NVIDIA Also Makes CPUs Now - Here’s What They’re Used For (And Why It Matters)
For most people, NVIDIA = GPUs.
That’s fair — NVIDIA basically defined modern AI acceleration. But in the last few years, NVIDIA quietly expanded into a category most people associate with Intel and AMD:
NVIDIA also produces CPUs.
Not “Ryzen/Core” consumer CPUs — but data-center CPUs designed for one job:
keep GPUs fed, connected, and efficient at massive scale.
That CPU line is called NVIDIA Grace.
1) “NVIDIA makes CPUs” — what that actually means
NVIDIA’s CPUs are:
Arm-based
built for servers / data centers
optimized for AI + HPC (high-performance computing)
They are not meant for:
laptops
desktops
gaming PCs
everyday office machines
This is important because it explains the strategy: NVIDIA isn’t trying to “beat Intel on your MacBook.”
They’re trying to build the fastest AI computers on Earth, end-to-end.
2) The real reason Grace exists: GPUs became the product, the system is the battlefield
Modern AI systems don’t fail because the GPU can’t do math fast enough.
They fail because:
data can’t reach the GPU fast enough
memory bandwidth becomes a bottleneck
the CPU can’t orchestrate workloads efficiently across many GPUs
the system burns too much power moving data around
So NVIDIA’s logic is simple:
If the GPU is the engine, then the CPU + memory + networking is the fuel system.
Grace exists to make the whole system behave like one optimized machine instead of a pile of parts.
3) What NVIDIA’s CPUs are used for
Use case A: Feeding GPUs for AI training and inference
In large-scale AI, the CPU often becomes the bottleneck because it handles:
loading huge datasets
decoding / preprocessing
scheduling work across many GPUs
coordinating CPU ↔ GPU ↔ memory movement
If the CPU can’t keep up, GPUs sit idle — and idle GPUs are expensive.
Grace is built to keep GPU utilization high, which matters when you’re running:
LLM training
multimodal AI training (text + image + video)
inference at scale (serving millions of users)
The goal isn’t “fast CPU benchmarks.”
The goal is: more tokens per watt, more work per rack, less downtime.
Use case B: HPC and supercomputing (science + simulation)
Grace CPUs also show up in workloads where the system is extremely memory- and bandwidth-heavy, such as:
climate and weather simulation
physics and chemistry modeling
genomics
digital twins (factories, cities, infrastructure)
large-scale numerical computing
In these environments:
the CPU often handles control logic and memory-heavy orchestration
GPUs do the acceleration (the heavy math)
Grace is optimized for exactly that partnership.
Use case C: “AI factories” in data centers
NVIDIA increasingly frames AI infrastructure like manufacturing:
You don’t buy single machines — you build an “AI factory” where every part is designed to work together:
compute (CPU + GPU)
memory
networking
software stack
Grace CPUs are part of NVIDIA’s push to turn data centers into rack-scale AI computers rather than traditional server rooms.
4) The most important product isn’t the CPU alone — it’s the CPU+GPU superchip
A big part of the Grace story is not “NVIDIA made a CPU.”
It’s that NVIDIA made CPU + GPU packages that are tightly integrated.
You’ll often see this described as a “superchip” concept:
Grace CPU + NVIDIA GPU in a single integrated design
ultra-fast interconnect between them
built to reduce bottlenecks and latency
In practice, this matters most for:
giant models that need huge memory bandwidth
workloads that bounce between CPU and GPU constantly
clusters that need predictable performance at scale
It’s less like “a server with a GPU card” and more like:
one purpose-built AI computer.
5) Why NVIDIA chose to make CPUs at all
This is the strategic part.
In the AI era, the GPU is not a component. It’s the center of the universe.
And once the GPU is the center, everything else must be built around it:
CPU must feed it
memory must support it
networking must connect thousands of them
software must orchestrate it
If NVIDIA depends on third-party CPUs, they inherit:
mismatched roadmaps
performance ceilings
integration limits
suboptimal power efficiency
So NVIDIA makes CPUs to control:
performance per watt
CPU↔GPU data movement
rack-level scaling
system design from chip to cluster
This is the same “platform” play Apple used with Apple Silicon — but for AI data centers.
6) What this means for builders, founders, and agencies
Even if you’re “just building software,” this matters because it changes what modern infrastructure looks like.
If you build AI products:
Expect more customers to run workloads on:
NVIDIA GPU instances
tightly integrated CPU+GPU systems
“AI-first” server architecture
That influences:
deployment strategy
latency expectations
cost structure (especially inference)
optimization priorities
If you build enterprise platforms:
AI workloads increasingly behave like:
continuous pipelines
real-time inference
memory-heavy retrieval + embeddings
massive parallelism
The infrastructure underneath is shifting away from “generic compute” toward specialized compute stacks.
7) The simple takeaway
Yes — NVIDIA produces CPUs.
But they aren’t trying to replace Intel and AMD in consumer PCs.
They’re building CPUs for one reason:
to make GPUs perform better and scale further in AI and HPC systems.
In the AI era, the product isn’t the chip.
The product is the system.
And NVIDIA is building the whole system.