Table of Contents
a. Nvidia 101
Nvidia designs semiconductors for data center, gaming and other use cases. It’s considered the technology leader in chips meant for accelerated compute and generative AI (GenAI) use cases. While that’s where it specializes, it does a lot more. Its toolkit includes chips, servers, switches, networking, AI models and cutting-edge software to optimize the hardware it provides. Owning more pieces of GenAI infrastructure means opportunity for more software-based product optimization.
The following items are important acronyms and definitions to know for this company:
Chips:
GPU: Graphics Processing Unit. This is an electronic circuit used to process visual information and data.
CPU: Central Processing Unit. This is a different type of electronic circuit that carries out tasks/assignments and data processing from applications. Teachers will often call this the “computer’s brain.”
Blackwell: Nvidia’s modern GPU architecture designed for accelerated compute and GenAI. It replaces Hopper. Rubin is the next platform after Blackwell. Then Feynmann.
Grace: Nvidia’s new CPU architecture that is designed for accelerated compute and GenAI.
GB300: Its Grace Blackwell Superchip with Nvidia latest “Blackwell Ultra” GPUs and ARM Holdings tech.
Connectivity:
NVLink Switches: Designed to aggregate and connect (or “scale-up”) Nvidia GPUs within one or a couple of server racks. This creates a sort of “mega-GPU.” GPU connections power greater efficiency, performance and computing scale (so cost advantages).
The newest system allows for 576 total GPUs to be connected.
InfiniBand: Standardized interconnectivity tech providing an ultra-low latency computing network. This can connect larger batches of server racks for more scalability (or “scale-out”).
Nvidia Spectrum X: Similar to InfiniBand functionality and performance but Ethernet-based.
Ethernet is vital for connecting larger compute clusters.
All 3 of these products are driving strong growth in this budding segment.
NVLink Fusion allows companies to build “semi-custom” AI infrastructure with Nvidia and its integration ecosystem. GPUs are general-purpose in nature. They’re not granularly designed for every single niche use case like an Application-Specific Integrated Circuit (ASIC). This can help Nvidia capture more of that demand by pairing with Marvell and a few other partners to more easily emulate purpose-built hardware.
The Nvidia GB300 NVLink72 is its rack-scale computing system. Rack scale means the entire server rack powers computation rather than a single server. Because this includes Blackwell chips and NVLink switches, it’s partially in the compute bucket and partially in networking. This aggregated product is the core revenue driver right now.
Software, Models & More:
NeMo: Guided step-functions to build granular GenAI models for client-specific needs. It’s a standardized environment for model creation.
CUDA: Nvidia-designed computing and program-writing platform purpose-built for Nvidia GPU optimizations. CUDA helps power things like Nvidia Inference Microservices (NIM), which guide the deployment of GenAI models (after NeMo helps build them).
NIMs help “run CUDA everywhere” — in both on-premise and hosted cloud environments.
GenAI Model Training: One of two key layers to model development. This seasons a model by feeding it specific data.
GenAI Model Inference: The second key layer to model development. This pushes trained models to create new insights and uncover new, related patterns. It connects data dots that we didn’t realize were related. Training comes first. Inference comes second… third… fourth etc.
Omniverse is its digital twin-building platform. This allows companies to deeply test decisions and reactions in a zero-stakes, simulated environment, turbocharging experimentation and progress.
Cosmos is its suite of world foundation models and apps for physical AI. It’s grounded in laws of physics and everything needed to effectively understand the physical world.
Thor is the name of its platform for robotics and physical AI.
DGX: Nvidia’s full-stack platform combining its chipsets and software services.
b. Key Points
Strong quarter for the data center despite no China revenue.
Rubin is on schedule.
Ampere utilization rates remain at 100% despite being 5.5 years old.
Strong quarter for Professional Visualization.
c. Demand
Beat revenue estimates by 3.3% & beat guidance by 5.6%. This was not materially helped by Hopper GPU sales to China.
Data center revenue beat estimates by 3.9%.
Compute data center revenue beat estimates by 3.4% and rose by 56% Y/Y to $43B.
Networking data center revenue beat estimates by 5.7% and rose by 162% Y/Y to $8.2B.
Gaming revenue missed revenue estimates by 3.6%.
Professional Visualization beat revenue estimates by 24%.
Automotive & Robotics missed revenue estimates by 4.7%.
d. Profits
Slightly missed GPM estimates & slightly beat GPM guidance.
Beat EBIT estimates by 3.7% & beat guidance by 6.3%.
Beat $1.26 EPS estimates by $0.04 & beat guidance by $0.08.
Its tax rate was slightly higher than guidance.
Missed FCF estimates by 22%. This is very lumpy and timing-related on a quarterly basis.
Gross margin fell Y/Y as expected due to the Blackwell-ramp. That revenue stream is replacing a lot of demand for its more mature Hopper products, which is weighing on margins. The headwind eased Q/Q, which enabled the sequential margin expansion. OpEx rose by 38% Y/Y to support higher growth-related infrastructure costs, as well as more engineering and research headcount.
Let’s take a moment to appreciate what we see below, using AMD for comparison. Nvidia’s 66% operating margin is far higher than AMD’s 54% gross margin. That’s not because AMD is a bad company. Lisa Su is a great CEO, they dominate the CPU landscape and they’re making great progress with GPUs as well. This simply highlights two very powerful things. First, Nvidia’s GPUs are excellent and yield considerable pricing power. Second, their ecosystem is wonderfully sticky and margin-accretive as they layer software and other products on top of their GPU-based offerings. We can bicker all we want to about how quickly AMD is closing the technological and ecosystem gaps. But? If those gaps were truly closed, Nvidia pricing power would wane and the giant margin lead would start to shrink a lot more quickly than it is. Again… AMD is a great company and they are winning their fair share of GPU deals. Nvidia is just a different animal.
e. Balance Sheet
$60.6B in cash & equivalents.
Inventory +161% Y/Y. The sharp inventory growth is because demand signals remain so wonderfully strong for Blackwell and eventually Rubin.
$8.5B total debt.
Share count fell slightly Y/Y.
f. Q4 Guidance & Valuation
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