Nvidia thinks its GPUs are better for onboard AI than NPUs

Daniel Sims

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The big picture: Competing standards could soon emerge as companies like Microsoft, Intel, Qualcomm, and Apple prepare to promote PCs and other devices that prioritize on-device AI operations. Microsoft and Intel recently outlined what they think should be classified as an "AI PC," but the AI sector's current leader, Nvidia, has different ideas.

A recently leaked internal presentation from Nvidia explains the company's apparent preference for discrete GPUs over NPUs (Neural Processing Units) for running local generative AI applications. The graphics card giant could view NPUs from other companies as a threat since its earnings have skyrocketed since its processors became integral for operating large language models.

Since launching its Meteor Lake CPUs late last year, Intel has tried to push laptops featuring the processors and their embedded NPUs as a new class of "AI PC" designed to perform generative AI operations without relying on massive data centers in the cloud. Microsoft and Qualcomm plan to shepherd more AI PCs into the market later this year, and Apple expects to jump onto the bandwagon in 2024 with its upcoming M4 and A18 bionic processors.

Microsoft is trying to promote its services as integral to the new trend by listing its Copilot virtual assistant and a new Copilot key as requirements for all AI PCs. However, Nvidia thinks its RTX graphics cards, which have been on the market since 2018, are much better suited for AI tasks, implying that NPUs are unnecessary and that millions of "AI PCs" are already in circulation.

Microsoft claims that AI performance reaching 40 trillion operations per second (TOPS) will be necessary for next-generation AI PCs, but Nvidia's presentation claims that RTX GPUs can already reach 100-1,300 TOPS. The GPU manufacturer said that chips like the currently available RTX 30 and 40 series graphics cards are excellent tools for content creation, productivity, chatbots, and other applications involving numerous large language models. For such tasks, the mobile GeForce RTX 4050 can supposedly outperform Apple's M3 processor, and the desktop RTX 4070 achieves "flagship performance" in Stable Diffusion 1.5.

To showcase the unique capabilities of its technology, Nvidia has rolled out a major update for ChatRTX. This chatbot, powered by Nvidia's TensorRT-LLM, operates locally on any PC equipped with an RTX 30- or 40-series GPU and a minimum of 8 GB VRAM. What sets ChatRTX apart is its ability to answer queries in multiple languages by scanning through documents or YouTube playlists provided by users. It is compatible with text, pdf, doc, docx, and XML formats.

Of course, just because Nvidia says it's the surefire leader for onboard AI performance doesn't mean competitors will throw in the towel and say, "You win." On the contrary, competition and R&D in the NPU market will only grow more fierce as companies try to unseat Nvidia.

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This is probably true, BUT at the expense of power consumption. In other words, it’s not efficient as compared to a NPU, and generally not suitable for thin and light devices like a tablet or slim laptop.
It seems fairly obvious that it would be "better" but how much power do we actually need for AI? It would cost a fortune and, like you said, eat up tons of power.
 
Yes and no... an APU with decent GPU capabilities have way more to offer than a cheap 4060.
No APU in existence comes close to the 4060's processing power, and will inevitably be hamstrung by slow system RAM.
This is probably true, BUT at the expense of power consumption. In other words, it’s not efficient as compared to a NPU, and generally not suitable for thin and light devices like a tablet or slim laptop.
We'd probably need some benchmarks to judge that. Nvidia's ADA arch is quite efficient when not clocked balls to the wall, and anything with CUDA enhancement would bury intel/AMD options.
 
This is probably true, BUT at the expense of power consumption. In other words, it’s not efficient as compared to a NPU, and generally not suitable for thin and light devices like a tablet or slim laptop.
This is exactly the point of NPU's, they are far more efficient and of course Ngreedia don't have one so are going to spruik their expensive gpu's and talk down NPU's right up until they have no choice but to offer one. Not sure how a powerful dGPU helps laptops chasing efficiency for better battery life. Whilst current APUs have weak NPU's this changes later this year and then the NPU will actually be worthwhile and competitive with the iGPU for performance at much lower power usage. Already Tom's Hardware tested this and showed AI on CPU using 60% cpu but on NPU only 2% utilisation.
 
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In addition to the power consumption differences mentioned already, many laptops simply don’t have a dGPU.

And AI is unlikely to upend the desire for thin & light or really cheap laptops.
 
No APU in existence comes close to the 4060's processing power, and will inevitably be hamstrung by slow system RAM.
We'd probably need some benchmarks to judge that. Nvidia's ADA arch is quite efficient when not clocked balls to the wall, and anything with CUDA enhancement would bury intel/AMD options.

Ignoring AMDs strix halo or whatever is coming

But a good APU does offer more.
I think you took more as a specific term to maybe AI/graphics

But the APU gives you a CPU, not sure linux or windows runs on a GPU , could be wrong - Doom seems to run on anything

Many consumers just need a car not a truck. Let's see NVidias solution for iPhone and Android phones , maybe they have a ARM cuda chip coming
So why go into servitude to Nvidia. All the big guys are trying to break free from the patented cuda cores. In having an agnostic software that can run on their own silicon ( think google, MS. Meta etc frantically designing their own AI boards ) or AMD and any other new entrants

Nvidia is just trying to control the next 5 years, but they have sold all their supply for this year already and probably next as well ( AI dedicated stuff ) , so they can jump in the lake
 
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