GPU The parallel processor that runs most AI models, and what to look at besides speed.
Short answer. A GPU (graphics processing unit) is a processor built to run thousands of simple calculations at the same time. It began as hardware for 3D graphics and now runs most AI models, because a language model is mostly large matrix multiplications that split neatly into parallel work. NVIDIA’s CUDA guide says a CPU is designed to run a few tens of threads as fast as possible, while a GPU is designed to run thousands in parallel, trading single-thread speed for total throughput. For AI, a GPU’s memory size and memory bandwidth matter as much as its calculation speed.
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CPU, GPU and NPU: three kinds of processor
Processor
Built for
What it does for AI
CPU
A few fast threads and general-purpose tasks
Runs small models and the software around them; Intel describes it as the engine for quick response
GPU
Thousands of parallel threads
Runs large models and image generation; Intel describes it as suited to large tasks that need high parallel throughput
NPU
Low-power AI arithmetic
Handles sustained AI tasks at lower power; AMD rates the NPU in the Ryzen AI Max+ 395 at up to 50 TOPS
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Integrated and discrete GPUs
An integrated GPU sits on the same chip as the processor and shares the computer’s memory. AMD’s Ryzen AI Max+ 395 is an example: its Radeon 8060S graphics has 40 graphics cores and draws on the same LPDDR5x memory as the 16 CPU cores.
A discrete GPU is a separate card with its own memory, connected over PCIe. NVIDIA’s RTX PRO 4000 Blackwell is one: a single-slot card with 24 GB of GDDR7 and a maximum power of 145 W.
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What a GPU does inside an AI answer
NVIDIA splits a language-model answer into two phases. In the prefill phase the model reads the whole prompt at once, which keeps the GPU busy. In the decode phase it writes the answer one token at a time, and the speed at which weights move from memory to the processor dominates the delay, so this phase is memory-bound.
That is why, for an office assistant that mostly writes answers, a GPU with large, fast memory can matter more than one with a higher calculation rating. Long documents stress the prefill phase; long answers stress the decode phase.
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Choosing a GPU for an office
Memory capacity first: it decides which models fit (see VRAM).
Memory bandwidth second: it sets how fast answers appear.
Compute (TFLOPS) third: it matters for long prompts, images and batches of work.
Software support: check that the vendor’s stack, CUDA for NVIDIA or ROCm for AMD, supports the tools you intend to run.
Power, heat and noise: NVIDIA’s RTX PRO range runs from 70 W cards to a 600 W card, which are very different neighbours in an office.
Sharing: ask how the machine behaves when ten people use it at once, not only one.
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Where ANVE stands
ANVE’s launch units use a GPU in two ways: AMD Strix Halo class integrated graphics with unified memory in ANVE One, and an NVIDIA RTX PRO 4000 Blackwell graphics card in ANVE Office (the SFF Edition) and ANVE Office Pro. The ANVE software already uses the box’s own GPU for image generation through ComfyUI. No sealed unit exists yet, and ANVE has published no speed figures.
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Questions people ask
Can AI run without a GPU?
Yes, smaller models do. In the faster-whisper project’s published test, the Whisper small model transcribed 13 minutes of audio in 1 minute 42 seconds on an Intel Core i7-12700K processor, with no GPU. Large language models need far more memory and bandwidth, which is where GPUs and shared-memory chips help.
What is the difference between a GPU and an NPU?
A GPU is a general parallel processor. An NPU is a smaller block tuned for low-power AI arithmetic. Microsoft’s Copilot+ PC label asks for an NPU of at least 40 TOPS, and AMD rates the NPU in the Ryzen AI Max+ 395 at up to 50 TOPS.
Does a faster GPU always give faster answers?
No. NVIDIA describes the word-by-word decode phase as memory-bound, so memory bandwidth often limits answer speed more than raw calculation speed does.
Can an office use a gaming GPU for AI?
It can work for one technical user. The trade-offs are memory size, ECC and driver support, which our question page covers.
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What is not real yet
No sealed ANVE unit exists; every image of the appliance on this site is a labelled render. The software runs today on a development machine. No customers yet, no certifications, no delivery date, and the final price is not announced.
Reservations are ₹999 in India or US$12 elsewhere, refundable in full until you confirm an order in writing (terms). Offices that cannot use cloud AI can apply to be one of ten design partners.
