success is not decided by chip volume alone: software compatibility, developer tooling, per-model performance, supply and real operating costs all shape what businesses choose.

For everyday users the final question is simpler: does the AI answer faster, is it smarter, does it have fewer limits, and is the monthly price worth it? More rivals in AI chips is a notable signal. But for AI to truly become cheaper for users, competition must travel the whole path from hardware and software to infrastructure and service pricing. That is the race users can actually feel.

Frequently asked questions

  • Is Nvidia still the leader in AI chips in China? — Yes. Per IDC (Reuters, 13 Oct 2026), Nvidia still held 34% of China's data-center AI-chip market in H1 2026, ahead of Huawei, though the gap is narrowing.
  • Can the 34% and 28% be compared directly? — Not quite. Nvidia's 34% is measured on data-center AI chips, while Huawei's 28% is on the overall AI-accelerator market — different baskets.
  • Why isn't AI cheaper even with more AI chips? — The chip is only one layer. Cost also sits in software, operating infrastructure and the cost of each inference. Cheaper chips do not automatically lower subscription prices.
  • What do Vietnamese users gain? — They may not see lower prices yet, but services may offer more usage, shorter waits or higher quality within the same plan.